mat/interface/matrix.c

/*
   This is where the abstract matrix operations are defined
   Portions of this code are under:
   Copyright (c) 2022 Advanced Micro Devices, Inc. All rights reserved.
*/

#include <petsc/private/matimpl.h> /*I "petscmat.h" I*/
#include <petsc/private/isimpl.h>
#include <petsc/private/vecimpl.h>

/* Logging support */
PetscClassId MAT_CLASSID;
PetscClassId MAT_COLORING_CLASSID;
PetscClassId MAT_FDCOLORING_CLASSID;
PetscClassId MAT_TRANSPOSECOLORING_CLASSID;

PetscLogEvent MAT_Mult, MAT_MultAdd, MAT_MultTranspose;
PetscLogEvent MAT_MultTransposeAdd, MAT_Solve, MAT_Solves, MAT_SolveAdd, MAT_SolveTranspose, MAT_MatSolve, MAT_MatTrSolve;
PetscLogEvent MAT_SolveTransposeAdd, MAT_SOR, MAT_ForwardSolve, MAT_BackwardSolve, MAT_LUFactor, MAT_LUFactorSymbolic;
PetscLogEvent MAT_LUFactorNumeric, MAT_CholeskyFactor, MAT_CholeskyFactorSymbolic, MAT_CholeskyFactorNumeric, MAT_ILUFactor;
PetscLogEvent MAT_ILUFactorSymbolic, MAT_ICCFactorSymbolic, MAT_Copy, MAT_Convert, MAT_Scale, MAT_AssemblyBegin;
PetscLogEvent MAT_QRFactorNumeric, MAT_QRFactorSymbolic, MAT_QRFactor;
PetscLogEvent MAT_AssemblyEnd, MAT_SetValues, MAT_GetValues, MAT_GetRow, MAT_GetRowIJ, MAT_CreateSubMats, MAT_GetOrdering, MAT_RedundantMat, MAT_GetSeqNonzeroStructure;
PetscLogEvent MAT_IncreaseOverlap, MAT_Partitioning, MAT_PartitioningND, MAT_Coarsen, MAT_ZeroEntries, MAT_Load, MAT_View, MAT_AXPY, MAT_FDColoringCreate;
PetscLogEvent MAT_FDColoringSetUp, MAT_FDColoringApply, MAT_Transpose, MAT_FDColoringFunction, MAT_CreateSubMat;
PetscLogEvent MAT_TransposeColoringCreate;
PetscLogEvent MAT_MatMult, MAT_MatMultSymbolic, MAT_MatMultNumeric;
PetscLogEvent MAT_PtAP, MAT_PtAPSymbolic, MAT_PtAPNumeric, MAT_RARt, MAT_RARtSymbolic, MAT_RARtNumeric;
PetscLogEvent MAT_MatTransposeMult, MAT_MatTransposeMultSymbolic, MAT_MatTransposeMultNumeric;
PetscLogEvent MAT_TransposeMatMult, MAT_TransposeMatMultSymbolic, MAT_TransposeMatMultNumeric;
PetscLogEvent MAT_MatMatMult, MAT_MatMatMultSymbolic, MAT_MatMatMultNumeric;
PetscLogEvent MAT_MultHermitianTranspose, MAT_MultHermitianTransposeAdd;
PetscLogEvent MAT_Getsymtransreduced, MAT_GetBrowsOfAcols;
PetscLogEvent MAT_GetBrowsOfAocols, MAT_Getlocalmat, MAT_Getlocalmatcondensed, MAT_Seqstompi, MAT_Seqstompinum, MAT_Seqstompisym;
PetscLogEvent MAT_GetMultiProcBlock;
PetscLogEvent MAT_CUSPARSECopyToGPU, MAT_CUSPARSECopyFromGPU, MAT_CUSPARSEGenerateTranspose, MAT_CUSPARSESolveAnalysis;
PetscLogEvent MAT_HIPSPARSECopyToGPU, MAT_HIPSPARSECopyFromGPU, MAT_HIPSPARSEGenerateTranspose, MAT_HIPSPARSESolveAnalysis;
PetscLogEvent MAT_PreallCOO, MAT_SetVCOO;
PetscLogEvent MAT_CreateGraph;
PetscLogEvent MAT_SetValuesBatch;
PetscLogEvent MAT_ViennaCLCopyToGPU;
PetscLogEvent MAT_CUDACopyToGPU, MAT_HIPCopyToGPU;
PetscLogEvent MAT_DenseCopyToGPU, MAT_DenseCopyFromGPU;
PetscLogEvent MAT_Merge, MAT_Residual, MAT_SetRandom;
PetscLogEvent MAT_FactorFactS, MAT_FactorInvS;
PetscLogEvent MATCOLORING_Apply, MATCOLORING_Comm, MATCOLORING_Local, MATCOLORING_ISCreate, MATCOLORING_SetUp, MATCOLORING_Weights;
PetscLogEvent MAT_H2Opus_Build, MAT_H2Opus_Compress, MAT_H2Opus_Orthog, MAT_H2Opus_LR;

const char *const MatFactorTypes[] = {"NONE", "LU", "CHOLESKY", "ILU", "ICC", "ILUDT", "QR", "MatFactorType", "MAT_FACTOR_", NULL};

/*@
  MatSetRandom - Sets all components of a matrix to random numbers.

  Logically Collective

  Input Parameters:
+ x    - the matrix
- rctx - the `PetscRandom` object, formed by `PetscRandomCreate()`, or `NULL` and
          it will create one internally.

  Example:
.vb
     PetscRandomCreate(PETSC_COMM_WORLD,&rctx);
     MatSetRandom(x,rctx);
     PetscRandomDestroy(rctx);
.ve

  Level: intermediate

  Notes:
  For sparse matrices that have been preallocated but not been assembled, it randomly selects appropriate locations,

  for sparse matrices that already have nonzero locations, it fills the locations with random numbers.

  It generates an error if used on unassembled sparse matrices that have not been preallocated.

.seealso: [](ch_matrices), `Mat`, `PetscRandom`, `PetscRandomCreate()`, `MatZeroEntries()`, `MatSetValues()`, `PetscRandomDestroy()`
@*/
PetscErrorCode MatSetRandom(Mat x, PetscRandom rctx)
{
  PetscRandom randObj = NULL;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(x, MAT_CLASSID, 1);
  if (rctx) PetscValidHeaderSpecific(rctx, PETSC_RANDOM_CLASSID, 2);
  PetscValidType(x, 1);
  MatCheckPreallocated(x, 1);

  if (!rctx) {
    MPI_Comm comm;
    PetscCall(PetscObjectGetComm((PetscObject)x, &comm));
    PetscCall(PetscRandomCreate(comm, &randObj));
    PetscCall(PetscRandomSetType(randObj, x->defaultrandtype));
    PetscCall(PetscRandomSetFromOptions(randObj));
    rctx = randObj;
  }
  PetscCall(PetscLogEventBegin(MAT_SetRandom, x, rctx, 0, 0));
  PetscUseTypeMethod(x, setrandom, rctx);
  PetscCall(PetscLogEventEnd(MAT_SetRandom, x, rctx, 0, 0));

  PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
  PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
  PetscCall(PetscRandomDestroy(&randObj));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatCopyHashToXAIJ - copy hash table entries into an XAIJ matrix type

  Logically Collective

  Input Parameter:
. A - A matrix in unassembled, hash table form

  Output Parameter:
. B - The XAIJ matrix. This can either be `A` or some matrix of equivalent size, e.g. obtained from `A` via `MatDuplicate()`

  Example:
.vb
     PetscCall(MatDuplicate(A, MAT_DO_NOT_COPY_VALUES, &B));
     PetscCall(MatCopyHashToXAIJ(A, B));
.ve

  Level: advanced

  Notes:
  If `B` is `A`, then the hash table data structure will be destroyed. `B` is assembled

.seealso: [](ch_matrices), `Mat`, `MAT_USE_HASH_TABLE`
@*/
PetscErrorCode MatCopyHashToXAIJ(Mat A, Mat B)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscUseTypeMethod(A, copyhashtoxaij, B);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in

  Logically Collective

  Input Parameter:
. mat - the factored matrix

  Output Parameters:
+ pivot - the pivot value computed
- row   - the row that the zero pivot occurred. This row value must be interpreted carefully due to row reorderings and which processes
         the share the matrix

  Level: advanced

  Notes:
  This routine does not work for factorizations done with external packages.

  This routine should only be called if `MatGetFactorError()` returns a value of `MAT_FACTOR_NUMERIC_ZEROPIVOT`

  This can also be called on non-factored matrices that come from, for example, matrices used in SOR.

.seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`,
`MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorClearError()`,
`MAT_FACTOR_NUMERIC_ZEROPIVOT`
@*/
PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat, PetscReal *pivot, PetscInt *row)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscAssertPointer(pivot, 2);
  PetscAssertPointer(row, 3);
  *pivot = mat->factorerror_zeropivot_value;
  *row   = mat->factorerror_zeropivot_row;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorGetError - gets the error code from a factorization

  Logically Collective

  Input Parameter:
. mat - the factored matrix

  Output Parameter:
. err - the error code

  Level: advanced

  Note:
  This can also be called on non-factored matrices that come from, for example, matrices used in SOR.

.seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`,
          `MatFactorClearError()`, `MatFactorGetErrorZeroPivot()`, `MatFactorError`
@*/
PetscErrorCode MatFactorGetError(Mat mat, MatFactorError *err)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscAssertPointer(err, 2);
  *err = mat->factorerrortype;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorClearError - clears the error code in a factorization

  Logically Collective

  Input Parameter:
. mat - the factored matrix

  Level: developer

  Note:
  This can also be called on non-factored matrices that come from, for example, matrices used in SOR.

.seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorGetError()`, `MatFactorGetErrorZeroPivot()`,
          `MatGetErrorCode()`, `MatFactorError`
@*/
PetscErrorCode MatFactorClearError(Mat mat)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  mat->factorerrortype             = MAT_FACTOR_NOERROR;
  mat->factorerror_zeropivot_value = 0.0;
  mat->factorerror_zeropivot_row   = 0;
  PetscFunctionReturn(PETSC_SUCCESS);
}

PetscErrorCode MatFindNonzeroRowsOrCols_Basic(Mat mat, PetscBool cols, PetscReal tol, IS *nonzero)
{
  Vec                r, l;
  const PetscScalar *al;
  PetscInt           i, nz, gnz, N, n, st;

  PetscFunctionBegin;
  PetscCall(MatCreateVecs(mat, &r, &l));
  if (!cols) { /* nonzero rows */
    PetscCall(MatGetOwnershipRange(mat, &st, NULL));
    PetscCall(MatGetSize(mat, &N, NULL));
    PetscCall(MatGetLocalSize(mat, &n, NULL));
    PetscCall(VecSet(l, 0.0));
    PetscCall(VecSetRandom(r, NULL));
    PetscCall(MatMult(mat, r, l));
    PetscCall(VecGetArrayRead(l, &al));
  } else { /* nonzero columns */
    PetscCall(MatGetOwnershipRangeColumn(mat, &st, NULL));
    PetscCall(MatGetSize(mat, NULL, &N));
    PetscCall(MatGetLocalSize(mat, NULL, &n));
    PetscCall(VecSet(r, 0.0));
    PetscCall(VecSetRandom(l, NULL));
    PetscCall(MatMultTranspose(mat, l, r));
    PetscCall(VecGetArrayRead(r, &al));
  }
  if (tol <= 0.0) {
    for (i = 0, nz = 0; i < n; i++)
      if (al[i] != 0.0) nz++;
  } else {
    for (i = 0, nz = 0; i < n; i++)
      if (PetscAbsScalar(al[i]) > tol) nz++;
  }
  PetscCallMPI(MPIU_Allreduce(&nz, &gnz, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
  if (gnz != N) {
    PetscInt *nzr;
    PetscCall(PetscMalloc1(nz, &nzr));
    if (nz) {
      if (tol < 0) {
        for (i = 0, nz = 0; i < n; i++)
          if (al[i] != 0.0) nzr[nz++] = i + st;
      } else {
        for (i = 0, nz = 0; i < n; i++)
          if (PetscAbsScalar(al[i]) > tol) nzr[nz++] = i + st;
      }
    }
    PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nz, nzr, PETSC_OWN_POINTER, nonzero));
  } else *nonzero = NULL;
  if (!cols) { /* nonzero rows */
    PetscCall(VecRestoreArrayRead(l, &al));
  } else {
    PetscCall(VecRestoreArrayRead(r, &al));
  }
  PetscCall(VecDestroy(&l));
  PetscCall(VecDestroy(&r));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix

  Input Parameter:
. mat - the matrix

  Output Parameter:
. keptrows - the rows that are not completely zero

  Level: intermediate

  Note:
  `keptrows` is set to `NULL` if all rows are nonzero.

  Developer Note:
  If `keptrows` is not `NULL`, it must be sorted.

.seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
 @*/
PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(keptrows, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
  else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
  if (keptrows && *keptrows) PetscCall(ISSetInfo(*keptrows, IS_SORTED, IS_GLOBAL, PETSC_FALSE, PETSC_TRUE));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFindZeroRows - Locate all rows that are completely zero in the matrix

  Input Parameter:
. mat - the matrix

  Output Parameter:
. zerorows - the rows that are completely zero

  Level: intermediate

  Note:
  `zerorows` is set to `NULL` if no rows are zero.

.seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
 @*/
PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
{
  IS       keptrows;
  PetscInt m, n;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(zerorows, 2);
  PetscCall(MatFindNonzeroRows(mat, &keptrows));
  /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
     In keeping with this convention, we set zerorows to NULL if there are no zero
     rows. */
  if (keptrows == NULL) {
    *zerorows = NULL;
  } else {
    PetscCall(MatGetOwnershipRange(mat, &m, &n));
    PetscCall(ISComplement(keptrows, m, n, zerorows));
    PetscCall(ISDestroy(&keptrows));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling

  Not Collective

  Input Parameter:
. A - the matrix

  Output Parameter:
. a - the diagonal part (which is a SEQUENTIAL matrix)

  Level: advanced

  Notes:
  See `MatCreateAIJ()` for more information on the "diagonal part" of the matrix.

  Use caution, as the reference count on the returned matrix is not incremented and it is used as part of `A`'s normal operation.

.seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
@*/
PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  PetscAssertPointer(a, 2);
  PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
  else {
    PetscMPIInt size;

    PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
    PetscCheck(size == 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for parallel matrix type %s", ((PetscObject)A)->type_name);
    *a = A;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.

  Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. trace - the sum of the diagonal entries

  Level: advanced

.seealso: [](ch_matrices), `Mat`
@*/
PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
{
  Vec diag;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscAssertPointer(trace, 2);
  PetscCall(MatCreateVecs(mat, &diag, NULL));
  PetscCall(MatGetDiagonal(mat, diag));
  PetscCall(VecSum(diag, trace));
  PetscCall(VecDestroy(&diag));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatRealPart - Zeros out the imaginary part of the matrix

  Logically Collective

  Input Parameter:
. mat - the matrix

  Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
@*/
PetscErrorCode MatRealPart(Mat mat)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  PetscUseTypeMethod(mat, realpart);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatGetGhosts - Get the global indices of all ghost nodes defined by the sparse matrix

  Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ nghosts - number of ghosts (for `MATBAIJ` and `MATSBAIJ` matrices there is one ghost for each matrix block)
- ghosts  - the global indices of the ghost points

  Level: advanced

  Note:
  `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()` or `VecCreateGhostBlock()`

.seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
@*/
PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
  else {
    if (nghosts) *nghosts = 0;
    if (ghosts) *ghosts = NULL;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part

  Logically Collective

  Input Parameter:
. mat - the matrix

  Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatRealPart()`
@*/
PetscErrorCode MatImaginaryPart(Mat mat)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  PetscUseTypeMethod(mat, imaginarypart);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for `MATBAIJ` and `MATSBAIJ` matrices) in the nonzero structure

  Not Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ missing - is any diagonal entry missing
- dd      - first diagonal entry that is missing (optional) on this process

  Level: advanced

  Note:
  This does not return diagonal entries that are in the nonzero structure but happen to have a zero numerical value

.seealso: [](ch_matrices), `Mat`
@*/
PetscErrorCode MatMissingDiagonal(Mat mat, PetscBool *missing, PetscInt *dd)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(missing, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix %s", ((PetscObject)mat)->type_name);
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscUseTypeMethod(mat, missingdiagonal, missing, dd);
  PetscFunctionReturn(PETSC_SUCCESS);
}

// PetscClangLinter pragma disable: -fdoc-section-header-unknown
/*@C
  MatGetRow - Gets a row of a matrix.  You MUST call `MatRestoreRow()`
  for each row that you get to ensure that your application does
  not bleed memory.

  Not Collective

  Input Parameters:
+ mat - the matrix
- row - the row to get

  Output Parameters:
+ ncols - if not `NULL`, the number of nonzeros in `row`
. cols  - if not `NULL`, the column numbers
- vals  - if not `NULL`, the numerical values

  Level: advanced

  Notes:
  This routine is provided for people who need to have direct access
  to the structure of a matrix.  We hope that we provide enough
  high-level matrix routines that few users will need it.

  `MatGetRow()` always returns 0-based column indices, regardless of
  whether the internal representation is 0-based (default) or 1-based.

  For better efficiency, set `cols` and/or `vals` to `NULL` if you do
  not wish to extract these quantities.

  The user can only examine the values extracted with `MatGetRow()`;
  the values CANNOT be altered.  To change the matrix entries, one
  must use `MatSetValues()`.

  You can only have one call to `MatGetRow()` outstanding for a particular
  matrix at a time, per processor. `MatGetRow()` can only obtain rows
  associated with the given processor, it cannot get rows from the
  other processors; for that we suggest using `MatCreateSubMatrices()`, then
  `MatGetRow()` on the submatrix. The row index passed to `MatGetRow()`
  is in the global number of rows.

  Use `MatGetRowIJ()` and `MatRestoreRowIJ()` to access all the local indices of the sparse matrix.

  Use `MatSeqAIJGetArray()` and similar functions to access the numerical values for certain matrix types directly.

  Fortran Note:
  The calling sequence is
.vb
   MatGetRow(matrix,row,ncols,cols,values,ierr)
         Mat         matrix (input)
         PetscInt    row    (input)
         PetscInt    ncols  (output)
         PetscInt    cols(maxcols) (output)
         PetscScalar values(maxcols) output
.ve
  where maxcols >= maximum nonzeros in any row of the matrix.

.seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
@*/
PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
{
  PetscInt incols;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  PetscCheck(row >= mat->rmap->rstart && row < mat->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Only for local rows, %" PetscInt_FMT " not in [%" PetscInt_FMT ",%" PetscInt_FMT ")", row, mat->rmap->rstart, mat->rmap->rend);
  PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
  if (ncols) *ncols = incols;
  PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatConjugate - replaces the matrix values with their complex conjugates

  Logically Collective

  Input Parameter:
. mat - the matrix

  Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
@*/
PetscErrorCode MatConjugate(Mat mat)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  if (PetscDefined(USE_COMPLEX) && mat->hermitian != PETSC_BOOL3_TRUE) {
    PetscUseTypeMethod(mat, conjugate);
    PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatRestoreRow - Frees any temporary space allocated by `MatGetRow()`.

  Not Collective

  Input Parameters:
+ mat   - the matrix
. row   - the row to get
. ncols - the number of nonzeros
. cols  - the columns of the nonzeros
- vals  - if nonzero the column values

  Level: advanced

  Notes:
  This routine should be called after you have finished examining the entries.

  This routine zeros out `ncols`, `cols`, and `vals`. This is to prevent accidental
  us of the array after it has been restored. If you pass `NULL`, it will
  not zero the pointers.  Use of `cols` or `vals` after `MatRestoreRow()` is invalid.

  Fortran Note:
  `MatRestoreRow()` MUST be called after `MatGetRow()`
  before another call to `MatGetRow()` can be made.

.seealso: [](ch_matrices), `Mat`, `MatGetRow()`
@*/
PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (ncols) PetscAssertPointer(ncols, 3);
  PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  if (!mat->ops->restorerow) PetscFunctionReturn(PETSC_SUCCESS);
  PetscUseTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
  if (ncols) *ncols = 0;
  if (cols) *cols = NULL;
  if (vals) *vals = NULL;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
  You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.

  Not Collective

  Input Parameter:
. mat - the matrix

  Level: advanced

  Note:
  The flag is to ensure that users are aware that `MatGetRow()` only provides the upper triangular part of the row for the matrices in `MATSBAIJ` format.

.seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
@*/
PetscErrorCode MatGetRowUpperTriangular(Mat mat)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  if (!mat->ops->getrowuppertriangular) PetscFunctionReturn(PETSC_SUCCESS);
  PetscUseTypeMethod(mat, getrowuppertriangular);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.

  Not Collective

  Input Parameter:
. mat - the matrix

  Level: advanced

  Note:
  This routine should be called after you have finished calls to `MatGetRow()` and `MatRestoreRow()`.

.seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
@*/
PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  if (!mat->ops->restorerowuppertriangular) PetscFunctionReturn(PETSC_SUCCESS);
  PetscUseTypeMethod(mat, restorerowuppertriangular);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetOptionsPrefix - Sets the prefix used for searching for all
  `Mat` options in the database.

  Logically Collective

  Input Parameters:
+ A      - the matrix
- prefix - the prefix to prepend to all option names

  Level: advanced

  Notes:
  A hyphen (-) must NOT be given at the beginning of the prefix name.
  The first character of all runtime options is AUTOMATICALLY the hyphen.

  This is NOT used for options for the factorization of the matrix. Normally the
  prefix is automatically passed in from the PC calling the factorization. To set
  it directly use  `MatSetOptionsPrefixFactor()`

.seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
@*/
PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
  for matrices created with `MatGetFactor()`

  Logically Collective

  Input Parameters:
+ A      - the matrix
- prefix - the prefix to prepend to all option names for the factored matrix

  Level: developer

  Notes:
  A hyphen (-) must NOT be given at the beginning of the prefix name.
  The first character of all runtime options is AUTOMATICALLY the hyphen.

  Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
  it directly when not using `KSP`/`PC` use  `MatSetOptionsPrefixFactor()`

.seealso: [](ch_matrices), `Mat`,   [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
@*/
PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  if (prefix) {
    PetscAssertPointer(prefix, 2);
    PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
    if (prefix != A->factorprefix) {
      PetscCall(PetscFree(A->factorprefix));
      PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
    }
  } else PetscCall(PetscFree(A->factorprefix));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
  for matrices created with `MatGetFactor()`

  Logically Collective

  Input Parameters:
+ A      - the matrix
- prefix - the prefix to prepend to all option names for the factored matrix

  Level: developer

  Notes:
  A hyphen (-) must NOT be given at the beginning of the prefix name.
  The first character of all runtime options is AUTOMATICALLY the hyphen.

  Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
  it directly when not using `KSP`/`PC` use  `MatAppendOptionsPrefixFactor()`

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
          `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
          `MatSetOptionsPrefix()`
@*/
PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
{
  size_t len1, len2, new_len;

  PetscFunctionBegin;
  PetscValidHeader(A, 1);
  if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
  if (!A->factorprefix) {
    PetscCall(MatSetOptionsPrefixFactor(A, prefix));
    PetscFunctionReturn(PETSC_SUCCESS);
  }
  PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");

  PetscCall(PetscStrlen(A->factorprefix, &len1));
  PetscCall(PetscStrlen(prefix, &len2));
  new_len = len1 + len2 + 1;
  PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
  PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatAppendOptionsPrefix - Appends to the prefix used for searching for all
  matrix options in the database.

  Logically Collective

  Input Parameters:
+ A      - the matrix
- prefix - the prefix to prepend to all option names

  Level: advanced

  Note:
  A hyphen (-) must NOT be given at the beginning of the prefix name.
  The first character of all runtime options is AUTOMATICALLY the hyphen.

.seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
@*/
PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetOptionsPrefix - Gets the prefix used for searching for all
  matrix options in the database.

  Not Collective

  Input Parameter:
. A - the matrix

  Output Parameter:
. prefix - pointer to the prefix string used

  Level: advanced

  Fortran Note:
  The user should pass in a string `prefix` of
  sufficient length to hold the prefix.

.seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
@*/
PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscAssertPointer(prefix, 2);
  PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`

  Not Collective

  Input Parameter:
. A - the matrix

  Output Parameter:
. state - the object state

  Level: advanced

  Note:
  Object state is an integer which gets increased every time
  the object is changed. By saving and later querying the object state
  one can determine whether information about the object is still current.

  See `MatGetNonzeroState()` to determine if the nonzero structure of the matrix has changed.

.seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
@*/
PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscAssertPointer(state, 2);
  PetscCall(PetscObjectStateGet((PetscObject)A, state));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatResetPreallocation - Reset matrix to use the original nonzero pattern provided by the user.

  Collective

  Input Parameter:
. A - the matrix

  Level: beginner

  Notes:
  The allocated memory will be shrunk after calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.

  Users can reset the preallocation to access the original memory.

  Currently only supported for  `MATAIJ` matrices.

.seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
@*/
PetscErrorCode MatResetPreallocation(Mat A)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
  if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
  PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.

  Collective

  Input Parameter:
. A - the matrix

  Level: intermediate

  Notes:
  The matrix will again delete the hash table data structures after following calls to `MatAssemblyBegin()`/`MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.

  Currently only supported for `MATAIJ` matrices.

.seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
@*/
PetscErrorCode MatResetHash(Mat A)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset to hash state after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
  if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
  PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
  /* These flags are used to determine whether certain setups occur */
  A->was_assembled = PETSC_FALSE;
  A->assembled     = PETSC_FALSE;
  /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
  PetscCall(PetscObjectStateIncrease((PetscObject)A));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetUp - Sets up the internal matrix data structures for later use.

  Collective

  Input Parameter:
. A - the matrix

  Level: intermediate

  Notes:
  If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
  setting values in the matrix.

  This routine is called internally by other matrix functions when needed so rarely needs to be called by users

.seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
@*/
PetscErrorCode MatSetUp(Mat A)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  if (!((PetscObject)A)->type_name) {
    PetscMPIInt size;

    PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
    PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
  }
  if (!A->preallocated) PetscTryTypeMethod(A, setup);
  PetscCall(PetscLayoutSetUp(A->rmap));
  PetscCall(PetscLayoutSetUp(A->cmap));
  A->preallocated = PETSC_TRUE;
  PetscFunctionReturn(PETSC_SUCCESS);
}

#if defined(PETSC_HAVE_SAWS)
  #include <petscviewersaws.h>
#endif

/*
   If threadsafety is on extraneous matrices may be printed

   This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
*/
#if !defined(PETSC_HAVE_THREADSAFETY)
static PetscInt insidematview = 0;
#endif

/*@
  MatViewFromOptions - View properties of the matrix based on options set in the options database

  Collective

  Input Parameters:
+ A    - the matrix
. obj  - optional additional object that provides the options prefix to use
- name - command line option

  Options Database Key:
. -mat_view [viewertype]:... - the viewer and its options

  Level: intermediate

  Note:
.vb
    If no value is provided ascii:stdout is used
       ascii[:[filename][:[format][:append]]]    defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
                                                  for example ascii::ascii_info prints just the information about the object not all details
                                                  unless :append is given filename opens in write mode, overwriting what was already there
       binary[:[filename][:[format][:append]]]   defaults to the file binaryoutput
       draw[:drawtype[:filename]]                for example, draw:tikz, draw:tikz:figure.tex  or draw:x
       socket[:port]                             defaults to the standard output port
       saws[:communicatorname]                    publishes object to the Scientific Application Webserver (SAWs)
.ve

.seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
@*/
PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
#if !defined(PETSC_HAVE_THREADSAFETY)
  if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
#endif
  PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatView - display information about a matrix in a variety ways

  Collective on viewer

  Input Parameters:
+ mat    - the matrix
- viewer - visualization context

  Options Database Keys:
+ -mat_view ::ascii_info           - Prints info on matrix at conclusion of `MatAssemblyEnd()`
. -mat_view ::ascii_info_detail    - Prints more detailed info
. -mat_view                        - Prints matrix in ASCII format
. -mat_view ::ascii_matlab         - Prints matrix in MATLAB format
. -mat_view draw                   - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
. -display <name>                  - Sets display name (default is host)
. -draw_pause <sec>                - Sets number of seconds to pause after display
. -mat_view socket                 - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
. -viewer_socket_machine <machine> - -
. -viewer_socket_port <port>       - -
. -mat_view binary                 - save matrix to file in binary format
- -viewer_binary_filename <name>   - -

  Level: beginner

  Notes:
  The available visualization contexts include
+    `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
.    `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
.    `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
-     `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure

  The user can open alternative visualization contexts with
+    `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
.    `PetscViewerBinaryOpen()` - Outputs matrix in binary to a
  specified file; corresponding input uses `MatLoad()`
.    `PetscViewerDrawOpen()` - Outputs nonzero matrix structure to
  an X window display
-    `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer.
  Currently only the `MATSEQDENSE` and `MATAIJ`
  matrix types support the Socket viewer.

  The user can call `PetscViewerPushFormat()` to specify the output
  format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
  `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`).  Available formats include
+    `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
.    `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
.    `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
.    `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse
  format common among all matrix types
.    `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific
  format (which is in many cases the same as the default)
.    `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix
  size and structure (not the matrix entries)
-    `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about
  the matrix structure (still not vector or matrix entries)

  The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
  the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.

  In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).

  See the manual page for `MatLoad()` for the exact format of the binary file when the binary
  viewer is used.

  See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
  viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.

  One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
  and then use the following mouse functions.
.vb
  left mouse: zoom in
  middle mouse: zoom out
  right mouse: continue with the simulation
.ve

.seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
          `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
@*/
PetscErrorCode MatView(Mat mat, PetscViewer viewer)
{
  PetscInt          rows, cols, rbs, cbs;
  PetscBool         isascii, isstring, issaws;
  PetscViewerFormat format;
  PetscMPIInt       size;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
  PetscValidHeaderSpecific(viewer, PETSC_VIEWER_CLASSID, 2);

  PetscCall(PetscViewerGetFormat(viewer, &format));
  PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
  if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);

#if !defined(PETSC_HAVE_THREADSAFETY)
  insidematview++;
#endif
  PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
  PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
  PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
  PetscCheck((isascii && (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL)) || !mat->factortype, PetscObjectComm((PetscObject)viewer), PETSC_ERR_ARG_WRONGSTATE, "No viewers for factored matrix except ASCII, info, or info_detail");

  PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
  if (isascii) {
    if (!mat->preallocated) {
      PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
#if !defined(PETSC_HAVE_THREADSAFETY)
      insidematview--;
#endif
      PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
      PetscFunctionReturn(PETSC_SUCCESS);
    }
    if (!mat->assembled) {
      PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
#if !defined(PETSC_HAVE_THREADSAFETY)
      insidematview--;
#endif
      PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
      PetscFunctionReturn(PETSC_SUCCESS);
    }
    PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
    if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
      MatNullSpace nullsp, transnullsp;

      PetscCall(PetscViewerASCIIPushTab(viewer));
      PetscCall(MatGetSize(mat, &rows, &cols));
      PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
      if (rbs != 1 || cbs != 1) {
        if (rbs != cbs) PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", rbs=%" PetscInt_FMT ", cbs=%" PetscInt_FMT "%s\n", rows, cols, rbs, cbs, mat->bsizes ? " variable blocks set" : ""));
        else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
      } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
      if (mat->factortype) {
        MatSolverType solver;
        PetscCall(MatFactorGetSolverType(mat, &solver));
        PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
      }
      if (mat->ops->getinfo) {
        MatInfo info;
        PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
        PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
        if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
      }
      PetscCall(MatGetNullSpace(mat, &nullsp));
      PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
      if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached null space\n"));
      if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached transposed null space\n"));
      PetscCall(MatGetNearNullSpace(mat, &nullsp));
      if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached near null space\n"));
      PetscCall(PetscViewerASCIIPushTab(viewer));
      PetscCall(MatProductView(mat, viewer));
      PetscCall(PetscViewerASCIIPopTab(viewer));
      if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
        IS tmp;

        PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
        PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
        PetscCall(PetscViewerASCIIPushTab(viewer));
        PetscCall(ISView(tmp, viewer));
        PetscCall(PetscViewerASCIIPopTab(viewer));
        PetscCall(ISDestroy(&tmp));
      }
    }
  } else if (issaws) {
#if defined(PETSC_HAVE_SAWS)
    PetscMPIInt rank;

    PetscCall(PetscObjectName((PetscObject)mat));
    PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
    if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
#endif
  } else if (isstring) {
    const char *type;
    PetscCall(MatGetType(mat, &type));
    PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
    PetscTryTypeMethod(mat, view, viewer);
  }
  if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
    PetscCall(PetscViewerASCIIPushTab(viewer));
    PetscUseTypeMethod(mat, viewnative, viewer);
    PetscCall(PetscViewerASCIIPopTab(viewer));
  } else if (mat->ops->view) {
    PetscCall(PetscViewerASCIIPushTab(viewer));
    PetscUseTypeMethod(mat, view, viewer);
    PetscCall(PetscViewerASCIIPopTab(viewer));
  }
  if (isascii) {
    PetscCall(PetscViewerGetFormat(viewer, &format));
    if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
  }
  PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
#if !defined(PETSC_HAVE_THREADSAFETY)
  insidematview--;
#endif
  PetscFunctionReturn(PETSC_SUCCESS);
}

#if defined(PETSC_USE_DEBUG)
  #include <../src/sys/totalview/tv_data_display.h>
PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
{
  TV_add_row("Local rows", "int", &mat->rmap->n);
  TV_add_row("Local columns", "int", &mat->cmap->n);
  TV_add_row("Global rows", "int", &mat->rmap->N);
  TV_add_row("Global columns", "int", &mat->cmap->N);
  TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
  return TV_format_OK;
}
#endif

/*@
  MatLoad - Loads a matrix that has been stored in binary/HDF5 format
  with `MatView()`.  The matrix format is determined from the options database.
  Generates a parallel MPI matrix if the communicator has more than one
  processor.  The default matrix type is `MATAIJ`.

  Collective

  Input Parameters:
+ mat    - the newly loaded matrix, this needs to have been created with `MatCreate()`
            or some related function before a call to `MatLoad()`
- viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer

  Options Database Key:
. -matload_block_size <bs> - set block size

  Level: beginner

  Notes:
  If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
  `Mat` before calling this routine if you wish to set it from the options database.

  `MatLoad()` automatically loads into the options database any options
  given in the file filename.info where filename is the name of the file
  that was passed to the `PetscViewerBinaryOpen()`. The options in the info
  file will be ignored if you use the -viewer_binary_skip_info option.

  If the type or size of mat is not set before a call to `MatLoad()`, PETSc
  sets the default matrix type AIJ and sets the local and global sizes.
  If type and/or size is already set, then the same are used.

  In parallel, each processor can load a subset of rows (or the
  entire matrix).  This routine is especially useful when a large
  matrix is stored on disk and only part of it is desired on each
  processor.  For example, a parallel solver may access only some of
  the rows from each processor.  The algorithm used here reads
  relatively small blocks of data rather than reading the entire
  matrix and then subsetting it.

  Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
  Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
  or the sequence like
.vb
    `PetscViewer` v;
    `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
    `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
    `PetscViewerSetFromOptions`(v);
    `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
    `PetscViewerFileSetName`(v,"datafile");
.ve
  The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
$ -viewer_type {binary, hdf5}

  See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
  and src/mat/tutorials/ex10.c with the second approach.

  In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
  is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
  Multiple objects, both matrices and vectors, can be stored within the same file.
  Their `PetscObject` name is ignored; they are loaded in the order of their storage.

  Most users should not need to know the details of the binary storage
  format, since `MatLoad()` and `MatView()` completely hide these details.
  But for anyone who is interested, the standard binary matrix storage
  format is

.vb
    PetscInt    MAT_FILE_CLASSID
    PetscInt    number of rows
    PetscInt    number of columns
    PetscInt    total number of nonzeros
    PetscInt    *number nonzeros in each row
    PetscInt    *column indices of all nonzeros (starting index is zero)
    PetscScalar *values of all nonzeros
.ve
  If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
  stored or loaded (each MPI process part of the matrix must have less than `PETSC_INT_MAX` nonzeros). Since the total nonzero count in this
  case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.

  PETSc automatically does the byte swapping for
  machines that store the bytes reversed. Thus if you write your own binary
  read/write routines you have to swap the bytes; see `PetscBinaryRead()`
  and `PetscBinaryWrite()` to see how this may be done.

  In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
  Each processor's chunk is loaded independently by its owning MPI process.
  Multiple objects, both matrices and vectors, can be stored within the same file.
  They are looked up by their PetscObject name.

  As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
  by default the same structure and naming of the AIJ arrays and column count
  within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
$    save example.mat A b -v7.3
  can be directly read by this routine (see Reference 1 for details).

  Depending on your MATLAB version, this format might be a default,
  otherwise you can set it as default in Preferences.

  Unless -nocompression flag is used to save the file in MATLAB,
  PETSc must be configured with ZLIB package.

  See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c

  This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`

  Corresponding `MatView()` is not yet implemented.

  The loaded matrix is actually a transpose of the original one in MATLAB,
  unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
  With this format, matrix is automatically transposed by PETSc,
  unless the matrix is marked as SPD or symmetric
  (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).

  See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>

.seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
 @*/
PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
{
  PetscBool flg;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(viewer, PETSC_VIEWER_CLASSID, 2);

  if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));

  flg = PETSC_FALSE;
  PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
  if (flg) {
    PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
    PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
  }
  flg = PETSC_FALSE;
  PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
  if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));

  PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
  PetscUseTypeMethod(mat, load, viewer);
  PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
{
  Mat_Redundant *redund = *redundant;

  PetscFunctionBegin;
  if (redund) {
    if (redund->matseq) { /* via MatCreateSubMatrices()  */
      PetscCall(ISDestroy(&redund->isrow));
      PetscCall(ISDestroy(&redund->iscol));
      PetscCall(MatDestroySubMatrices(1, &redund->matseq));
    } else {
      PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
      PetscCall(PetscFree(redund->sbuf_j));
      PetscCall(PetscFree(redund->sbuf_a));
      for (PetscInt i = 0; i < redund->nrecvs; i++) {
        PetscCall(PetscFree(redund->rbuf_j[i]));
        PetscCall(PetscFree(redund->rbuf_a[i]));
      }
      PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
    }

    if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
    PetscCall(PetscFree(redund));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatDestroy - Frees space taken by a matrix.

  Collective

  Input Parameter:
. A - the matrix

  Level: beginner

  Developer Note:
  Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
  `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
  `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
  if changes are needed here.

.seealso: [](ch_matrices), `Mat`, `MatCreate()`
@*/
PetscErrorCode MatDestroy(Mat *A)
{
  PetscFunctionBegin;
  if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
  PetscValidHeaderSpecific(*A, MAT_CLASSID, 1);
  if (--((PetscObject)*A)->refct > 0) {
    *A = NULL;
    PetscFunctionReturn(PETSC_SUCCESS);
  }

  /* if memory was published with SAWs then destroy it */
  PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
  PetscTryTypeMethod(*A, destroy);

  PetscCall(PetscFree((*A)->factorprefix));
  PetscCall(PetscFree((*A)->defaultvectype));
  PetscCall(PetscFree((*A)->defaultrandtype));
  PetscCall(PetscFree((*A)->bsizes));
  PetscCall(PetscFree((*A)->solvertype));
  for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
  if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
  PetscCall(MatDestroy_Redundant(&(*A)->redundant));
  PetscCall(MatProductClear(*A));
  PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
  PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
  PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
  PetscCall(MatDestroy(&(*A)->schur));
  PetscCall(PetscLayoutDestroy(&(*A)->rmap));
  PetscCall(PetscLayoutDestroy(&(*A)->cmap));
  PetscCall(PetscHeaderDestroy(A));
  PetscFunctionReturn(PETSC_SUCCESS);
}

// PetscClangLinter pragma disable: -fdoc-section-header-unknown
/*@
  MatSetValues - Inserts or adds a block of values into a matrix.
  These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
  MUST be called after all calls to `MatSetValues()` have been completed.

  Not Collective

  Input Parameters:
+ mat  - the matrix
. v    - a logically two-dimensional array of values
. m    - the number of rows
. idxm - the global indices of the rows
. n    - the number of columns
. idxn - the global indices of the columns
- addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

  Level: beginner

  Notes:
  By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

  Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
  options cannot be mixed without intervening calls to the assembly
  routines.

  `MatSetValues()` uses 0-based row and column numbers in Fortran
  as well as in C.

  Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
  simply ignored. This allows easily inserting element stiffness matrices
  with homogeneous Dirichlet boundary conditions that you don't want represented
  in the matrix.

  Efficiency Alert:
  The routine `MatSetValuesBlocked()` may offer much better efficiency
  for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

  Fortran Notes:
  If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
.vb
  MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES)
.ve

  If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

  Developer Note:
  This is labeled with C so does not automatically generate Fortran stubs and interfaces
  because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

.seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
          `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
@*/
PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
{
  PetscFunctionBeginHot;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
  PetscAssertPointer(idxm, 3);
  PetscAssertPointer(idxn, 5);
  MatCheckPreallocated(mat, 1);

  if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
  else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");

  if (PetscDefined(USE_DEBUG)) {
    PetscInt i, j;

    PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    if (v) {
      for (i = 0; i < m; i++) {
        for (j = 0; j < n; j++) {
          if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
#if defined(PETSC_USE_COMPLEX)
            SETERRQ(PETSC_COMM_SELF, PETSC_ERR_FP, "Inserting %g+i%g at matrix entry (%" PetscInt_FMT ",%" PetscInt_FMT ")", (double)PetscRealPart(v[i * n + j]), (double)PetscImaginaryPart(v[i * n + j]), idxm[i], idxn[j]);
#else
            SETERRQ(PETSC_COMM_SELF, PETSC_ERR_FP, "Inserting %g at matrix entry (%" PetscInt_FMT ",%" PetscInt_FMT ")", (double)v[i * n + j], idxm[i], idxn[j]);
#endif
        }
      }
    }
    for (i = 0; i < m; i++) PetscCheck(idxm[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot insert in row %" PetscInt_FMT ", maximum is %" PetscInt_FMT, idxm[i], mat->rmap->N - 1);
    for (i = 0; i < n; i++) PetscCheck(idxn[i] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot insert in column %" PetscInt_FMT ", maximum is %" PetscInt_FMT, idxn[i], mat->cmap->N - 1);
  }

  if (mat->assembled) {
    mat->was_assembled = PETSC_TRUE;
    mat->assembled     = PETSC_FALSE;
  }
  PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
  PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

// PetscClangLinter pragma disable: -fdoc-section-header-unknown
/*@
  MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
  These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
  MUST be called after all calls to `MatSetValues()` have been completed.

  Not Collective

  Input Parameters:
+ mat  - the matrix
. v    - a logically two-dimensional array of values
. ism  - the rows to provide
. isn  - the columns to provide
- addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

  Level: beginner

  Notes:
  By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

  Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
  options cannot be mixed without intervening calls to the assembly
  routines.

  `MatSetValues()` uses 0-based row and column numbers in Fortran
  as well as in C.

  Negative indices may be passed in `ism` and `isn`, these rows and columns are
  simply ignored. This allows easily inserting element stiffness matrices
  with homogeneous Dirichlet boundary conditions that you don't want represented
  in the matrix.

  Efficiency Alert:
  The routine `MatSetValuesBlocked()` may offer much better efficiency
  for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

  This is currently not optimized for any particular `ISType`

  Developer Note:
  This is labeled with C so does not automatically generate Fortran stubs and interfaces
  because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

.seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
          `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
@*/
PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
{
  PetscInt        m, n;
  const PetscInt *rows, *cols;

  PetscFunctionBeginHot;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscCall(ISGetIndices(ism, &rows));
  PetscCall(ISGetIndices(isn, &cols));
  PetscCall(ISGetLocalSize(ism, &m));
  PetscCall(ISGetLocalSize(isn, &n));
  PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
  PetscCall(ISRestoreIndices(ism, &rows));
  PetscCall(ISRestoreIndices(isn, &cols));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
  values into a matrix

  Not Collective

  Input Parameters:
+ mat - the matrix
. row - the (block) row to set
- v   - a logically two-dimensional array of values

  Level: intermediate

  Notes:
  The values, `v`, are column-oriented (for the block version) and sorted

  All the nonzero values in `row` must be provided

  The matrix must have previously had its column indices set, likely by having been assembled.

  `row` must belong to this MPI process

.seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
          `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
@*/
PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
{
  PetscInt globalrow;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(v, 3);
  PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
  PetscCall(MatSetValuesRow(mat, globalrow, v));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
  values into a matrix

  Not Collective

  Input Parameters:
+ mat - the matrix
. row - the (block) row to set
- v   - a logically two-dimensional (column major) array of values for  block matrices with blocksize larger than one, otherwise a one dimensional array of values

  Level: advanced

  Notes:
  The values, `v`, are column-oriented for the block version.

  All the nonzeros in `row` must be provided

  THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.

  `row` must belong to this process

.seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
          `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
@*/
PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
{
  PetscFunctionBeginHot;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  MatCheckPreallocated(mat, 1);
  PetscAssertPointer(v, 3);
  PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
  PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  mat->insertmode = INSERT_VALUES;

  if (mat->assembled) {
    mat->was_assembled = PETSC_TRUE;
    mat->assembled     = PETSC_FALSE;
  }
  PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, setvaluesrow, row, v);
  PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

// PetscClangLinter pragma disable: -fdoc-section-header-unknown
/*@
  MatSetValuesStencil - Inserts or adds a block of values into a matrix.
  Using structured grid indexing

  Not Collective

  Input Parameters:
+ mat  - the matrix
. m    - number of rows being entered
. idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
. n    - number of columns being entered
. idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
. v    - a logically two-dimensional array of values
- addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values

  Level: beginner

  Notes:
  By default the values, `v`, are row-oriented.  See `MatSetOption()` for other options.

  Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
  options cannot be mixed without intervening calls to the assembly
  routines.

  The grid coordinates are across the entire grid, not just the local portion

  `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
  as well as in C.

  For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

  In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
  or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

  The columns and rows in the stencil passed in MUST be contained within the
  ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
  if you create a `DMDA` with an overlap of one grid level and on a particular process its first
  local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
  first i index you can use in your column and row indices in `MatSetStencil()` is 5.

  For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
  obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
  etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
  `DM_BOUNDARY_PERIODIC` boundary type.

  For indices that don't mean anything for your case (like the k index when working in 2d) or the c index when you have
  a single value per point) you can skip filling those indices.

  Inspired by the structured grid interface to the HYPRE package
  (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

  Efficiency Alert:
  The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
  for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

  Fortran Note:
  `idxm` and `idxn` should be declared as
$     MatStencil idxm(4,m),idxn(4,n)
  and the values inserted using
.vb
    idxm(MatStencil_i,1) = i
    idxm(MatStencil_j,1) = j
    idxm(MatStencil_k,1) = k
    idxm(MatStencil_c,1) = c
    etc
.ve

.seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
          `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
@*/
PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
{
  PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
  PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
  PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

  PetscFunctionBegin;
  if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(idxm, 3);
  PetscAssertPointer(idxn, 5);

  if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
    jdxm = buf;
    jdxn = buf + m;
  } else {
    PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
    jdxm = bufm;
    jdxn = bufn;
  }
  for (i = 0; i < m; i++) {
    for (j = 0; j < 3 - sdim; j++) dxm++;
    tmp = *dxm++ - starts[0];
    for (j = 0; j < dim - 1; j++) {
      if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
      else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
    }
    if (mat->stencil.noc) dxm++;
    jdxm[i] = tmp;
  }
  for (i = 0; i < n; i++) {
    for (j = 0; j < 3 - sdim; j++) dxn++;
    tmp = *dxn++ - starts[0];
    for (j = 0; j < dim - 1; j++) {
      if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
      else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
    }
    if (mat->stencil.noc) dxn++;
    jdxn[i] = tmp;
  }
  PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
  PetscCall(PetscFree2(bufm, bufn));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
  Using structured grid indexing

  Not Collective

  Input Parameters:
+ mat  - the matrix
. m    - number of rows being entered
. idxm - grid coordinates for matrix rows being entered
. n    - number of columns being entered
. idxn - grid coordinates for matrix columns being entered
. v    - a logically two-dimensional array of values
- addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values

  Level: beginner

  Notes:
  By default the values, `v`, are row-oriented and unsorted.
  See `MatSetOption()` for other options.

  Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
  options cannot be mixed without intervening calls to the assembly
  routines.

  The grid coordinates are across the entire grid, not just the local portion

  `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
  as well as in C.

  For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

  In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
  or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

  The columns and rows in the stencil passed in MUST be contained within the
  ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
  if you create a `DMDA` with an overlap of one grid level and on a particular process its first
  local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
  first i index you can use in your column and row indices in `MatSetStencil()` is 5.

  Negative indices may be passed in idxm and idxn, these rows and columns are
  simply ignored. This allows easily inserting element stiffness matrices
  with homogeneous Dirichlet boundary conditions that you don't want represented
  in the matrix.

  Inspired by the structured grid interface to the HYPRE package
  (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

  Fortran Note:
  `idxm` and `idxn` should be declared as
$     MatStencil idxm(4,m),idxn(4,n)
  and the values inserted using
.vb
    idxm(MatStencil_i,1) = i
    idxm(MatStencil_j,1) = j
    idxm(MatStencil_k,1) = k
   etc
.ve

.seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
          `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
          `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
@*/
PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
{
  PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
  PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
  PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

  PetscFunctionBegin;
  if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(idxm, 3);
  PetscAssertPointer(idxn, 5);
  PetscAssertPointer(v, 6);

  if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
    jdxm = buf;
    jdxn = buf + m;
  } else {
    PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
    jdxm = bufm;
    jdxn = bufn;
  }
  for (i = 0; i < m; i++) {
    for (j = 0; j < 3 - sdim; j++) dxm++;
    tmp = *dxm++ - starts[0];
    for (j = 0; j < sdim - 1; j++) {
      if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
      else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
    }
    dxm++;
    jdxm[i] = tmp;
  }
  for (i = 0; i < n; i++) {
    for (j = 0; j < 3 - sdim; j++) dxn++;
    tmp = *dxn++ - starts[0];
    for (j = 0; j < sdim - 1; j++) {
      if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
      else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
    }
    dxn++;
    jdxn[i] = tmp;
  }
  PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
  PetscCall(PetscFree2(bufm, bufn));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetStencil - Sets the grid information for setting values into a matrix via
  `MatSetValuesStencil()`

  Not Collective

  Input Parameters:
+ mat    - the matrix
. dim    - dimension of the grid 1, 2, or 3
. dims   - number of grid points in x, y, and z direction, including ghost points on your processor
. starts - starting point of ghost nodes on your processor in x, y, and z direction
- dof    - number of degrees of freedom per node

  Level: beginner

  Notes:
  Inspired by the structured grid interface to the HYPRE package
  (www.llnl.gov/CASC/hyper)

  For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
  user.

.seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
          `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
@*/
PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscAssertPointer(dims, 3);
  PetscAssertPointer(starts, 4);

  mat->stencil.dim = dim + (dof > 1);
  for (PetscInt i = 0; i < dim; i++) {
    mat->stencil.dims[i]   = dims[dim - i - 1]; /* copy the values in backwards */
    mat->stencil.starts[i] = starts[dim - i - 1];
  }
  mat->stencil.dims[dim]   = dof;
  mat->stencil.starts[dim] = 0;
  mat->stencil.noc         = (PetscBool)(dof == 1);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

  Not Collective

  Input Parameters:
+ mat  - the matrix
. v    - a logically two-dimensional array of values
. m    - the number of block rows
. idxm - the global block indices
. n    - the number of block columns
. idxn - the global block indices
- addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values

  Level: intermediate

  Notes:
  If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
  MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.

  The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
  NOT the total number of rows/columns; for example, if the block size is 2 and
  you are passing in values for rows 2,3,4,5  then `m` would be 2 (not 4).
  The values in `idxm` would be 1 2; that is the first index for each block divided by
  the block size.

  You must call `MatSetBlockSize()` when constructing this matrix (before
  preallocating it).

  By default the values, `v`, are row-oriented, so the layout of
  `v` is the same as for `MatSetValues()`. See `MatSetOption()` for other options.

  Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
  options cannot be mixed without intervening calls to the assembly
  routines.

  `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
  as well as in C.

  Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
  simply ignored. This allows easily inserting element stiffness matrices
  with homogeneous Dirichlet boundary conditions that you don't want represented
  in the matrix.

  Each time an entry is set within a sparse matrix via `MatSetValues()`,
  internal searching must be done to determine where to place the
  data in the matrix storage space.  By instead inserting blocks of
  entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
  reduced.

  Example:
.vb
   Suppose m=n=2 and block size(bs) = 2 The array is

   1  2  | 3  4
   5  6  | 7  8
   - - - | - - -
   9  10 | 11 12
   13 14 | 15 16

   v[] should be passed in like
   v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]

  If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
   v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
.ve

  Fortran Notes:
  If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
.vb
  MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES)
.ve

  If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

.seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
@*/
PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
{
  PetscFunctionBeginHot;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
  PetscAssertPointer(idxm, 3);
  PetscAssertPointer(idxn, 5);
  MatCheckPreallocated(mat, 1);
  if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
  else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
  if (PetscDefined(USE_DEBUG)) {
    PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
  }
  if (PetscDefined(USE_DEBUG)) {
    PetscInt rbs, cbs, M, N, i;
    PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
    PetscCall(MatGetSize(mat, &M, &N));
    for (i = 0; i < m; i++) PetscCheck(idxm[i] * rbs < M, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row block %" PetscInt_FMT " contains an index %" PetscInt_FMT "*%" PetscInt_FMT " greater than row length %" PetscInt_FMT, i, idxm[i], rbs, M);
    for (i = 0; i < n; i++)
      PetscCheck(idxn[i] * cbs < N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column block %" PetscInt_FMT " contains an index %" PetscInt_FMT "*%" PetscInt_FMT " greater than column length %" PetscInt_FMT, i, idxn[i], cbs, N);
  }
  if (mat->assembled) {
    mat->was_assembled = PETSC_TRUE;
    mat->assembled     = PETSC_FALSE;
  }
  PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
  if (mat->ops->setvaluesblocked) {
    PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
  } else {
    PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
    PetscInt i, j, bs, cbs;

    PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
    if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
      iidxm = buf;
      iidxn = buf + m * bs;
    } else {
      PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
      iidxm = bufr;
      iidxn = bufc;
    }
    for (i = 0; i < m; i++) {
      for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
    }
    if (m != n || bs != cbs || idxm != idxn) {
      for (i = 0; i < n; i++) {
        for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
      }
    } else iidxn = iidxm;
    PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
    PetscCall(PetscFree2(bufr, bufc));
  }
  PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetValues - Gets a block of local values from a matrix.

  Not Collective; can only return values that are owned by the give process

  Input Parameters:
+ mat  - the matrix
. v    - a logically two-dimensional array for storing the values
. m    - the number of rows
. idxm - the  global indices of the rows
. n    - the number of columns
- idxn - the global indices of the columns

  Level: advanced

  Notes:
  The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
  The values, `v`, are then returned in a row-oriented format,
  analogous to that used by default in `MatSetValues()`.

  `MatGetValues()` uses 0-based row and column numbers in
  Fortran as well as in C.

  `MatGetValues()` requires that the matrix has been assembled
  with `MatAssemblyBegin()`/`MatAssemblyEnd()`.  Thus, calls to
  `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
  without intermediate matrix assembly.

  Negative row or column indices will be ignored and those locations in `v` will be
  left unchanged.

  For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
  That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
  from `MatGetOwnershipRange`(mat,&rstart,&rend).

.seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
@*/
PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
  PetscAssertPointer(idxm, 3);
  PetscAssertPointer(idxn, 5);
  PetscAssertPointer(v, 6);
  PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
  PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
  defined previously by `MatSetLocalToGlobalMapping()`

  Not Collective

  Input Parameters:
+ mat  - the matrix
. nrow - number of rows
. irow - the row local indices
. ncol - number of columns
- icol - the column local indices

  Output Parameter:
. y - a logically two-dimensional array of values

  Level: advanced

  Notes:
  If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.

  This routine can only return values that are owned by the requesting MPI process. That is, for standard matrix formats, rows that, in the global numbering,
  are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
  determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
  with `MatSetLocalToGlobalMapping()`.

  Developer Note:
  This is labelled with C so does not automatically generate Fortran stubs and interfaces
  because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

.seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
          `MatSetValuesLocal()`, `MatGetValues()`
@*/
PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
{
  PetscFunctionBeginHot;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  MatCheckPreallocated(mat, 1);
  if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
  PetscAssertPointer(irow, 3);
  PetscAssertPointer(icol, 5);
  if (PetscDefined(USE_DEBUG)) {
    PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
  }
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
  if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
  else {
    PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
    if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
      irowm = buf;
      icolm = buf + nrow;
    } else {
      PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
      irowm = bufr;
      icolm = bufc;
    }
    PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
    PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
    PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
    PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
    PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
    PetscCall(PetscFree2(bufr, bufc));
  }
  PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
  the same size. Currently, this can only be called once and creates the given matrix.

  Not Collective

  Input Parameters:
+ mat  - the matrix
. nb   - the number of blocks
. bs   - the number of rows (and columns) in each block
. rows - a concatenation of the rows for each block
- v    - a concatenation of logically two-dimensional arrays of values

  Level: advanced

  Notes:
  `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values

  In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.

.seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
          `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
@*/
PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(rows, 4);
  PetscAssertPointer(v, 5);
  PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

  PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
  if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
  else {
    for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
  }
  PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
  the routine `MatSetValuesLocal()` to allow users to insert matrix entries
  using a local (per-processor) numbering.

  Not Collective

  Input Parameters:
+ x        - the matrix
. rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
- cmapping - column mapping

  Level: intermediate

  Note:
  If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix

.seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
@*/
PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(x, MAT_CLASSID, 1);
  PetscValidType(x, 1);
  if (rmapping) PetscValidHeaderSpecific(rmapping, IS_LTOGM_CLASSID, 2);
  if (cmapping) PetscValidHeaderSpecific(cmapping, IS_LTOGM_CLASSID, 3);
  if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
  else {
    PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
    PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`

  Not Collective

  Input Parameter:
. A - the matrix

  Output Parameters:
+ rmapping - row mapping
- cmapping - column mapping

  Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
@*/
PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  if (rmapping) {
    PetscAssertPointer(rmapping, 2);
    *rmapping = A->rmap->mapping;
  }
  if (cmapping) {
    PetscAssertPointer(cmapping, 3);
    *cmapping = A->cmap->mapping;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix

  Logically Collective

  Input Parameters:
+ A    - the matrix
. rmap - row layout
- cmap - column layout

  Level: advanced

  Note:
  The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.

.seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
@*/
PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscCall(PetscLayoutReference(rmap, &A->rmap));
  PetscCall(PetscLayoutReference(cmap, &A->cmap));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetLayouts - Gets the `PetscLayout` objects for rows and columns

  Not Collective

  Input Parameter:
. A - the matrix

  Output Parameters:
+ rmap - row layout
- cmap - column layout

  Level: advanced

.seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
@*/
PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  if (rmap) {
    PetscAssertPointer(rmap, 2);
    *rmap = A->rmap;
  }
  if (cmap) {
    PetscAssertPointer(cmap, 3);
    *cmap = A->cmap;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
  using a local numbering of the rows and columns.

  Not Collective

  Input Parameters:
+ mat  - the matrix
. nrow - number of rows
. irow - the row local indices
. ncol - number of columns
. icol - the column local indices
. y    - a logically two-dimensional array of values
- addv - either `INSERT_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

  Level: intermediate

  Notes:
  If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine

  Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
  options cannot be mixed without intervening calls to the assembly
  routines.

  These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
  MUST be called after all calls to `MatSetValuesLocal()` have been completed.

  Fortran Notes:
  If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
.vb
  MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES)
.ve

  If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

  Developer Note:
  This is labeled with C so does not automatically generate Fortran stubs and interfaces
  because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

.seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
          `MatGetValuesLocal()`
@*/
PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
{
  PetscFunctionBeginHot;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  MatCheckPreallocated(mat, 1);
  if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
  PetscAssertPointer(irow, 3);
  PetscAssertPointer(icol, 5);
  if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
  else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
  if (PetscDefined(USE_DEBUG)) {
    PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
  }

  if (mat->assembled) {
    mat->was_assembled = PETSC_TRUE;
    mat->assembled     = PETSC_FALSE;
  }
  PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
  if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
  else {
    PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
    const PetscInt *irowm, *icolm;

    if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
      bufr  = buf;
      bufc  = buf + nrow;
      irowm = bufr;
      icolm = bufc;
    } else {
      PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
      irowm = bufr;
      icolm = bufc;
    }
    if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
    else irowm = irow;
    if (mat->cmap->mapping) {
      if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
        PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
      } else icolm = irowm;
    } else icolm = icol;
    PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
    if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
  }
  PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
  using a local ordering of the nodes a block at a time.

  Not Collective

  Input Parameters:
+ mat  - the matrix
. nrow - number of rows
. irow - the row local indices
. ncol - number of columns
. icol - the column local indices
. y    - a logically two-dimensional array of values
- addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

  Level: intermediate

  Notes:
  If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
  before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the

  Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
  options cannot be mixed without intervening calls to the assembly
  routines.

  These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
  MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.

  Fortran Notes:
  If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
.vb
  MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES)
.ve

  If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

  Developer Note:
  This is labeled with C so does not automatically generate Fortran stubs and interfaces
  because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

.seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
          `MatSetValuesLocal()`, `MatSetValuesBlocked()`
@*/
PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
{
  PetscFunctionBeginHot;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  MatCheckPreallocated(mat, 1);
  if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
  PetscAssertPointer(irow, 3);
  PetscAssertPointer(icol, 5);
  if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
  else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
  if (PetscDefined(USE_DEBUG)) {
    PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
    PetscCheck(mat->ops->setvaluesblockedlocal || mat->ops->setvaluesblocked || mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
  }

  if (mat->assembled) {
    mat->was_assembled = PETSC_TRUE;
    mat->assembled     = PETSC_FALSE;
  }
  if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
    PetscInt irbs, rbs;
    PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
    PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
    PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
  }
  if (PetscUnlikelyDebug(mat->cmap->mapping)) {
    PetscInt icbs, cbs;
    PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
    PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
    PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
  }
  PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
  if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
  else {
    PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
    const PetscInt *irowm, *icolm;

    if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
      bufr  = buf;
      bufc  = buf + nrow;
      irowm = bufr;
      icolm = bufc;
    } else {
      PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
      irowm = bufr;
      icolm = bufc;
    }
    if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
    else irowm = irow;
    if (mat->cmap->mapping) {
      if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
        PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
      } else icolm = irowm;
    } else icolm = icol;
    PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
    if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
  }
  PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal

  Collective

  Input Parameters:
+ mat - the matrix
- x   - the vector to be multiplied

  Output Parameter:
. y - the result

  Level: developer

  Note:
  The vectors `x` and `y` cannot be the same.  I.e., one cannot
  call `MatMultDiagonalBlock`(A,y,y).

.seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
@*/
PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(y, VEC_CLASSID, 3);

  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
  MatCheckPreallocated(mat, 1);

  PetscUseTypeMethod(mat, multdiagonalblock, x, y);
  PetscCall(PetscObjectStateIncrease((PetscObject)y));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMult - Computes the matrix-vector product, $y = Ax$.

  Neighbor-wise Collective

  Input Parameters:
+ mat - the matrix
- x   - the vector to be multiplied

  Output Parameter:
. y - the result

  Level: beginner

  Note:
  The vectors `x` and `y` cannot be the same.  I.e., one cannot
  call `MatMult`(A,y,y).

.seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
@*/
PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 2);
  VecCheckAssembled(x);
  PetscValidHeaderSpecific(y, VEC_CLASSID, 3);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
  PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
  PetscCheck(mat->rmap->N == y->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, y->map->N);
  PetscCheck(mat->cmap->n == x->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->n, x->map->n);
  PetscCheck(mat->rmap->n == y->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, y->map->n);
  PetscCall(VecSetErrorIfLocked(y, 3));
  if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
  MatCheckPreallocated(mat, 1);

  PetscCall(VecLockReadPush(x));
  PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
  PetscUseTypeMethod(mat, mult, x, y);
  PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
  if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
  PetscCall(VecLockReadPop(x));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.

  Neighbor-wise Collective

  Input Parameters:
+ mat - the matrix
- x   - the vector to be multiplied

  Output Parameter:
. y - the result

  Level: beginner

  Notes:
  The vectors `x` and `y` cannot be the same.  I.e., one cannot
  call `MatMultTranspose`(A,y,y).

  For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
  use `MatMultHermitianTranspose()`

.seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
@*/
PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
{
  PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 2);
  VecCheckAssembled(x);
  PetscValidHeaderSpecific(y, VEC_CLASSID, 3);

  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
  PetscCheck(mat->cmap->N == y->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, y->map->N);
  PetscCheck(mat->rmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, x->map->N);
  PetscCheck(mat->cmap->n == y->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->n, y->map->n);
  PetscCheck(mat->rmap->n == x->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, x->map->n);
  if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
  MatCheckPreallocated(mat, 1);

  if (!mat->ops->multtranspose) {
    if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
    PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s does not have a multiply transpose defined or is symmetric and does not have a multiply defined", ((PetscObject)mat)->type_name);
  } else op = mat->ops->multtranspose;
  PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
  PetscCall(VecLockReadPush(x));
  PetscCall((*op)(mat, x, y));
  PetscCall(VecLockReadPop(x));
  PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)y));
  if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.

  Neighbor-wise Collective

  Input Parameters:
+ mat - the matrix
- x   - the vector to be multiplied

  Output Parameter:
. y - the result

  Level: beginner

  Notes:
  The vectors `x` and `y` cannot be the same.  I.e., one cannot
  call `MatMultHermitianTranspose`(A,y,y).

  Also called the conjugate transpose, complex conjugate transpose, or adjoint.

  For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.

.seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
@*/
PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(y, VEC_CLASSID, 3);

  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
  PetscCheck(mat->cmap->N == y->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, y->map->N);
  PetscCheck(mat->rmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, x->map->N);
  PetscCheck(mat->cmap->n == y->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->n, y->map->n);
  PetscCheck(mat->rmap->n == x->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, x->map->n);
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
#if defined(PETSC_USE_COMPLEX)
  if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
    PetscCall(VecLockReadPush(x));
    if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
    else PetscUseTypeMethod(mat, mult, x, y);
    PetscCall(VecLockReadPop(x));
  } else {
    Vec w;
    PetscCall(VecDuplicate(x, &w));
    PetscCall(VecCopy(x, w));
    PetscCall(VecConjugate(w));
    PetscCall(MatMultTranspose(mat, w, y));
    PetscCall(VecDestroy(&w));
    PetscCall(VecConjugate(y));
  }
  PetscCall(PetscObjectStateIncrease((PetscObject)y));
#else
  PetscCall(MatMultTranspose(mat, x, y));
#endif
  PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMultAdd -  Computes $v3 = v2 + A * v1$.

  Neighbor-wise Collective

  Input Parameters:
+ mat - the matrix
. v1  - the vector to be multiplied by `mat`
- v2  - the vector to be added to the result

  Output Parameter:
. v3 - the result

  Level: beginner

  Note:
  The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
  call `MatMultAdd`(A,v1,v2,v1).

.seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
@*/
PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(v1, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(v2, VEC_CLASSID, 3);
  PetscValidHeaderSpecific(v3, VEC_CLASSID, 4);

  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(mat->cmap->N == v1->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v1: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v1->map->N);
  /* PetscCheck(mat->rmap->N == v2->map->N,PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %" PetscInt_FMT " %" PetscInt_FMT,mat->rmap->N,v2->map->N);
     PetscCheck(mat->rmap->N == v3->map->N,PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %" PetscInt_FMT " %" PetscInt_FMT,mat->rmap->N,v3->map->N); */
  PetscCheck(mat->rmap->n == v3->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec v3: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, v3->map->n);
  PetscCheck(mat->rmap->n == v2->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec v2: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, v2->map->n);
  PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
  PetscCall(VecLockReadPush(v1));
  PetscUseTypeMethod(mat, multadd, v1, v2, v3);
  PetscCall(VecLockReadPop(v1));
  PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
  PetscCall(PetscObjectStateIncrease((PetscObject)v3));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.

  Neighbor-wise Collective

  Input Parameters:
+ mat - the matrix
. v1  - the vector to be multiplied by the transpose of the matrix
- v2  - the vector to be added to the result

  Output Parameter:
. v3 - the result

  Level: beginner

  Note:
  The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
  call `MatMultTransposeAdd`(A,v1,v2,v1).

.seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
@*/
PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
{
  PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(v1, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(v2, VEC_CLASSID, 3);
  PetscValidHeaderSpecific(v3, VEC_CLASSID, 4);

  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(mat->rmap->N == v1->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v1: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, v1->map->N);
  PetscCheck(mat->cmap->N == v2->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v2: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v2->map->N);
  PetscCheck(mat->cmap->N == v3->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v3: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v3->map->N);
  PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
  PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
  PetscCall(VecLockReadPush(v1));
  PetscCall((*op)(mat, v1, v2, v3));
  PetscCall(VecLockReadPop(v1));
  PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
  PetscCall(PetscObjectStateIncrease((PetscObject)v3));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.

  Neighbor-wise Collective

  Input Parameters:
+ mat - the matrix
. v1  - the vector to be multiplied by the Hermitian transpose
- v2  - the vector to be added to the result

  Output Parameter:
. v3 - the result

  Level: beginner

  Note:
  The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
  call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).

.seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
@*/
PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(v1, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(v2, VEC_CLASSID, 3);
  PetscValidHeaderSpecific(v3, VEC_CLASSID, 4);

  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
  PetscCheck(mat->rmap->N == v1->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v1: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, v1->map->N);
  PetscCheck(mat->cmap->N == v2->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v2: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v2->map->N);
  PetscCheck(mat->cmap->N == v3->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v3: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v3->map->N);
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
  PetscCall(VecLockReadPush(v1));
  if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
  else {
    Vec w, z;
    PetscCall(VecDuplicate(v1, &w));
    PetscCall(VecCopy(v1, w));
    PetscCall(VecConjugate(w));
    PetscCall(VecDuplicate(v3, &z));
    PetscCall(MatMultTranspose(mat, w, z));
    PetscCall(VecDestroy(&w));
    PetscCall(VecConjugate(z));
    if (v2 != v3) {
      PetscCall(VecWAXPY(v3, 1.0, v2, z));
    } else {
      PetscCall(VecAXPY(v3, 1.0, z));
    }
    PetscCall(VecDestroy(&z));
  }
  PetscCall(VecLockReadPop(v1));
  PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
  PetscCall(PetscObjectStateIncrease((PetscObject)v3));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetFactorType - gets the type of factorization a matrix is

  Not Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. t - the type, one of `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`, `MAT_FACTOR_ICC,MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`

  Level: intermediate

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
          `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
@*/
PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(t, 2);
  *t = mat->factortype;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetFactorType - sets the type of factorization a matrix is

  Logically Collective

  Input Parameters:
+ mat - the matrix
- t   - the type, one of `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`, `MAT_FACTOR_ICC,MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`

  Level: intermediate

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
          `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
@*/
PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  mat->factortype = t;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetInfo - Returns information about matrix storage (number of
  nonzeros, memory, etc.).

  Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag

  Input Parameters:
+ mat  - the matrix
- flag - flag indicating the type of parameters to be returned (`MAT_LOCAL` - local matrix, `MAT_GLOBAL_MAX` - maximum over all processors, `MAT_GLOBAL_SUM` - sum over all processors)

  Output Parameter:
. info - matrix information context

  Options Database Key:
. -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`

  Level: intermediate

  Notes:
  The `MatInfo` context contains a variety of matrix data, including
  number of nonzeros allocated and used, number of mallocs during
  matrix assembly, etc.  Additional information for factored matrices
  is provided (such as the fill ratio, number of mallocs during
  factorization, etc.).

  Example:
  See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
  data within the `MatInfo` context.  For example,
.vb
      MatInfo info;
      Mat     A;
      double  mal, nz_a, nz_u;

      MatGetInfo(A, MAT_LOCAL, &info);
      mal  = info.mallocs;
      nz_a = info.nz_allocated;
.ve

  Fortran Note:
  Declare info as a `MatInfo` array of dimension `MAT_INFO_SIZE`, and then extract the parameters
  of interest.  See the file ${PETSC_DIR}/include/petsc/finclude/petscmat.h
  a complete list of parameter names.
.vb
      MatInfo info(MAT_INFO_SIZE)
      double  precision mal, nz_a
      Mat     A
      integer ierr

      call MatGetInfo(A, MAT_LOCAL, info, ierr)
      mal = info(MAT_INFO_MALLOCS)
      nz_a = info(MAT_INFO_NZ_ALLOCATED)
.ve

.seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
@*/
PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(info, 3);
  MatCheckPreallocated(mat, 1);
  PetscUseTypeMethod(mat, getinfo, flag, info);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*
   This is used by external packages where it is not easy to get the info from the actual
   matrix factorization.
*/
PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
{
  PetscFunctionBegin;
  PetscCall(PetscMemzero(info, sizeof(MatInfo)));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatLUFactor - Performs in-place LU factorization of matrix.

  Collective

  Input Parameters:
+ mat  - the matrix
. row  - row permutation
. col  - column permutation
- info - options for factorization, includes
.vb
          fill - expected fill as ratio of original fill.
          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
                   Run with the option -info to determine an optimal value to use
.ve

  Level: developer

  Notes:
  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  This changes the state of the matrix to a factored matrix; it cannot be used
  for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

  This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
  when not using `KSP`.

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to `MatFactorInfo`]

.seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
          `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
@*/
PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
{
  MatFactorInfo tinfo;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (row) PetscValidHeaderSpecific(row, IS_CLASSID, 2);
  if (col) PetscValidHeaderSpecific(col, IS_CLASSID, 3);
  if (info) PetscAssertPointer(info, 4);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  if (!info) {
    PetscCall(MatFactorInfoInitialize(&tinfo));
    info = &tinfo;
  }

  PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
  PetscUseTypeMethod(mat, lufactor, row, col, info);
  PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatILUFactor - Performs in-place ILU factorization of matrix.

  Collective

  Input Parameters:
+ mat  - the matrix
. row  - row permutation
. col  - column permutation
- info - structure containing
.vb
      levels - number of levels of fill.
      expected fill - as ratio of original fill.
      1 or 0 - indicating force fill on diagonal (improves robustness for matrices
                missing diagonal entries)
.ve

  Level: developer

  Notes:
  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  Probably really in-place only when level of fill is zero, otherwise allocates
  new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
  when not using `KSP`.

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to MatFactorInfo]

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
@*/
PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (row) PetscValidHeaderSpecific(row, IS_CLASSID, 2);
  if (col) PetscValidHeaderSpecific(col, IS_CLASSID, 3);
  PetscAssertPointer(info, 4);
  PetscValidType(mat, 1);
  PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
  PetscUseTypeMethod(mat, ilufactor, row, col, info);
  PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
  Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.

  Collective

  Input Parameters:
+ fact - the factor matrix obtained with `MatGetFactor()`
. mat  - the matrix
. row  - the row permutation
. col  - the column permutation
- info - options for factorization, includes
.vb
          fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
.ve

  Level: developer

  Notes:
  See [Matrix Factorization](sec_matfactor) for additional information about factorizations

  Most users should employ the simplified `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to `MatFactorInfo`]

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
@*/
PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
{
  MatFactorInfo tinfo;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(fact, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 2);
  if (row) PetscValidHeaderSpecific(row, IS_CLASSID, 3);
  if (col) PetscValidHeaderSpecific(col, IS_CLASSID, 4);
  if (info) PetscAssertPointer(info, 5);
  PetscValidType(fact, 1);
  PetscValidType(mat, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 2);
  if (!info) {
    PetscCall(MatFactorInfoInitialize(&tinfo));
    info = &tinfo;
  }

  if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
  PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
  if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)fact));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
  Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.

  Collective

  Input Parameters:
+ fact - the factor matrix obtained with `MatGetFactor()`
. mat  - the matrix
- info - options for factorization

  Level: developer

  Notes:
  See `MatLUFactor()` for in-place factorization.  See
  `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.

  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to `MatFactorInfo`]

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
@*/
PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
{
  MatFactorInfo tinfo;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(fact, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 2);
  PetscValidType(fact, 1);
  PetscValidType(mat, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(mat->rmap->N == (fact)->rmap->N && mat->cmap->N == (fact)->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Mat fact: global dimensions are different %" PetscInt_FMT " should = %" PetscInt_FMT " %" PetscInt_FMT " should = %" PetscInt_FMT,
             mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

  MatCheckPreallocated(mat, 2);
  if (!info) {
    PetscCall(MatFactorInfoInitialize(&tinfo));
    info = &tinfo;
  }

  if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
  else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
  PetscUseTypeMethod(fact, lufactornumeric, mat, info);
  if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
  else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
  PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
  PetscCall(PetscObjectStateIncrease((PetscObject)fact));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatCholeskyFactor - Performs in-place Cholesky factorization of a
  symmetric matrix.

  Collective

  Input Parameters:
+ mat  - the matrix
. perm - row and column permutations
- info - expected fill as ratio of original fill

  Level: developer

  Notes:
  See `MatLUFactor()` for the nonsymmetric case.  See also `MatGetFactor()`,
  `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.

  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to `MatFactorInfo`]

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
          `MatGetOrdering()`
@*/
PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
{
  MatFactorInfo tinfo;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (perm) PetscValidHeaderSpecific(perm, IS_CLASSID, 2);
  if (info) PetscAssertPointer(info, 3);
  PetscValidType(mat, 1);
  PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  if (!info) {
    PetscCall(MatFactorInfoInitialize(&tinfo));
    info = &tinfo;
  }

  PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
  PetscUseTypeMethod(mat, choleskyfactor, perm, info);
  PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
  of a symmetric matrix.

  Collective

  Input Parameters:
+ fact - the factor matrix obtained with `MatGetFactor()`
. mat  - the matrix
. perm - row and column permutations
- info - options for factorization, includes
.vb
          fill - expected fill as ratio of original fill.
          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
                   Run with the option -info to determine an optimal value to use
.ve

  Level: developer

  Notes:
  See `MatLUFactorSymbolic()` for the nonsymmetric case.  See also
  `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.

  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to `MatFactorInfo`]

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
          `MatGetOrdering()`
@*/
PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
{
  MatFactorInfo tinfo;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(fact, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 2);
  if (perm) PetscValidHeaderSpecific(perm, IS_CLASSID, 3);
  if (info) PetscAssertPointer(info, 4);
  PetscValidType(fact, 1);
  PetscValidType(mat, 2);
  PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 2);
  if (!info) {
    PetscCall(MatFactorInfoInitialize(&tinfo));
    info = &tinfo;
  }

  if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
  PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
  if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)fact));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
  of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
  `MatCholeskyFactorSymbolic()`.

  Collective

  Input Parameters:
+ fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
. mat  - the initial matrix that is to be factored
- info - options for factorization

  Level: developer

  Note:
  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to `MatFactorInfo`]

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
@*/
PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
{
  MatFactorInfo tinfo;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(fact, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 2);
  PetscValidType(fact, 1);
  PetscValidType(mat, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(mat->rmap->N == (fact)->rmap->N && mat->cmap->N == (fact)->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Mat fact: global dim %" PetscInt_FMT " should = %" PetscInt_FMT " %" PetscInt_FMT " should = %" PetscInt_FMT,
             mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
  MatCheckPreallocated(mat, 2);
  if (!info) {
    PetscCall(MatFactorInfoInitialize(&tinfo));
    info = &tinfo;
  }

  if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
  else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
  PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
  if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
  else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
  PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
  PetscCall(PetscObjectStateIncrease((PetscObject)fact));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatQRFactor - Performs in-place QR factorization of matrix.

  Collective

  Input Parameters:
+ mat  - the matrix
. col  - column permutation
- info - options for factorization, includes
.vb
          fill - expected fill as ratio of original fill.
          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
                   Run with the option -info to determine an optimal value to use
.ve

  Level: developer

  Notes:
  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  This changes the state of the matrix to a factored matrix; it cannot be used
  for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to MatFactorInfo]

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
          `MatSetUnfactored()`
@*/
PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (col) PetscValidHeaderSpecific(col, IS_CLASSID, 2);
  if (info) PetscAssertPointer(info, 3);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
  PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
  PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
  Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.

  Collective

  Input Parameters:
+ fact - the factor matrix obtained with `MatGetFactor()`
. mat  - the matrix
. col  - column permutation
- info - options for factorization, includes
.vb
          fill - expected fill as ratio of original fill.
          dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
                   Run with the option -info to determine an optimal value to use
.ve

  Level: developer

  Note:
  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to `MatFactorInfo`]

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
@*/
PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
{
  MatFactorInfo tinfo;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(fact, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 2);
  if (col) PetscValidHeaderSpecific(col, IS_CLASSID, 3);
  if (info) PetscAssertPointer(info, 4);
  PetscValidType(fact, 1);
  PetscValidType(mat, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 2);
  if (!info) {
    PetscCall(MatFactorInfoInitialize(&tinfo));
    info = &tinfo;
  }

  if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
  PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
  if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)fact));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
  Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.

  Collective

  Input Parameters:
+ fact - the factor matrix obtained with `MatGetFactor()`
. mat  - the matrix
- info - options for factorization

  Level: developer

  Notes:
  See `MatQRFactor()` for in-place factorization.

  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to `MatFactorInfo`]

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
@*/
PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
{
  MatFactorInfo tinfo;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(fact, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 2);
  PetscValidType(fact, 1);
  PetscValidType(mat, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(mat->rmap->N == fact->rmap->N && mat->cmap->N == fact->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Mat fact: global dimensions are different %" PetscInt_FMT " should = %" PetscInt_FMT " %" PetscInt_FMT " should = %" PetscInt_FMT,
             mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

  MatCheckPreallocated(mat, 2);
  if (!info) {
    PetscCall(MatFactorInfoInitialize(&tinfo));
    info = &tinfo;
  }

  if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
  else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
  PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
  if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
  else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
  PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
  PetscCall(PetscObjectStateIncrease((PetscObject)fact));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSolve - Solves $A x = b$, given a factored matrix.

  Neighbor-wise Collective

  Input Parameters:
+ mat - the factored matrix
- b   - the right-hand-side vector

  Output Parameter:
. x - the result vector

  Level: developer

  Notes:
  The vectors `b` and `x` cannot be the same.  I.e., one cannot
  call `MatSolve`(A,x,x).

  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
@*/
PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(b, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 3);
  PetscCheckSameComm(mat, 1, b, 2);
  PetscCheckSameComm(mat, 1, x, 3);
  PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
  PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
  PetscCheck(mat->rmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, b->map->N);
  PetscCheck(mat->rmap->n == b->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, b->map->n);
  if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
  PetscCall(VecFlag(x, mat->factorerrortype));
  if (mat->factorerrortype) {
    PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
  } else PetscUseTypeMethod(mat, solve, b, x);
  PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)x));
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
{
  Vec      b, x;
  PetscInt N, i;
  PetscErrorCode (*f)(Mat, Vec, Vec);
  PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;

  PetscFunctionBegin;
  if (A->factorerrortype) {
    PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
    PetscCall(MatSetInf(X));
    PetscFunctionReturn(PETSC_SUCCESS);
  }
  f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
  PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
  PetscCall(MatBoundToCPU(A, &Abound));
  if (!Abound) {
    PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
    PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
  }
#if PetscDefined(HAVE_CUDA)
  if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
  if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
#elif PetscDefined(HAVE_HIP)
  if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
  if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
#endif
  PetscCall(MatGetSize(B, NULL, &N));
  for (i = 0; i < N; i++) {
    PetscCall(MatDenseGetColumnVecRead(B, i, &b));
    PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
    PetscCall((*f)(A, b, x));
    PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
    PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
  }
  if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
  if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMatSolve - Solves $A X = B$, given a factored matrix.

  Neighbor-wise Collective

  Input Parameters:
+ A - the factored matrix
- B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)

  Output Parameter:
. X - the result matrix (dense matrix)

  Level: developer

  Note:
  If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
  otherwise, `B` and `X` cannot be the same.

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
@*/
PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  PetscValidHeaderSpecific(B, MAT_CLASSID, 2);
  PetscValidHeaderSpecific(X, MAT_CLASSID, 3);
  PetscCheckSameComm(A, 1, B, 2);
  PetscCheckSameComm(A, 1, X, 3);
  PetscCheck(A->cmap->N == X->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat X: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->cmap->N, X->rmap->N);
  PetscCheck(A->rmap->N == B->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat B: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->N, B->rmap->N);
  PetscCheck(X->cmap->N == B->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Solution matrix must have same number of columns as rhs matrix");
  if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
  MatCheckPreallocated(A, 1);

  PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
  if (!A->ops->matsolve) {
    PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
    PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
  } else PetscUseTypeMethod(A, matsolve, B, X);
  PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)X));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.

  Neighbor-wise Collective

  Input Parameters:
+ A - the factored matrix
- B - the right-hand-side matrix  (`MATDENSE` matrix)

  Output Parameter:
. X - the result matrix (dense matrix)

  Level: developer

  Note:
  The matrices `B` and `X` cannot be the same.  I.e., one cannot
  call `MatMatSolveTranspose`(A,X,X).

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
@*/
PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  PetscValidHeaderSpecific(B, MAT_CLASSID, 2);
  PetscValidHeaderSpecific(X, MAT_CLASSID, 3);
  PetscCheckSameComm(A, 1, B, 2);
  PetscCheckSameComm(A, 1, X, 3);
  PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
  PetscCheck(A->cmap->N == X->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat X: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->cmap->N, X->rmap->N);
  PetscCheck(A->rmap->N == B->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat B: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->N, B->rmap->N);
  PetscCheck(A->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat A,Mat B: local dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->n, B->rmap->n);
  PetscCheck(X->cmap->N >= B->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Solution matrix must have same number of columns as rhs matrix");
  if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
  MatCheckPreallocated(A, 1);

  PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
  if (!A->ops->matsolvetranspose) {
    PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
    PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
  } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
  PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)X));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.

  Neighbor-wise Collective

  Input Parameters:
+ A  - the factored matrix
- Bt - the transpose of right-hand-side matrix as a `MATDENSE`

  Output Parameter:
. X - the result matrix (dense matrix)

  Level: developer

  Note:
  For MUMPS, it only supports centralized sparse compressed column format on the host processor for right-hand side matrix. User must create `Bt` in sparse compressed row
  format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
@*/
PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  PetscValidHeaderSpecific(Bt, MAT_CLASSID, 2);
  PetscValidHeaderSpecific(X, MAT_CLASSID, 3);
  PetscCheckSameComm(A, 1, Bt, 2);
  PetscCheckSameComm(A, 1, X, 3);

  PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
  PetscCheck(A->cmap->N == X->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat X: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->cmap->N, X->rmap->N);
  PetscCheck(A->rmap->N == Bt->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat Bt: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->N, Bt->cmap->N);
  PetscCheck(X->cmap->N >= Bt->rmap->N, PetscObjectComm((PetscObject)X), PETSC_ERR_ARG_SIZ, "Solution matrix must have same number of columns as row number of the rhs matrix");
  if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
  PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
  MatCheckPreallocated(A, 1);

  PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
  PetscUseTypeMethod(A, mattransposesolve, Bt, X);
  PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)X));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
  $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

  Neighbor-wise Collective

  Input Parameters:
+ mat - the factored matrix
- b   - the right-hand-side vector

  Output Parameter:
. x - the result vector

  Level: developer

  Notes:
  `MatSolve()` should be used for most applications, as it performs
  a forward solve followed by a backward solve.

  The vectors `b` and `x` cannot be the same,  i.e., one cannot
  call `MatForwardSolve`(A,x,x).

  For matrix in `MATSEQBAIJ` format with block size larger than 1,
  the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
  `MatForwardSolve()` solves $U^T*D y = b$, and
  `MatBackwardSolve()` solves $U x = y$.
  Thus they do not provide a symmetric preconditioner.

.seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
@*/
PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(b, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 3);
  PetscCheckSameComm(mat, 1, b, 2);
  PetscCheckSameComm(mat, 1, x, 3);
  PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
  PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
  PetscCheck(mat->rmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, b->map->N);
  PetscCheck(mat->rmap->n == b->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, b->map->n);
  if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
  PetscUseTypeMethod(mat, forwardsolve, b, x);
  PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)x));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
  $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

  Neighbor-wise Collective

  Input Parameters:
+ mat - the factored matrix
- b   - the right-hand-side vector

  Output Parameter:
. x - the result vector

  Level: developer

  Notes:
  `MatSolve()` should be used for most applications, as it performs
  a forward solve followed by a backward solve.

  The vectors `b` and `x` cannot be the same.  I.e., one cannot
  call `MatBackwardSolve`(A,x,x).

  For matrix in `MATSEQBAIJ` format with block size larger than 1,
  the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
  `MatForwardSolve()` solves $U^T*D y = b$, and
  `MatBackwardSolve()` solves $U x = y$.
  Thus they do not provide a symmetric preconditioner.

.seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
@*/
PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(b, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 3);
  PetscCheckSameComm(mat, 1, b, 2);
  PetscCheckSameComm(mat, 1, x, 3);
  PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
  PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
  PetscCheck(mat->rmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, b->map->N);
  PetscCheck(mat->rmap->n == b->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, b->map->n);
  if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
  PetscUseTypeMethod(mat, backwardsolve, b, x);
  PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)x));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.

  Neighbor-wise Collective

  Input Parameters:
+ mat - the factored matrix
. b   - the right-hand-side vector
- y   - the vector to be added to

  Output Parameter:
. x - the result vector

  Level: developer

  Note:
  The vectors `b` and `x` cannot be the same.  I.e., one cannot
  call `MatSolveAdd`(A,x,y,x).

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
@*/
PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
{
  PetscScalar one = 1.0;
  Vec         tmp;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(y, VEC_CLASSID, 3);
  PetscValidHeaderSpecific(b, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 4);
  PetscCheckSameComm(mat, 1, b, 2);
  PetscCheckSameComm(mat, 1, y, 3);
  PetscCheckSameComm(mat, 1, x, 4);
  PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
  PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
  PetscCheck(mat->rmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, b->map->N);
  PetscCheck(mat->rmap->N == y->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, y->map->N);
  PetscCheck(mat->rmap->n == b->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, b->map->n);
  PetscCheck(x->map->n == y->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Vec x,Vec y: local dim %" PetscInt_FMT " %" PetscInt_FMT, x->map->n, y->map->n);
  if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
  PetscCall(VecFlag(x, mat->factorerrortype));
  if (mat->factorerrortype) {
    PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
  } else if (mat->ops->solveadd) {
    PetscUseTypeMethod(mat, solveadd, b, y, x);
  } else {
    /* do the solve then the add manually */
    if (x != y) {
      PetscCall(MatSolve(mat, b, x));
      PetscCall(VecAXPY(x, one, y));
    } else {
      PetscCall(VecDuplicate(x, &tmp));
      PetscCall(VecCopy(x, tmp));
      PetscCall(MatSolve(mat, b, x));
      PetscCall(VecAXPY(x, one, tmp));
      PetscCall(VecDestroy(&tmp));
    }
  }
  PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
  PetscCall(PetscObjectStateIncrease((PetscObject)x));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.

  Neighbor-wise Collective

  Input Parameters:
+ mat - the factored matrix
- b   - the right-hand-side vector

  Output Parameter:
. x - the result vector

  Level: developer

  Notes:
  The vectors `b` and `x` cannot be the same.  I.e., one cannot
  call `MatSolveTranspose`(A,x,x).

  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
@*/
PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
{
  PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(b, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 3);
  PetscCheckSameComm(mat, 1, b, 2);
  PetscCheckSameComm(mat, 1, x, 3);
  PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
  PetscCheck(mat->rmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, x->map->N);
  PetscCheck(mat->cmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, b->map->N);
  if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
  MatCheckPreallocated(mat, 1);
  PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
  PetscCall(VecFlag(x, mat->factorerrortype));
  if (mat->factorerrortype) {
    PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
  } else {
    PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
    PetscCall((*f)(mat, b, x));
  }
  PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)x));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
  factored matrix.

  Neighbor-wise Collective

  Input Parameters:
+ mat - the factored matrix
. b   - the right-hand-side vector
- y   - the vector to be added to

  Output Parameter:
. x - the result vector

  Level: developer

  Note:
  The vectors `b` and `x` cannot be the same.  I.e., one cannot
  call `MatSolveTransposeAdd`(A,x,y,x).

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
@*/
PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
{
  PetscScalar one = 1.0;
  Vec         tmp;
  PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(y, VEC_CLASSID, 3);
  PetscValidHeaderSpecific(b, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 4);
  PetscCheckSameComm(mat, 1, b, 2);
  PetscCheckSameComm(mat, 1, y, 3);
  PetscCheckSameComm(mat, 1, x, 4);
  PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
  PetscCheck(mat->rmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, x->map->N);
  PetscCheck(mat->cmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, b->map->N);
  PetscCheck(mat->cmap->N == y->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, y->map->N);
  PetscCheck(x->map->n == y->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Vec x,Vec y: local dim %" PetscInt_FMT " %" PetscInt_FMT, x->map->n, y->map->n);
  if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
  PetscCall(VecFlag(x, mat->factorerrortype));
  if (mat->factorerrortype) {
    PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
  } else if (f) {
    PetscCall((*f)(mat, b, y, x));
  } else {
    /* do the solve then the add manually */
    if (x != y) {
      PetscCall(MatSolveTranspose(mat, b, x));
      PetscCall(VecAXPY(x, one, y));
    } else {
      PetscCall(VecDuplicate(x, &tmp));
      PetscCall(VecCopy(x, tmp));
      PetscCall(MatSolveTranspose(mat, b, x));
      PetscCall(VecAXPY(x, one, tmp));
      PetscCall(VecDestroy(&tmp));
    }
  }
  PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
  PetscCall(PetscObjectStateIncrease((PetscObject)x));
  PetscFunctionReturn(PETSC_SUCCESS);
}

// PetscClangLinter pragma disable: -fdoc-section-header-unknown
/*@
  MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

  Neighbor-wise Collective

  Input Parameters:
+ mat   - the matrix
. b     - the right-hand side
. omega - the relaxation factor
. flag  - flag indicating the type of SOR (see below)
. shift - diagonal shift
. its   - the number of iterations
- lits  - the number of local iterations

  Output Parameter:
. x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)

  SOR Flags:
+     `SOR_FORWARD_SWEEP` - forward SOR
.     `SOR_BACKWARD_SWEEP` - backward SOR
.     `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
.     `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
.     `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
.     `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
.     `SOR_EISENSTAT` - SOR with Eisenstat trick
.     `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
  upper/lower triangular part of matrix to
  vector (with omega)
-     `SOR_ZERO_INITIAL_GUESS` - zero initial guess

  Level: developer

  Notes:
  `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
  `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
  on each processor.

  Application programmers will not generally use `MatSOR()` directly,
  but instead will employ the `KSP`/`PC` interface.

  For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  Vectors `x` and `b` CANNOT be the same

  The flags are implemented as bitwise inclusive or operations.
  For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
  to specify a zero initial guess for SSOR.

  Developer Note:
  We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes

.seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
@*/
PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(b, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 8);
  PetscCheckSameComm(mat, 1, b, 2);
  PetscCheckSameComm(mat, 1, x, 8);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
  PetscCheck(mat->rmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, b->map->N);
  PetscCheck(mat->rmap->n == b->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, b->map->n);
  PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
  PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
  PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");

  MatCheckPreallocated(mat, 1);
  PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
  PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
  PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)x));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*
      Default matrix copy routine.
*/
PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
{
  PetscInt           i, rstart = 0, rend = 0, nz;
  const PetscInt    *cwork;
  const PetscScalar *vwork;

  PetscFunctionBegin;
  if (B->assembled) PetscCall(MatZeroEntries(B));
  if (str == SAME_NONZERO_PATTERN) {
    PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
    for (i = rstart; i < rend; i++) {
      PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
      PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
      PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
    }
  } else {
    PetscCall(MatAYPX(B, 0.0, A, str));
  }
  PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
  PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatCopy - Copies a matrix to another matrix.

  Collective

  Input Parameters:
+ A   - the matrix
- str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`

  Output Parameter:
. B - where the copy is put

  Level: intermediate

  Notes:
  If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.

  `MatCopy()` copies the matrix entries of a matrix to another existing
  matrix (after first zeroing the second matrix).  A related routine is
  `MatConvert()`, which first creates a new matrix and then copies the data.

.seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
@*/
PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
{
  PetscInt i;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(B, MAT_CLASSID, 2);
  PetscValidType(A, 1);
  PetscValidType(B, 2);
  PetscCheckSameComm(A, 1, B, 2);
  MatCheckPreallocated(B, 2);
  PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(A->rmap->N == B->rmap->N && A->cmap->N == B->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat B: global dim (%" PetscInt_FMT ",%" PetscInt_FMT ") (%" PetscInt_FMT ",%" PetscInt_FMT ")", A->rmap->N, B->rmap->N,
             A->cmap->N, B->cmap->N);
  MatCheckPreallocated(A, 1);
  if (A == B) PetscFunctionReturn(PETSC_SUCCESS);

  PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
  if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
  else PetscCall(MatCopy_Basic(A, B, str));

  B->stencil.dim = A->stencil.dim;
  B->stencil.noc = A->stencil.noc;
  for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
    B->stencil.dims[i]   = A->stencil.dims[i];
    B->stencil.starts[i] = A->stencil.starts[i];
  }

  PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)B));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatConvert - Converts a matrix to another matrix, either of the same
  or different type.

  Collective

  Input Parameters:
+ mat     - the matrix
. newtype - new matrix type.  Use `MATSAME` to create a new matrix of the
            same type as the original matrix.
- reuse   - denotes if the destination matrix is to be created or reused.
            Use `MAT_INPLACE_MATRIX` for inplace conversion (that is when you want the input mat to be changed to contain the matrix in the new format), otherwise use
            `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX` (can only be used after the first call was made with `MAT_INITIAL_MATRIX`, causes the matrix space in M to be reused).

  Output Parameter:
. M - pointer to place new matrix

  Level: intermediate

  Notes:
  `MatConvert()` first creates a new matrix and then copies the data from
  the first matrix.  A related routine is `MatCopy()`, which copies the matrix
  entries of one matrix to another already existing matrix context.

  Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
  the MPI communicator of the generated matrix is always the same as the communicator
  of the input matrix.

.seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
@*/
PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
{
  PetscBool  sametype, issame, flg;
  PetscBool3 issymmetric, ishermitian;
  char       convname[256], mtype[256];
  Mat        B;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(M, 4);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
  if (flg) newtype = mtype;

  PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
  PetscCall(PetscStrcmp(newtype, "same", &issame));
  PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
  if (reuse == MAT_REUSE_MATRIX) {
    PetscValidHeaderSpecific(*M, MAT_CLASSID, 4);
    PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
  }

  if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
    PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
    PetscFunctionReturn(PETSC_SUCCESS);
  }

  /* Cache Mat options because some converters use MatHeaderReplace  */
  issymmetric = mat->symmetric;
  ishermitian = mat->hermitian;

  if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
    PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
    PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
  } else {
    PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
    const char *prefix[3]                                 = {"seq", "mpi", ""};
    PetscInt    i;
    /*
       Order of precedence:
       0) See if newtype is a superclass of the current matrix.
       1) See if a specialized converter is known to the current matrix.
       2) See if a specialized converter is known to the desired matrix class.
       3) See if a good general converter is registered for the desired class
          (as of 6/27/03 only MATMPIADJ falls into this category).
       4) See if a good general converter is known for the current matrix.
       5) Use a really basic converter.
    */

    /* 0) See if newtype is a superclass of the current matrix.
          i.e mat is mpiaij and newtype is aij */
    for (i = 0; i < 2; i++) {
      PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
      PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
      PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
      PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
      if (flg) {
        if (reuse == MAT_INPLACE_MATRIX) {
          PetscCall(PetscInfo(mat, "Early return\n"));
          PetscFunctionReturn(PETSC_SUCCESS);
        } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
          PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
          PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
          PetscFunctionReturn(PETSC_SUCCESS);
        } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
          PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
          PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
          PetscFunctionReturn(PETSC_SUCCESS);
        }
      }
    }
    /* 1) See if a specialized converter is known to the current matrix and the desired class */
    for (i = 0; i < 3; i++) {
      PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
      PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
      PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
      PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
      PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
      PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
      PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
      PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
      if (conv) goto foundconv;
    }

    /* 2)  See if a specialized converter is known to the desired matrix class. */
    PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
    PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
    PetscCall(MatSetType(B, newtype));
    for (i = 0; i < 3; i++) {
      PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
      PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
      PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
      PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
      PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
      PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
      PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
      PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
      if (conv) {
        PetscCall(MatDestroy(&B));
        goto foundconv;
      }
    }

    /* 3) See if a good general converter is registered for the desired class */
    conv = B->ops->convertfrom;
    PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
    PetscCall(MatDestroy(&B));
    if (conv) goto foundconv;

    /* 4) See if a good general converter is known for the current matrix */
    if (mat->ops->convert) conv = mat->ops->convert;
    PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
    if (conv) goto foundconv;

    /* 5) Use a really basic converter. */
    PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
    conv = MatConvert_Basic;

  foundconv:
    PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
    PetscCall((*conv)(mat, newtype, reuse, M));
    if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
      /* the block sizes must be same if the mappings are copied over */
      (*M)->rmap->bs = mat->rmap->bs;
      (*M)->cmap->bs = mat->cmap->bs;
      PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
      PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
      (*M)->rmap->mapping = mat->rmap->mapping;
      (*M)->cmap->mapping = mat->cmap->mapping;
    }
    (*M)->stencil.dim = mat->stencil.dim;
    (*M)->stencil.noc = mat->stencil.noc;
    for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
      (*M)->stencil.dims[i]   = mat->stencil.dims[i];
      (*M)->stencil.starts[i] = mat->stencil.starts[i];
    }
    PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
  }
  PetscCall(PetscObjectStateIncrease((PetscObject)*M));

  /* Copy Mat options */
  if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
  else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
  if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
  else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorGetSolverType - Returns name of the package providing the factorization routines

  Not Collective

  Input Parameter:
. mat - the matrix, must be a factored matrix

  Output Parameter:
. type - the string name of the package (do not free this string)

  Level: intermediate

  Fortran Note:
  Pass in an empty string that is long enough and the package name will be copied into it.

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
@*/
PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
{
  PetscErrorCode (*conv)(Mat, MatSolverType *);

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(type, 2);
  PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
  PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
  if (conv) PetscCall((*conv)(mat, type));
  else *type = MATSOLVERPETSC;
  PetscFunctionReturn(PETSC_SUCCESS);
}

typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
struct _MatSolverTypeForSpecifcType {
  MatType mtype;
  /* no entry for MAT_FACTOR_NONE */
  PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
  MatSolverTypeForSpecifcType next;
};

typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
struct _MatSolverTypeHolder {
  char                       *name;
  MatSolverTypeForSpecifcType handlers;
  MatSolverTypeHolder         next;
};

static MatSolverTypeHolder MatSolverTypeHolders = NULL;

/*@C
  MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type

  Logically Collective, No Fortran Support

  Input Parameters:
+ package      - name of the package, for example petsc or superlu
. mtype        - the matrix type that works with this package
. ftype        - the type of factorization supported by the package
- createfactor - routine that will create the factored matrix ready to be used

  Level: developer

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
  `MatGetFactor()`
@*/
PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
{
  MatSolverTypeHolder         next = MatSolverTypeHolders, prev = NULL;
  PetscBool                   flg;
  MatSolverTypeForSpecifcType inext, iprev = NULL;

  PetscFunctionBegin;
  PetscCall(MatInitializePackage());
  if (!next) {
    PetscCall(PetscNew(&MatSolverTypeHolders));
    PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
    PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
    PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
    MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
    PetscFunctionReturn(PETSC_SUCCESS);
  }
  while (next) {
    PetscCall(PetscStrcasecmp(package, next->name, &flg));
    if (flg) {
      PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
      inext = next->handlers;
      while (inext) {
        PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
        if (flg) {
          inext->createfactor[(int)ftype - 1] = createfactor;
          PetscFunctionReturn(PETSC_SUCCESS);
        }
        iprev = inext;
        inext = inext->next;
      }
      PetscCall(PetscNew(&iprev->next));
      PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
      iprev->next->createfactor[(int)ftype - 1] = createfactor;
      PetscFunctionReturn(PETSC_SUCCESS);
    }
    prev = next;
    next = next->next;
  }
  PetscCall(PetscNew(&prev->next));
  PetscCall(PetscStrallocpy(package, &prev->next->name));
  PetscCall(PetscNew(&prev->next->handlers));
  PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
  prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatSolverTypeGet - Gets the function that creates the factor matrix if it exist

  Input Parameters:
+ type  - name of the package, for example petsc or superlu, if this is 'NULL', then the first result that satisfies the other criteria is returned
. ftype - the type of factorization supported by the type
- mtype - the matrix type that works with this type

  Output Parameters:
+ foundtype    - `PETSC_TRUE` if the type was registered
. foundmtype   - `PETSC_TRUE` if the type supports the requested mtype
- createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found

  Calling sequence of `createfactor`:
+ A     - the matrix providing the factor matrix
. ftype - the `MatFactorType` of the factor requested
- B     - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`

  Level: developer

  Note:
  When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
  Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
  For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.

.seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
          `MatInitializePackage()`
@*/
PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
{
  MatSolverTypeHolder         next = MatSolverTypeHolders;
  PetscBool                   flg;
  MatSolverTypeForSpecifcType inext;

  PetscFunctionBegin;
  if (foundtype) *foundtype = PETSC_FALSE;
  if (foundmtype) *foundmtype = PETSC_FALSE;
  if (createfactor) *createfactor = NULL;

  if (type) {
    while (next) {
      PetscCall(PetscStrcasecmp(type, next->name, &flg));
      if (flg) {
        if (foundtype) *foundtype = PETSC_TRUE;
        inext = next->handlers;
        while (inext) {
          PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
          if (flg) {
            if (foundmtype) *foundmtype = PETSC_TRUE;
            if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
            PetscFunctionReturn(PETSC_SUCCESS);
          }
          inext = inext->next;
        }
      }
      next = next->next;
    }
  } else {
    while (next) {
      inext = next->handlers;
      while (inext) {
        PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
        if (flg && inext->createfactor[(int)ftype - 1]) {
          if (foundtype) *foundtype = PETSC_TRUE;
          if (foundmtype) *foundmtype = PETSC_TRUE;
          if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
          PetscFunctionReturn(PETSC_SUCCESS);
        }
        inext = inext->next;
      }
      next = next->next;
    }
    /* try with base classes inext->mtype */
    next = MatSolverTypeHolders;
    while (next) {
      inext = next->handlers;
      while (inext) {
        PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
        if (flg && inext->createfactor[(int)ftype - 1]) {
          if (foundtype) *foundtype = PETSC_TRUE;
          if (foundmtype) *foundmtype = PETSC_TRUE;
          if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
          PetscFunctionReturn(PETSC_SUCCESS);
        }
        inext = inext->next;
      }
      next = next->next;
    }
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

PetscErrorCode MatSolverTypeDestroy(void)
{
  MatSolverTypeHolder         next = MatSolverTypeHolders, prev;
  MatSolverTypeForSpecifcType inext, iprev;

  PetscFunctionBegin;
  while (next) {
    PetscCall(PetscFree(next->name));
    inext = next->handlers;
    while (inext) {
      PetscCall(PetscFree(inext->mtype));
      iprev = inext;
      inext = inext->next;
      PetscCall(PetscFree(iprev));
    }
    prev = next;
    next = next->next;
    PetscCall(PetscFree(prev));
  }
  MatSolverTypeHolders = NULL;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`

  Logically Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. flg - `PETSC_TRUE` if uses the ordering

  Level: developer

  Note:
  Most internal PETSc factorizations use the ordering passed to the factorization routine but external
  packages do not, thus we want to skip generating the ordering when it is not needed or used.

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
@*/
PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
{
  PetscFunctionBegin;
  *flg = mat->canuseordering;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object

  Logically Collective

  Input Parameters:
+ mat   - the matrix obtained with `MatGetFactor()`
- ftype - the factorization type to be used

  Output Parameter:
. otype - the preferred ordering type

  Level: developer

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
@*/
PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
{
  PetscFunctionBegin;
  *otype = mat->preferredordering[ftype];
  PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()

  Collective

  Input Parameters:
+ mat   - the matrix
. type  - name of solver type, for example, superlu, petsc (to use PETSc's solver if it is available), if this is 'NULL', then the first result that satisfies
          the other criteria is returned
- ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

  Output Parameter:
. f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.

  Options Database Keys:
+ -pc_factor_mat_solver_type <type>             - choose the type at run time. When using `KSP` solvers
- -mat_factor_bind_factorization <host, device> - Where to do matrix factorization? Default is device (might consume more device memory.
                                                  One can choose host to save device memory). Currently only supported with `MATSEQAIJCUSPARSE` matrices.

  Level: intermediate

  Notes:
  The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
  types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.

  Users usually access the factorization solvers via `KSP`

  Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
  such as pastix, superlu, mumps etc. PETSc must have been ./configure to use the external solver, using the option --download-package or --with-package-dir

  When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
  Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
  For example if one configuration had --download-mumps while a different one had --download-superlu_dist.

  Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
  where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
  call `MatSetOptionsPrefixFactor()` on the originating matrix or  `MatSetOptionsPrefix()` on the resulting factor matrix.

  Developer Note:
  This should actually be called `MatCreateFactor()` since it creates a new factor object

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
          `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
          `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
@*/
PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
{
  PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
  PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);

  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscCall(MatIsShell(mat, &shell));
  if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
  if (hasop) {
    PetscUseTypeMethod(mat, getfactor, type, ftype, f);
    PetscFunctionReturn(PETSC_SUCCESS);
  }

  PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
  if (!foundtype) {
    if (type) {
      SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "Could not locate solver type %s for factorization type %s and matrix type %s. Perhaps you must ./configure with --download-%s", type, MatFactorTypes[ftype],
              ((PetscObject)mat)->type_name, type);
    } else {
      SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "Could not locate a solver type for factorization type %s and matrix type %s.", MatFactorTypes[ftype], ((PetscObject)mat)->type_name);
    }
  }
  PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
  PetscCheck(conv, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support factorization type %s for matrix type %s", type, MatFactorTypes[ftype], ((PetscObject)mat)->type_name);

  PetscCall((*conv)(mat, ftype, f));
  if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type

  Not Collective

  Input Parameters:
+ mat   - the matrix
. type  - name of solver type, for example, superlu, petsc (to use PETSc's default)
- ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

  Output Parameter:
. flg - PETSC_TRUE if the factorization is available

  Level: intermediate

  Notes:
  Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
  such as pastix, superlu, mumps etc.

  PETSc must have been ./configure to use the external solver, using the option --download-package

  Developer Note:
  This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
          `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
@*/
PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
{
  PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscAssertPointer(flg, 4);

  *flg = PETSC_FALSE;
  if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);

  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
  *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatDuplicate - Duplicates a matrix including the non-zero structure.

  Collective

  Input Parameters:
+ mat - the matrix
- op  - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
        See the manual page for `MatDuplicateOption()` for an explanation of these options.

  Output Parameter:
. M - pointer to place new matrix

  Level: intermediate

  Notes:
  You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.

  If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.

  May be called with an unassembled input `Mat` if `MAT_DO_NOT_COPY_VALUES` is used, in which case the output `Mat` is unassembled as well.

  When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
  is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
  User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.

.seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
@*/
PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
{
  Mat         B;
  VecType     vtype;
  PetscInt    i;
  PetscObject dm, container_h, container_d;
  void (*viewf)(void);

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(M, 3);
  PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  *M = NULL;
  PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, duplicate, op, M);
  PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
  B = *M;

  PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
  if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
  PetscCall(MatGetVecType(mat, &vtype));
  PetscCall(MatSetVecType(B, vtype));

  B->stencil.dim = mat->stencil.dim;
  B->stencil.noc = mat->stencil.noc;
  for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
    B->stencil.dims[i]   = mat->stencil.dims[i];
    B->stencil.starts[i] = mat->stencil.starts[i];
  }

  B->nooffproczerorows = mat->nooffproczerorows;
  B->nooffprocentries  = mat->nooffprocentries;

  PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
  if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
  PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
  if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
  PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
  if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
  if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
  PetscCall(PetscObjectStateIncrease((PetscObject)B));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`

  Logically Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. v - the diagonal of the matrix

  Level: intermediate

  Note:
  If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
  of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
  is larger than `ndiag`, the values of the remaining entries are unspecified.

  Currently only correct in parallel for square matrices.

.seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
@*/
PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(v, VEC_CLASSID, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  MatCheckPreallocated(mat, 1);
  if (PetscDefined(USE_DEBUG)) {
    PetscInt nv, row, col, ndiag;

    PetscCall(VecGetLocalSize(v, &nv));
    PetscCall(MatGetLocalSize(mat, &row, &col));
    ndiag = PetscMin(row, col);
    PetscCheck(nv >= ndiag, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming Mat and Vec. Vec local size %" PetscInt_FMT " < Mat local diagonal length %" PetscInt_FMT, nv, ndiag);
  }

  PetscUseTypeMethod(mat, getdiagonal, v);
  PetscCall(PetscObjectStateIncrease((PetscObject)v));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetRowMin - Gets the minimum value (of the real part) of each
  row of the matrix

  Logically Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ v   - the vector for storing the maximums
- idx - the indices of the column found for each row (optional, pass `NULL` if not needed)

  Level: intermediate

  Note:
  The result of this call are the same as if one converted the matrix to dense format
  and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

  This code is only implemented for a couple of matrix formats.

.seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
          `MatGetRowMax()`
@*/
PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(v, VEC_CLASSID, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

  if (!mat->cmap->N) {
    PetscCall(VecSet(v, PETSC_MAX_REAL));
    if (idx) {
      PetscInt i, m = mat->rmap->n;
      for (i = 0; i < m; i++) idx[i] = -1;
    }
  } else {
    MatCheckPreallocated(mat, 1);
  }
  PetscUseTypeMethod(mat, getrowmin, v, idx);
  PetscCall(PetscObjectStateIncrease((PetscObject)v));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
  row of the matrix

  Logically Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ v   - the vector for storing the minimums
- idx - the indices of the column found for each row (or `NULL` if not needed)

  Level: intermediate

  Notes:
  if a row is completely empty or has only 0.0 values, then the `idx` value for that
  row is 0 (the first column).

  This code is only implemented for a couple of matrix formats.

.seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
@*/
PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(v, VEC_CLASSID, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

  if (!mat->cmap->N) {
    PetscCall(VecSet(v, 0.0));
    if (idx) {
      PetscInt i, m = mat->rmap->n;
      for (i = 0; i < m; i++) idx[i] = -1;
    }
  } else {
    MatCheckPreallocated(mat, 1);
    if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
    PetscUseTypeMethod(mat, getrowminabs, v, idx);
  }
  PetscCall(PetscObjectStateIncrease((PetscObject)v));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetRowMax - Gets the maximum value (of the real part) of each
  row of the matrix

  Logically Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ v   - the vector for storing the maximums
- idx - the indices of the column found for each row (optional, otherwise pass `NULL`)

  Level: intermediate

  Notes:
  The result of this call are the same as if one converted the matrix to dense format
  and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

  This code is only implemented for a couple of matrix formats.

.seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
@*/
PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(v, VEC_CLASSID, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

  if (!mat->cmap->N) {
    PetscCall(VecSet(v, PETSC_MIN_REAL));
    if (idx) {
      PetscInt i, m = mat->rmap->n;
      for (i = 0; i < m; i++) idx[i] = -1;
    }
  } else {
    MatCheckPreallocated(mat, 1);
    PetscUseTypeMethod(mat, getrowmax, v, idx);
  }
  PetscCall(PetscObjectStateIncrease((PetscObject)v));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
  row of the matrix

  Logically Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ v   - the vector for storing the maximums
- idx - the indices of the column found for each row (or `NULL` if not needed)

  Level: intermediate

  Notes:
  if a row is completely empty or has only 0.0 values, then the `idx` value for that
  row is 0 (the first column).

  This code is only implemented for a couple of matrix formats.

.seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
@*/
PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(v, VEC_CLASSID, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

  if (!mat->cmap->N) {
    PetscCall(VecSet(v, 0.0));
    if (idx) {
      PetscInt i, m = mat->rmap->n;
      for (i = 0; i < m; i++) idx[i] = -1;
    }
  } else {
    MatCheckPreallocated(mat, 1);
    if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
    PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
  }
  PetscCall(PetscObjectStateIncrease((PetscObject)v));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix

  Logically Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. v - the vector for storing the sum

  Level: intermediate

  This code is only implemented for a couple of matrix formats.

.seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
@*/
PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(v, VEC_CLASSID, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

  if (!mat->cmap->N) {
    PetscCall(VecSet(v, 0.0));
  } else {
    MatCheckPreallocated(mat, 1);
    PetscUseTypeMethod(mat, getrowsumabs, v);
  }
  PetscCall(PetscObjectStateIncrease((PetscObject)v));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetRowSum - Gets the sum of each row of the matrix

  Logically or Neighborhood Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. v - the vector for storing the sum of rows

  Level: intermediate

  Note:
  This code is slow since it is not currently specialized for different formats

.seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
@*/
PetscErrorCode MatGetRowSum(Mat mat, Vec v)
{
  Vec ones;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(v, VEC_CLASSID, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  MatCheckPreallocated(mat, 1);
  PetscCall(MatCreateVecs(mat, &ones, NULL));
  PetscCall(VecSet(ones, 1.));
  PetscCall(MatMult(mat, ones, v));
  PetscCall(VecDestroy(&ones));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
  when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)

  Collective

  Input Parameter:
. mat - the matrix to provide the transpose

  Output Parameter:
. B - the matrix to contain the transpose; it MUST have the nonzero structure of the transpose of A or the code will crash or generate incorrect results

  Level: advanced

  Note:
  Normally the use of `MatTranspose`(A, `MAT_REUSE_MATRIX`, &B) requires that `B` was obtained with a call to `MatTranspose`(A, `MAT_INITIAL_MATRIX`, &B). This
  routine allows bypassing that call.

.seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
@*/
PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
{
  MatParentState *rb = NULL;

  PetscFunctionBegin;
  PetscCall(PetscNew(&rb));
  rb->id    = ((PetscObject)mat)->id;
  rb->state = 0;
  PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
  PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatTranspose - Computes an in-place or out-of-place transpose of a matrix.

  Collective

  Input Parameters:
+ mat   - the matrix to transpose
- reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

  Output Parameter:
. B - the transpose

  Level: intermediate

  Notes:
  If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`

  `MAT_REUSE_MATRIX` uses the `B` matrix obtained from a previous call to this function with `MAT_INITIAL_MATRIX`. If you already have a matrix to contain the
  transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.

  If the nonzero structure of mat changed from the previous call to this function with the same matrices an error will be generated for some matrix types.

  Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose, but don't need the storage to be changed.

  If mat is unchanged from the last call this function returns immediately without recomputing the result

  If you only need the symbolic transpose, and not the numerical values, use `MatTransposeSymbolic()`

.seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
          `MatTransposeSymbolic()`, `MatCreateTranspose()`
@*/
PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
{
  PetscContainer  rB = NULL;
  MatParentState *rb = NULL;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
  PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
  MatCheckPreallocated(mat, 1);
  if (reuse == MAT_REUSE_MATRIX) {
    PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
    PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
    PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
    PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
    if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
  }

  PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
  if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
    PetscUseTypeMethod(mat, transpose, reuse, B);
    PetscCall(PetscObjectStateIncrease((PetscObject)*B));
  }
  PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));

  if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
  if (reuse != MAT_INPLACE_MATRIX) {
    PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
    PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
    rb->state        = ((PetscObject)mat)->state;
    rb->nonzerostate = mat->nonzerostate;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.

  Collective

  Input Parameter:
. A - the matrix to transpose

  Output Parameter:
. B - the transpose. This is a complete matrix but the numerical portion is invalid. One can call `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B) to compute the
      numerical portion.

  Level: intermediate

  Note:
  This is not supported for many matrix types, use `MatTranspose()` in those cases

.seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
@*/
PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
  PetscUseTypeMethod(A, transposesymbolic, B);
  PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));

  PetscCall(MatTransposeSetPrecursor(A, *B));
  PetscFunctionReturn(PETSC_SUCCESS);
}

PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
{
  PetscContainer  rB;
  MatParentState *rb;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
  PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
  PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
  PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
  PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatIsTranspose - Test whether a matrix is another one's transpose,
  or its own, in which case it tests symmetry.

  Collective

  Input Parameters:
+ A   - the matrix to test
. B   - the matrix to test against, this can equal the first parameter
- tol - tolerance, differences between entries smaller than this are counted as zero

  Output Parameter:
. flg - the result

  Level: intermediate

  Notes:
  The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
  test involves parallel copies of the block off-diagonal parts of the matrix.

.seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
@*/
PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
{
  PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(B, MAT_CLASSID, 2);
  PetscAssertPointer(flg, 4);
  PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
  PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
  *flg = PETSC_FALSE;
  if (f && g) {
    PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
    PetscCall((*f)(A, B, tol, flg));
  } else {
    MatType mattype;

    PetscCall(MatGetType(f ? B : A, &mattype));
    SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.

  Collective

  Input Parameters:
+ mat   - the matrix to transpose and complex conjugate
- reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

  Output Parameter:
. B - the Hermitian transpose

  Level: intermediate

.seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
@*/
PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
{
  PetscFunctionBegin;
  PetscCall(MatTranspose(mat, reuse, B));
#if defined(PETSC_USE_COMPLEX)
  PetscCall(MatConjugate(*B));
#endif
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

  Collective

  Input Parameters:
+ A   - the matrix to test
. B   - the matrix to test against, this can equal the first parameter
- tol - tolerance, differences between entries smaller than this are counted as zero

  Output Parameter:
. flg - the result

  Level: intermediate

  Notes:
  Only available for `MATAIJ` matrices.

  The sequential algorithm
  has a running time of the order of the number of nonzeros; the parallel
  test involves parallel copies of the block off-diagonal parts of the matrix.

.seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
@*/
PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
{
  PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(B, MAT_CLASSID, 2);
  PetscAssertPointer(flg, 4);
  PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
  PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
  if (f && g) {
    PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
    PetscCall((*f)(A, B, tol, flg));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatPermute - Creates a new matrix with rows and columns permuted from the
  original.

  Collective

  Input Parameters:
+ mat - the matrix to permute
. row - row permutation, each processor supplies only the permutation for its rows
- col - column permutation, each processor supplies only the permutation for its columns

  Output Parameter:
. B - the permuted matrix

  Level: advanced

  Note:
  The index sets map from row/col of permuted matrix to row/col of original matrix.
  The index sets should be on the same communicator as mat and have the same local sizes.

  Developer Note:
  If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
  exploit the fact that row and col are permutations, consider implementing the
  more general `MatCreateSubMatrix()` instead.

.seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
@*/
PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(row, IS_CLASSID, 2);
  PetscValidHeaderSpecific(col, IS_CLASSID, 3);
  PetscAssertPointer(B, 4);
  PetscCheckSameComm(mat, 1, row, 2);
  if (row != col) PetscCheckSameComm(row, 2, col, 3);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
  MatCheckPreallocated(mat, 1);

  if (mat->ops->permute) {
    PetscUseTypeMethod(mat, permute, row, col, B);
    PetscCall(PetscObjectStateIncrease((PetscObject)*B));
  } else {
    PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatEqual - Compares two matrices.

  Collective

  Input Parameters:
+ A - the first matrix
- B - the second matrix

  Output Parameter:
. flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.

  Level: intermediate

.seealso: [](ch_matrices), `Mat`
@*/
PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(B, MAT_CLASSID, 2);
  PetscValidType(A, 1);
  PetscValidType(B, 2);
  PetscAssertPointer(flg, 3);
  PetscCheckSameComm(A, 1, B, 2);
  MatCheckPreallocated(A, 1);
  MatCheckPreallocated(B, 2);
  PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(A->rmap->N == B->rmap->N && A->cmap->N == B->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat B: global dim %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->N, B->rmap->N, A->cmap->N,
             B->cmap->N);
  if (A->ops->equal && A->ops->equal == B->ops->equal) {
    PetscUseTypeMethod(A, equal, B, flg);
  } else {
    PetscCall(MatMultEqual(A, B, 10, flg));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatDiagonalScale - Scales a matrix on the left and right by diagonal
  matrices that are stored as vectors.  Either of the two scaling
  matrices can be `NULL`.

  Collective

  Input Parameters:
+ mat - the matrix to be scaled
. l   - the left scaling vector (or `NULL`)
- r   - the right scaling vector (or `NULL`)

  Level: intermediate

  Note:
  `MatDiagonalScale()` computes $A = LAR$, where
  L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
  The L scales the rows of the matrix, the R scales the columns of the matrix.

.seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
@*/
PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (l) {
    PetscValidHeaderSpecific(l, VEC_CLASSID, 2);
    PetscCheckSameComm(mat, 1, l, 2);
  }
  if (r) {
    PetscValidHeaderSpecific(r, VEC_CLASSID, 3);
    PetscCheckSameComm(mat, 1, r, 3);
  }
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);

  PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, diagonalscale, l, r);
  PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatScale - Scales all elements of a matrix by a given number.

  Logically Collective

  Input Parameters:
+ mat - the matrix to be scaled
- a   - the scaling value

  Level: intermediate

.seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
@*/
PetscErrorCode MatScale(Mat mat, PetscScalar a)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscValidLogicalCollectiveScalar(mat, a, 2);
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
  if (a != (PetscScalar)1.0) {
    PetscUseTypeMethod(mat, scale, a);
    PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  }
  PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatNorm - Calculates various norms of a matrix.

  Collective

  Input Parameters:
+ mat  - the matrix
- type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`

  Output Parameter:
. nrm - the resulting norm

  Level: intermediate

.seealso: [](ch_matrices), `Mat`
@*/
PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(nrm, 3);

  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscUseTypeMethod(mat, norm, type, nrm);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*
     This variable is used to prevent counting of MatAssemblyBegin() that
   are called from within a MatAssemblyEnd().
*/
static PetscInt MatAssemblyEnd_InUse = 0;
/*@
  MatAssemblyBegin - Begins assembling the matrix.  This routine should
  be called after completing all calls to `MatSetValues()`.

  Collective

  Input Parameters:
+ mat  - the matrix
- type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

  Level: beginner

  Notes:
  `MatSetValues()` generally caches the values that belong to other MPI processes.  The matrix is ready to
  use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.

  Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
  in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
  using the matrix.

  ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
  same flag of `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY` for all processes. Thus you CANNOT locally change from `ADD_VALUES` to `INSERT_VALUES`, that is
  a global collective operation requiring all processes that share the matrix.

  Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
  out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
  before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.

.seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
@*/
PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  MatCheckPreallocated(mat, 1);
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
  if (mat->assembled) {
    mat->was_assembled = PETSC_TRUE;
    mat->assembled     = PETSC_FALSE;
  }

  if (!MatAssemblyEnd_InUse) {
    PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
    PetscTryTypeMethod(mat, assemblybegin, type);
    PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
  } else PetscTryTypeMethod(mat, assemblybegin, type);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatAssembled - Indicates if a matrix has been assembled and is ready for
  use; for example, in matrix-vector product.

  Not Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. assembled - `PETSC_TRUE` or `PETSC_FALSE`

  Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
@*/
PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscAssertPointer(assembled, 2);
  *assembled = mat->assembled;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatAssemblyEnd - Completes assembling the matrix.  This routine should
  be called after `MatAssemblyBegin()`.

  Collective

  Input Parameters:
+ mat  - the matrix
- type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

  Options Database Keys:
+ -mat_view ::ascii_info             - Prints info on matrix at conclusion of `MatAssemblyEnd()`
. -mat_view ::ascii_info_detail      - Prints more detailed info
. -mat_view                          - Prints matrix in ASCII format
. -mat_view ::ascii_matlab           - Prints matrix in MATLAB format
. -mat_view draw                     - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
. -display <name>                    - Sets display name (default is host)
. -draw_pause <sec>                  - Sets number of seconds to pause after display
. -mat_view socket                   - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
. -viewer_socket_machine <machine>   - Machine to use for socket
. -viewer_socket_port <port>         - Port number to use for socket
- -mat_view binary:filename[:append] - Save matrix to file in binary format

  Level: beginner

.seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
@*/
PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
{
  static PetscInt inassm = 0;
  PetscBool       flg    = PETSC_FALSE;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);

  inassm++;
  MatAssemblyEnd_InUse++;
  if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
    PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
    PetscTryTypeMethod(mat, assemblyend, type);
    PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
  } else PetscTryTypeMethod(mat, assemblyend, type);

  /* Flush assembly is not a true assembly */
  if (type != MAT_FLUSH_ASSEMBLY) {
    if (mat->num_ass) {
      if (!mat->symmetry_eternal) {
        mat->symmetric = PETSC_BOOL3_UNKNOWN;
        mat->hermitian = PETSC_BOOL3_UNKNOWN;
      }
      if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
      if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
    }
    mat->num_ass++;
    mat->assembled        = PETSC_TRUE;
    mat->ass_nonzerostate = mat->nonzerostate;
  }

  mat->insertmode = NOT_SET_VALUES;
  MatAssemblyEnd_InUse--;
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
    PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));

    if (mat->checksymmetryonassembly) {
      PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
      if (flg) {
        PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
      } else {
        PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
      }
    }
    if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
  }
  inassm--;
  PetscFunctionReturn(PETSC_SUCCESS);
}

// PetscClangLinter pragma disable: -fdoc-section-header-unknown
/*@
  MatSetOption - Sets a parameter option for a matrix. Some options
  may be specific to certain storage formats.  Some options
  determine how values will be inserted (or added). Sorted,
  row-oriented input will generally assemble the fastest. The default
  is row-oriented.

  Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`

  Input Parameters:
+ mat - the matrix
. op  - the option, one of those listed below (and possibly others),
- flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

  Options Describing Matrix Structure:
+ `MAT_SPD`                         - symmetric positive definite
. `MAT_SYMMETRIC`                   - symmetric in terms of both structure and value
. `MAT_HERMITIAN`                   - transpose is the complex conjugation
. `MAT_STRUCTURALLY_SYMMETRIC`      - symmetric nonzero structure
. `MAT_SYMMETRY_ETERNAL`            - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
. `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
. `MAT_SPD_ETERNAL`                 - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix

   These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
   do not need to be computed (usually at a high cost)

   Options For Use with `MatSetValues()`:
   Insert a logically dense subblock, which can be
. `MAT_ROW_ORIENTED`                - row-oriented (default)

   These options reflect the data you pass in with `MatSetValues()`; it has
   nothing to do with how the data is stored internally in the matrix
   data structure.

   When (re)assembling a matrix, we can restrict the input for
   efficiency/debugging purposes.  These options include
. `MAT_NEW_NONZERO_LOCATIONS`       - additional insertions will be allowed if they generate a new nonzero (slow)
. `MAT_FORCE_DIAGONAL_ENTRIES`      - forces diagonal entries to be allocated
. `MAT_IGNORE_OFF_PROC_ENTRIES`     - drops off-processor entries
. `MAT_NEW_NONZERO_LOCATION_ERR`    - generates an error for new matrix entry
. `MAT_USE_HASH_TABLE`              - uses a hash table to speed up matrix assembly
. `MAT_NO_OFF_PROC_ENTRIES`         - you know each process will only set values for its own rows, will generate an error if
        any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
        performance for very large process counts.
- `MAT_SUBSET_OFF_PROC_ENTRIES`     - you know that the first assembly after setting this flag will set a superset
        of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
        functions, instead sending only neighbor messages.

  Level: intermediate

  Notes:
  Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and  `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!

  Some options are relevant only for particular matrix types and
  are thus ignored by others.  Other options are not supported by
  certain matrix types and will generate an error message if set.

  If using Fortran to compute a matrix, one may need to
  use the column-oriented option (or convert to the row-oriented
  format).

  `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
  that would generate a new entry in the nonzero structure is instead
  ignored.  Thus, if memory has not already been allocated for this particular
  data, then the insertion is ignored. For dense matrices, in which
  the entire array is allocated, no entries are ever ignored.
  Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

  `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
  that would generate a new entry in the nonzero structure instead produces
  an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats only.) If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

  `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
  that would generate a new entry that has not been preallocated will
  instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
  only.) This is a useful flag when debugging matrix memory preallocation.
  If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

  `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
  other processors should be dropped, rather than stashed.
  This is useful if you know that the "owning" processor is also
  always generating the correct matrix entries, so that PETSc need
  not transfer duplicate entries generated on another processor.

  `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
  searches during matrix assembly. When this flag is set, the hash table
  is created during the first matrix assembly. This hash table is
  used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
  to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
  should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
  supported by `MATMPIBAIJ` format only.

  `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
  are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`

  `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
  a zero location in the matrix

  `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types

  `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
  zero row routines and thus improves performance for very large process counts.

  `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
  part of the matrix (since they should match the upper triangular part).

  `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
  single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
  with finite difference schemes with non-periodic boundary conditions.

  Developer Note:
  `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
  places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
  to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
  not changed.

.seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
@*/
PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (op > 0) {
    PetscValidLogicalCollectiveEnum(mat, op, 2);
    PetscValidLogicalCollectiveBool(mat, flg, 3);
  }

  PetscCheck(((int)op) > MAT_OPTION_MIN && ((int)op) < MAT_OPTION_MAX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Options %d is out of range", (int)op);

  switch (op) {
  case MAT_FORCE_DIAGONAL_ENTRIES:
    mat->force_diagonals = flg;
    PetscFunctionReturn(PETSC_SUCCESS);
  case MAT_NO_OFF_PROC_ENTRIES:
    mat->nooffprocentries = flg;
    PetscFunctionReturn(PETSC_SUCCESS);
  case MAT_SUBSET_OFF_PROC_ENTRIES:
    mat->assembly_subset = flg;
    if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
#if !defined(PETSC_HAVE_MPIUNI)
      PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
#endif
      mat->stash.first_assembly_done = PETSC_FALSE;
    }
    PetscFunctionReturn(PETSC_SUCCESS);
  case MAT_NO_OFF_PROC_ZERO_ROWS:
    mat->nooffproczerorows = flg;
    PetscFunctionReturn(PETSC_SUCCESS);
  case MAT_SPD:
    if (flg) {
      mat->spd                    = PETSC_BOOL3_TRUE;
      mat->symmetric              = PETSC_BOOL3_TRUE;
      mat->structurally_symmetric = PETSC_BOOL3_TRUE;
    } else {
      mat->spd = PETSC_BOOL3_FALSE;
    }
    break;
  case MAT_SYMMETRIC:
    mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
    if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
#if !defined(PETSC_USE_COMPLEX)
    mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
#endif
    break;
  case MAT_HERMITIAN:
    mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
    if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
#if !defined(PETSC_USE_COMPLEX)
    mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
#endif
    break;
  case MAT_STRUCTURALLY_SYMMETRIC:
    mat->structurally_symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
    break;
  case MAT_SYMMETRY_ETERNAL:
    PetscCheck(mat->symmetric != PETSC_BOOL3_UNKNOWN, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot set MAT_SYMMETRY_ETERNAL without first setting MAT_SYMMETRIC to true or false");
    mat->symmetry_eternal = flg;
    if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
    break;
  case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
    PetscCheck(mat->structurally_symmetric != PETSC_BOOL3_UNKNOWN, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot set MAT_STRUCTURAL_SYMMETRY_ETERNAL without first setting MAT_STRUCTURALLY_SYMMETRIC to true or false");
    mat->structural_symmetry_eternal = flg;
    break;
  case MAT_SPD_ETERNAL:
    PetscCheck(mat->spd != PETSC_BOOL3_UNKNOWN, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot set MAT_SPD_ETERNAL without first setting MAT_SPD to true or false");
    mat->spd_eternal = flg;
    if (flg) {
      mat->structural_symmetry_eternal = PETSC_TRUE;
      mat->symmetry_eternal            = PETSC_TRUE;
    }
    break;
  case MAT_STRUCTURE_ONLY:
    mat->structure_only = flg;
    break;
  case MAT_SORTED_FULL:
    mat->sortedfull = flg;
    break;
  default:
    break;
  }
  PetscTryTypeMethod(mat, setoption, op, flg);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetOption - Gets a parameter option that has been set for a matrix.

  Logically Collective

  Input Parameters:
+ mat - the matrix
- op  - the option, this only responds to certain options, check the code for which ones

  Output Parameter:
. flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

  Level: intermediate

  Notes:
  Can only be called after `MatSetSizes()` and `MatSetType()` have been set.

  Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
  `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`

.seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
    `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
@*/
PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);

  PetscCheck(((int)op) > MAT_OPTION_MIN && ((int)op) < MAT_OPTION_MAX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Options %d is out of range", (int)op);
  PetscCheck(((PetscObject)mat)->type_name, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_TYPENOTSET, "Cannot get options until type and size have been set, see MatSetType() and MatSetSizes()");

  switch (op) {
  case MAT_NO_OFF_PROC_ENTRIES:
    *flg = mat->nooffprocentries;
    break;
  case MAT_NO_OFF_PROC_ZERO_ROWS:
    *flg = mat->nooffproczerorows;
    break;
  case MAT_SYMMETRIC:
    SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
    break;
  case MAT_HERMITIAN:
    SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
    break;
  case MAT_STRUCTURALLY_SYMMETRIC:
    SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
    break;
  case MAT_SPD:
    SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
    break;
  case MAT_SYMMETRY_ETERNAL:
    *flg = mat->symmetry_eternal;
    break;
  case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
    *flg = mat->symmetry_eternal;
    break;
  default:
    break;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
  this routine retains the old nonzero structure.

  Logically Collective

  Input Parameter:
. mat - the matrix

  Level: intermediate

  Note:
  If the matrix was not preallocated then a default, likely poor preallocation will be set in the matrix, so this should be called after the preallocation phase.
  See the Performance chapter of the users manual for information on preallocating matrices.

.seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
@*/
PetscErrorCode MatZeroEntries(Mat mat)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(mat->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for matrices where you have set values but not yet assembled");
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, zeroentries);
  PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
  of a set of rows and columns of a matrix.

  Collective

  Input Parameters:
+ mat     - the matrix
. numRows - the number of rows/columns to zero
. rows    - the global row indices
. diag    - value put in the diagonal of the eliminated rows
. x       - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
- b       - optional vector of the right-hand side, that will be adjusted by provided solution entries

  Level: intermediate

  Notes:
  This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

  For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
  The other entries of `b` will be adjusted by the known values of `x` times the corresponding matrix entries in the columns that are being eliminated

  If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
  Krylov method to take advantage of the known solution on the zeroed rows.

  For the parallel case, all processes that share the matrix (i.e.,
  those in the communicator used for matrix creation) MUST call this
  routine, regardless of whether any rows being zeroed are owned by
  them.

  Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
  removes them from the nonzero pattern. The nonzero pattern of the matrix can still change if a nonzero needs to be inserted on a diagonal entry that was previously
  missing.

  Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
  list only rows local to itself).

  The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.

.seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
          `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
@*/
PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (numRows) PetscAssertPointer(rows, 3);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
  PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
  of a set of rows and columns of a matrix.

  Collective

  Input Parameters:
+ mat  - the matrix
. is   - the rows to zero
. diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
. x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
- b    - optional vector of right-hand side, that will be adjusted by provided solution

  Level: intermediate

  Note:
  See `MatZeroRowsColumns()` for details on how this routine operates.

.seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
          `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
@*/
PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
{
  PetscInt        numRows;
  const PetscInt *rows;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(is, IS_CLASSID, 2);
  PetscValidType(mat, 1);
  PetscValidType(is, 2);
  PetscCall(ISGetLocalSize(is, &numRows));
  PetscCall(ISGetIndices(is, &rows));
  PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
  PetscCall(ISRestoreIndices(is, &rows));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatZeroRows - Zeros all entries (except possibly the main diagonal)
  of a set of rows of a matrix.

  Collective

  Input Parameters:
+ mat     - the matrix
. numRows - the number of rows to zero
. rows    - the global row indices
. diag    - value put in the diagonal of the zeroed rows
. x       - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
- b       - optional vector of right-hand side, that will be adjusted by provided solution entries

  Level: intermediate

  Notes:
  This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

  For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.

  If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
  Krylov method to take advantage of the known solution on the zeroed rows.

  May be followed by using a `PC` of type `PCREDISTRIBUTE` to solve the reduced problem (`PCDISTRIBUTE` completely eliminates the zeroed rows and their corresponding columns)
  from the matrix.

  Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
  but does not release memory.  Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense and block diagonal
  formats this does not alter the nonzero structure.

  If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
  of the matrix is not changed the values are
  merely zeroed.

  The user can set a value in the diagonal entry (or for the `MATAIJ` format
  formats can optionally remove the main diagonal entry from the
  nonzero structure as well, by passing 0.0 as the final argument).

  For the parallel case, all processes that share the matrix (i.e.,
  those in the communicator used for matrix creation) MUST call this
  routine, regardless of whether any rows being zeroed are owned by
  them.

  Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
  list only rows local to itself).

  You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
  owns that are to be zeroed. This saves a global synchronization in the implementation.

.seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
          `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
@*/
PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (numRows) PetscAssertPointer(rows, 3);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
  PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
  of a set of rows of a matrix.

  Collective

  Input Parameters:
+ mat  - the matrix
. is   - index set of rows to remove (if `NULL` then no row is removed)
. diag - value put in all diagonals of eliminated rows
. x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
- b    - optional vector of right-hand side, that will be adjusted by provided solution

  Level: intermediate

  Note:
  See `MatZeroRows()` for details on how this routine operates.

.seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
          `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
@*/
PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
{
  PetscInt        numRows = 0;
  const PetscInt *rows    = NULL;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (is) {
    PetscValidHeaderSpecific(is, IS_CLASSID, 2);
    PetscCall(ISGetLocalSize(is, &numRows));
    PetscCall(ISGetIndices(is, &rows));
  }
  PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
  if (is) PetscCall(ISRestoreIndices(is, &rows));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
  of a set of rows of a matrix. These rows must be local to the process.

  Collective

  Input Parameters:
+ mat     - the matrix
. numRows - the number of rows to remove
. rows    - the grid coordinates (and component number when dof > 1) for matrix rows
. diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
. x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
- b       - optional vector of right-hand side, that will be adjusted by provided solution

  Level: intermediate

  Notes:
  See `MatZeroRows()` for details on how this routine operates.

  The grid coordinates are across the entire grid, not just the local portion

  For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
  obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
  etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
  `DM_BOUNDARY_PERIODIC` boundary type.

  For indices that don't mean anything for your case (like the k index when working in 2d) or the c index when you have
  a single value per point) you can skip filling those indices.

  Fortran Note:
  `idxm` and `idxn` should be declared as
$     MatStencil idxm(4, m)
  and the values inserted using
.vb
    idxm(MatStencil_i, 1) = i
    idxm(MatStencil_j, 1) = j
    idxm(MatStencil_k, 1) = k
    idxm(MatStencil_c, 1) = c
   etc
.ve

.seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
          `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
@*/
PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
{
  PetscInt  dim    = mat->stencil.dim;
  PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
  PetscInt *dims   = mat->stencil.dims + 1;
  PetscInt *starts = mat->stencil.starts;
  PetscInt *dxm    = (PetscInt *)rows;
  PetscInt *jdxm, i, j, tmp, numNewRows = 0;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (numRows) PetscAssertPointer(rows, 3);

  PetscCall(PetscMalloc1(numRows, &jdxm));
  for (i = 0; i < numRows; ++i) {
    /* Skip unused dimensions (they are ordered k, j, i, c) */
    for (j = 0; j < 3 - sdim; ++j) dxm++;
    /* Local index in X dir */
    tmp = *dxm++ - starts[0];
    /* Loop over remaining dimensions */
    for (j = 0; j < dim - 1; ++j) {
      /* If nonlocal, set index to be negative */
      if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
      /* Update local index */
      else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
    }
    /* Skip component slot if necessary */
    if (mat->stencil.noc) dxm++;
    /* Local row number */
    if (tmp >= 0) jdxm[numNewRows++] = tmp;
  }
  PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
  PetscCall(PetscFree(jdxm));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
  of a set of rows and columns of a matrix.

  Collective

  Input Parameters:
+ mat     - the matrix
. numRows - the number of rows/columns to remove
. rows    - the grid coordinates (and component number when dof > 1) for matrix rows
. diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
. x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
- b       - optional vector of right-hand side, that will be adjusted by provided solution

  Level: intermediate

  Notes:
  See `MatZeroRowsColumns()` for details on how this routine operates.

  The grid coordinates are across the entire grid, not just the local portion

  For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
  obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
  etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
  `DM_BOUNDARY_PERIODIC` boundary type.

  For indices that don't mean anything for your case (like the k index when working in 2d) or the c index when you have
  a single value per point) you can skip filling those indices.

  Fortran Note:
  `idxm` and `idxn` should be declared as
$     MatStencil idxm(4, m)
  and the values inserted using
.vb
    idxm(MatStencil_i, 1) = i
    idxm(MatStencil_j, 1) = j
    idxm(MatStencil_k, 1) = k
    idxm(MatStencil_c, 1) = c
    etc
.ve

.seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
          `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
@*/
PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
{
  PetscInt  dim    = mat->stencil.dim;
  PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
  PetscInt *dims   = mat->stencil.dims + 1;
  PetscInt *starts = mat->stencil.starts;
  PetscInt *dxm    = (PetscInt *)rows;
  PetscInt *jdxm, i, j, tmp, numNewRows = 0;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (numRows) PetscAssertPointer(rows, 3);

  PetscCall(PetscMalloc1(numRows, &jdxm));
  for (i = 0; i < numRows; ++i) {
    /* Skip unused dimensions (they are ordered k, j, i, c) */
    for (j = 0; j < 3 - sdim; ++j) dxm++;
    /* Local index in X dir */
    tmp = *dxm++ - starts[0];
    /* Loop over remaining dimensions */
    for (j = 0; j < dim - 1; ++j) {
      /* If nonlocal, set index to be negative */
      if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
      /* Update local index */
      else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
    }
    /* Skip component slot if necessary */
    if (mat->stencil.noc) dxm++;
    /* Local row number */
    if (tmp >= 0) jdxm[numNewRows++] = tmp;
  }
  PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
  PetscCall(PetscFree(jdxm));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
  of a set of rows of a matrix; using local numbering of rows.

  Collective

  Input Parameters:
+ mat     - the matrix
. numRows - the number of rows to remove
. rows    - the local row indices
. diag    - value put in all diagonals of eliminated rows
. x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
- b       - optional vector of right-hand side, that will be adjusted by provided solution

  Level: intermediate

  Notes:
  Before calling `MatZeroRowsLocal()`, the user must first set the
  local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.

  See `MatZeroRows()` for details on how this routine operates.

.seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
          `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
@*/
PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (numRows) PetscAssertPointer(rows, 3);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  if (mat->ops->zerorowslocal) {
    PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
  } else {
    IS              is, newis;
    const PetscInt *newRows;

    PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
    PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
    PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
    PetscCall(ISGetIndices(newis, &newRows));
    PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
    PetscCall(ISRestoreIndices(newis, &newRows));
    PetscCall(ISDestroy(&newis));
    PetscCall(ISDestroy(&is));
  }
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
  of a set of rows of a matrix; using local numbering of rows.

  Collective

  Input Parameters:
+ mat  - the matrix
. is   - index set of rows to remove
. diag - value put in all diagonals of eliminated rows
. x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
- b    - optional vector of right-hand side, that will be adjusted by provided solution

  Level: intermediate

  Notes:
  Before calling `MatZeroRowsLocalIS()`, the user must first set the
  local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

  See `MatZeroRows()` for details on how this routine operates.

.seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
          `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
@*/
PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
{
  PetscInt        numRows;
  const PetscInt *rows;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(is, IS_CLASSID, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscCall(ISGetLocalSize(is, &numRows));
  PetscCall(ISGetIndices(is, &rows));
  PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
  PetscCall(ISRestoreIndices(is, &rows));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
  of a set of rows and columns of a matrix; using local numbering of rows.

  Collective

  Input Parameters:
+ mat     - the matrix
. numRows - the number of rows to remove
. rows    - the global row indices
. diag    - value put in all diagonals of eliminated rows
. x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
- b       - optional vector of right-hand side, that will be adjusted by provided solution

  Level: intermediate

  Notes:
  Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
  local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

  See `MatZeroRowsColumns()` for details on how this routine operates.

.seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
          `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
@*/
PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
{
  IS              is, newis;
  const PetscInt *newRows;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (numRows) PetscAssertPointer(rows, 3);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
  PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
  PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
  PetscCall(ISGetIndices(newis, &newRows));
  PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
  PetscCall(ISRestoreIndices(newis, &newRows));
  PetscCall(ISDestroy(&newis));
  PetscCall(ISDestroy(&is));
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
  of a set of rows and columns of a matrix; using local numbering of rows.

  Collective

  Input Parameters:
+ mat  - the matrix
. is   - index set of rows to remove
. diag - value put in all diagonals of eliminated rows
. x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
- b    - optional vector of right-hand side, that will be adjusted by provided solution

  Level: intermediate

  Notes:
  Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
  local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

  See `MatZeroRowsColumns()` for details on how this routine operates.

.seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
          `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
@*/
PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
{
  PetscInt        numRows;
  const PetscInt *rows;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(is, IS_CLASSID, 2);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscCall(ISGetLocalSize(is, &numRows));
  PetscCall(ISGetIndices(is, &rows));
  PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
  PetscCall(ISRestoreIndices(is, &rows));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetSize - Returns the numbers of rows and columns in a matrix.

  Not Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ m - the number of global rows
- n - the number of global columns

  Level: beginner

  Note:
  Both output parameters can be `NULL` on input.

.seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
@*/
PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (m) *m = mat->rmap->N;
  if (n) *n = mat->cmap->N;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
  of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.

  Not Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ m - the number of local rows, use `NULL` to not obtain this value
- n - the number of local columns, use `NULL` to not obtain this value

  Level: beginner

.seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
@*/
PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (m) PetscAssertPointer(m, 2);
  if (n) PetscAssertPointer(n, 3);
  if (m) *m = mat->rmap->n;
  if (n) *n = mat->cmap->n;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
  vector one multiplies this matrix by that are owned by this processor.

  Not Collective, unless matrix has not been allocated, then collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ m - the global index of the first local column, use `NULL` to not obtain this value
- n - one more than the global index of the last local column, use `NULL` to not obtain this value

  Level: developer

  Notes:
  If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

  If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
  If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

  For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
  the local values in the matrix.

  Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
  Layouts](sec_matlayout) for details on matrix layouts.

.seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
          `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
@*/
PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (m) PetscAssertPointer(m, 2);
  if (n) PetscAssertPointer(n, 3);
  MatCheckPreallocated(mat, 1);
  if (m) *m = mat->cmap->rstart;
  if (n) *n = mat->cmap->rend;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
  this MPI process.

  Not Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ m - the global index of the first local row, use `NULL` to not obtain this value
- n - one more than the global index of the last local row, use `NULL` to not obtain this value

  Level: beginner

  Notes:
  If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

  If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
  If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

  For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
  the local values in the matrix.

  The high argument is one more than the last element stored locally.

  For all matrices  it returns the range of matrix rows associated with rows of a vector that
  would contain the result of a matrix vector product with this matrix. See [Matrix
  Layouts](sec_matlayout) for details on matrix layouts.

.seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
          `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
@*/
PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (m) PetscAssertPointer(m, 2);
  if (n) PetscAssertPointer(n, 3);
  MatCheckPreallocated(mat, 1);
  if (m) *m = mat->rmap->rstart;
  if (n) *n = mat->rmap->rend;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
  `MATSCALAPACK`, returns the range of matrix rows owned by each process.

  Not Collective, unless matrix has not been allocated

  Input Parameter:
. mat - the matrix

  Output Parameter:
. ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
           where `size` is the number of MPI processes used by `mat`

  Level: beginner

  Notes:
  If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

  If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
  If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

  For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
  the local values in the matrix.

  For all matrices  it returns the ranges of matrix rows associated with rows of a vector that
  would contain the result of a matrix vector product with this matrix. See [Matrix
  Layouts](sec_matlayout) for details on matrix layouts.

.seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
          `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
          `DMDAGetGhostCorners()`, `DM`
@*/
PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  MatCheckPreallocated(mat, 1);
  PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
  vector one multiplies this vector by that are owned by each processor.

  Not Collective, unless matrix has not been allocated

  Input Parameter:
. mat - the matrix

  Output Parameter:
. ranges - start of each processors portion plus one more than the total length at the end

  Level: beginner

  Notes:
  If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

  If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
  If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

  For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
  the local values in the matrix.

  Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
  Layouts](sec_matlayout) for details on matrix layouts.

.seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
          `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
          `DMDAGetGhostCorners()`, `DM`
@*/
PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  MatCheckPreallocated(mat, 1);
  PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.

  Not Collective

  Input Parameter:
. A - matrix

  Output Parameters:
+ rows - rows in which this process owns elements, , use `NULL` to not obtain this value
- cols - columns in which this process owns elements, use `NULL` to not obtain this value

  Level: intermediate

  Note:
  You should call `ISDestroy()` on the returned `IS`

  For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
  returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
  `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
  details on matrix layouts.

.seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
@*/
PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
{
  PetscErrorCode (*f)(Mat, IS *, IS *);

  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  MatCheckPreallocated(A, 1);
  PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
  if (f) {
    PetscCall((*f)(A, rows, cols));
  } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
    if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
    if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
  Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
  to complete the factorization.

  Collective

  Input Parameters:
+ fact - the factorized matrix obtained with `MatGetFactor()`
. mat  - the matrix
. row  - row permutation
. col  - column permutation
- info - structure containing
.vb
      levels - number of levels of fill.
      expected fill - as ratio of original fill.
      1 or 0 - indicating force fill on diagonal (improves robustness for matrices
                missing diagonal entries)
.ve

  Level: developer

  Notes:
  See [Matrix Factorization](sec_matfactor) for additional information.

  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to `MatFactorInfo`]

.seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
          `MatGetOrdering()`, `MatFactorInfo`
@*/
PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 2);
  PetscValidType(mat, 2);
  if (row) PetscValidHeaderSpecific(row, IS_CLASSID, 3);
  if (col) PetscValidHeaderSpecific(col, IS_CLASSID, 4);
  PetscAssertPointer(info, 5);
  PetscAssertPointer(fact, 1);
  PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
  PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 2);

  if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
  PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
  if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatICCFactorSymbolic - Performs symbolic incomplete
  Cholesky factorization for a symmetric matrix.  Use
  `MatCholeskyFactorNumeric()` to complete the factorization.

  Collective

  Input Parameters:
+ fact - the factorized matrix obtained with `MatGetFactor()`
. mat  - the matrix to be factored
. perm - row and column permutation
- info - structure containing
.vb
      levels - number of levels of fill.
      expected fill - as ratio of original fill.
.ve

  Level: developer

  Notes:
  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  This uses the definition of level of fill as in Y. Saad {cite}`saad2003`

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to `MatFactorInfo`]

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
@*/
PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 2);
  PetscValidType(mat, 2);
  if (perm) PetscValidHeaderSpecific(perm, IS_CLASSID, 3);
  PetscAssertPointer(info, 4);
  PetscAssertPointer(fact, 1);
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
  PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  MatCheckPreallocated(mat, 2);

  if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
  PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
  if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
  points to an array of valid matrices, they may be reused to store the new
  submatrices.

  Collective

  Input Parameters:
+ mat   - the matrix
. n     - the number of submatrixes to be extracted (on this processor, may be zero)
. irow  - index set of rows to extract
. icol  - index set of columns to extract
- scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

  Output Parameter:
. submat - the array of submatrices

  Level: advanced

  Notes:
  `MatCreateSubMatrices()` can extract ONLY sequential submatrices
  (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
  to extract a parallel submatrix.

  Some matrix types place restrictions on the row and column
  indices, such as that they be sorted or that they be equal to each other.

  The index sets may not have duplicate entries.

  When extracting submatrices from a parallel matrix, each processor can
  form a different submatrix by setting the rows and columns of its
  individual index sets according to the local submatrix desired.

  When finished using the submatrices, the user should destroy
  them with `MatDestroySubMatrices()`.

  `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
  original matrix has not changed from that last call to `MatCreateSubMatrices()`.

  This routine creates the matrices in submat; you should NOT create them before
  calling it. It also allocates the array of matrix pointers submat.

  For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
  request one row/column in a block, they must request all rows/columns that are in
  that block. For example, if the block size is 2 you cannot request just row 0 and
  column 0.

  Fortran Note:
  One must pass in as `submat` a `Mat` array of size at least `n`+1.

.seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
@*/
PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
{
  PetscInt  i;
  PetscBool eq;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (n) {
    PetscAssertPointer(irow, 3);
    for (i = 0; i < n; i++) PetscValidHeaderSpecific(irow[i], IS_CLASSID, 3);
    PetscAssertPointer(icol, 4);
    for (i = 0; i < n; i++) PetscValidHeaderSpecific(icol[i], IS_CLASSID, 4);
  }
  PetscAssertPointer(submat, 6);
  if (n && scall == MAT_REUSE_MATRIX) {
    PetscAssertPointer(*submat, 6);
    for (i = 0; i < n; i++) PetscValidHeaderSpecific((*submat)[i], MAT_CLASSID, 6);
  }
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
  PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
  for (i = 0; i < n; i++) {
    (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
    PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
    if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
#if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
    if (mat->boundtocpu && mat->bindingpropagates) {
      PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
      PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
    }
#endif
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of mat (by pairs of `IS` that may live on subcomms).

  Collective

  Input Parameters:
+ mat   - the matrix
. n     - the number of submatrixes to be extracted
. irow  - index set of rows to extract
. icol  - index set of columns to extract
- scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

  Output Parameter:
. submat - the array of submatrices

  Level: advanced

  Note:
  This is used by `PCGASM`

.seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
@*/
PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
{
  PetscInt  i;
  PetscBool eq;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (n) {
    PetscAssertPointer(irow, 3);
    PetscValidHeaderSpecific(*irow, IS_CLASSID, 3);
    PetscAssertPointer(icol, 4);
    PetscValidHeaderSpecific(*icol, IS_CLASSID, 4);
  }
  PetscAssertPointer(submat, 6);
  if (n && scall == MAT_REUSE_MATRIX) {
    PetscAssertPointer(*submat, 6);
    PetscValidHeaderSpecific(**submat, MAT_CLASSID, 6);
  }
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
  PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
  for (i = 0; i < n; i++) {
    PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
    if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatDestroyMatrices - Destroys an array of matrices.

  Collective

  Input Parameters:
+ n   - the number of local matrices
- mat - the matrices (this is a pointer to the array of matrices)

  Level: advanced

  Notes:
  Frees not only the matrices, but also the array that contains the matrices

  For matrices obtained with  `MatCreateSubMatrices()` use `MatDestroySubMatrices()`

  Fortran Note:
  Does not free the `mat` array.

.seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
@*/
PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
{
  PetscInt i;

  PetscFunctionBegin;
  if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
  PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
  PetscAssertPointer(mat, 2);

  for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));

  /* memory is allocated even if n = 0 */
  PetscCall(PetscFree(*mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.

  Collective

  Input Parameters:
+ n   - the number of local matrices
- mat - the matrices (this is a pointer to the array of matrices, just to match the calling
                       sequence of `MatCreateSubMatrices()`)

  Level: advanced

  Note:
  Frees not only the matrices, but also the array that contains the matrices

  Fortran Note:
  Does not free the `mat` array.

.seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
@*/
PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
{
  Mat mat0;

  PetscFunctionBegin;
  if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
  /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
  PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
  PetscAssertPointer(mat, 2);

  mat0 = (*mat)[0];
  if (mat0 && mat0->ops->destroysubmatrices) {
    PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
  } else {
    PetscCall(MatDestroyMatrices(n, mat));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process

  Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. matstruct - the sequential matrix with the nonzero structure of `mat`

  Level: developer

.seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
@*/
PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscAssertPointer(matstruct, 2);

  PetscValidType(mat, 1);
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
  PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.

  Collective

  Input Parameter:
. mat - the matrix

  Level: advanced

  Note:
  This is not needed, one can just call `MatDestroy()`

.seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
@*/
PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
{
  PetscFunctionBegin;
  PetscAssertPointer(mat, 1);
  PetscCall(MatDestroy(mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
  replaces the index sets by larger ones that represent submatrices with
  additional overlap.

  Collective

  Input Parameters:
+ mat - the matrix
. n   - the number of index sets
. is  - the array of index sets (these index sets will changed during the call)
- ov  - the additional overlap requested

  Options Database Key:
. -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

  Level: developer

  Note:
  The computed overlap preserves the matrix block sizes when the blocks are square.
  That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
  that block are included in the overlap regardless of whether each specific column would increase the overlap.

.seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
@*/
PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
{
  PetscInt i, bs, cbs;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscValidLogicalCollectiveInt(mat, n, 2);
  PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
  if (n) {
    PetscAssertPointer(is, 3);
    for (i = 0; i < n; i++) PetscValidHeaderSpecific(is[i], IS_CLASSID, 3);
  }
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
  PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
  PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
  PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
  if (bs == cbs) {
    for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);

/*@
  MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
  a sub communicator, replaces the index sets by larger ones that represent submatrices with
  additional overlap.

  Collective

  Input Parameters:
+ mat - the matrix
. n   - the number of index sets
. is  - the array of index sets (these index sets will changed during the call)
- ov  - the additional overlap requested

  `   Options Database Key:
. -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

  Level: developer

.seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
@*/
PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
{
  PetscInt i;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
  if (n) {
    PetscAssertPointer(is, 3);
    PetscValidHeaderSpecific(*is, IS_CLASSID, 3);
  }
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
  PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
  for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
  PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetBlockSize - Returns the matrix block size.

  Not Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. bs - block size

  Level: intermediate

  Notes:
  Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.

  If the block size has not been set yet this routine returns 1.

.seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
@*/
PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscAssertPointer(bs, 2);
  *bs = PetscAbs(mat->rmap->bs);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetBlockSizes - Returns the matrix block row and column sizes.

  Not Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ rbs - row block size
- cbs - column block size

  Level: intermediate

  Notes:
  Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
  If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

  If a block size has not been set yet this routine returns 1.

.seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
@*/
PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (rbs) PetscAssertPointer(rbs, 2);
  if (cbs) PetscAssertPointer(cbs, 3);
  if (rbs) *rbs = PetscAbs(mat->rmap->bs);
  if (cbs) *cbs = PetscAbs(mat->cmap->bs);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetBlockSize - Sets the matrix block size.

  Logically Collective

  Input Parameters:
+ mat - the matrix
- bs  - block size

  Level: intermediate

  Notes:
  Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
  This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

  For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
  is compatible with the matrix local sizes.

.seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
@*/
PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidLogicalCollectiveInt(mat, bs, 2);
  PetscCall(MatSetBlockSizes(mat, bs, bs));
  PetscFunctionReturn(PETSC_SUCCESS);
}

typedef struct {
  PetscInt         n;
  IS              *is;
  Mat             *mat;
  PetscObjectState nonzerostate;
  Mat              C;
} EnvelopeData;

static PetscErrorCode EnvelopeDataDestroy(void **ptr)
{
  EnvelopeData *edata = (EnvelopeData *)*ptr;

  PetscFunctionBegin;
  for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
  PetscCall(PetscFree(edata->is));
  PetscCall(PetscFree(edata));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
  the sizes of these blocks in the matrix. An individual block may lie over several processes.

  Collective

  Input Parameter:
. mat - the matrix

  Level: intermediate

  Notes:
  There can be zeros within the blocks

  The blocks can overlap between processes, including laying on more than two processes

.seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
@*/
PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
{
  PetscInt           n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
  PetscInt          *diag, *odiag, sc;
  VecScatter         scatter;
  PetscScalar       *seqv;
  const PetscScalar *parv;
  const PetscInt    *ia, *ja;
  PetscBool          set, flag, done;
  Mat                AA = mat, A;
  MPI_Comm           comm;
  PetscMPIInt        rank, size, tag;
  MPI_Status         status;
  PetscContainer     container;
  EnvelopeData      *edata;
  Vec                seq, par;
  IS                 isglobal;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
  if (!set || !flag) {
    /* TODO: only needs nonzero structure of transpose */
    PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
    PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
  }
  PetscCall(MatAIJGetLocalMat(AA, &A));
  PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
  PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");

  PetscCall(MatGetLocalSize(mat, &n, NULL));
  PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
  PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
  PetscCallMPI(MPI_Comm_size(comm, &size));
  PetscCallMPI(MPI_Comm_rank(comm, &rank));

  PetscCall(PetscMalloc2(n, &sizes, n, &starts));

  if (rank > 0) {
    PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
    PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
  }
  PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
  for (i = 0; i < n; i++) {
    env = PetscMax(env, ja[ia[i + 1] - 1]);
    II  = rstart + i;
    if (env == II) {
      starts[lblocks]  = tbs;
      sizes[lblocks++] = 1 + II - tbs;
      tbs              = 1 + II;
    }
  }
  if (rank < size - 1) {
    PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
    PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
  }

  PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
  if (!set || !flag) PetscCall(MatDestroy(&AA));
  PetscCall(MatDestroy(&A));

  PetscCall(PetscNew(&edata));
  PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
  edata->n = lblocks;
  /* create IS needed for extracting blocks from the original matrix */
  PetscCall(PetscMalloc1(lblocks, &edata->is));
  for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));

  /* Create the resulting inverse matrix structure with preallocation information */
  PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
  PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
  PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
  PetscCall(MatSetType(edata->C, MATAIJ));

  /* Communicate the start and end of each row, from each block to the correct rank */
  /* TODO: Use PetscSF instead of VecScatter */
  for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
  PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
  PetscCall(VecGetArrayWrite(seq, &seqv));
  for (PetscInt i = 0; i < lblocks; i++) {
    for (PetscInt j = 0; j < sizes[i]; j++) {
      seqv[cnt]     = starts[i];
      seqv[cnt + 1] = starts[i] + sizes[i];
      cnt += 2;
    }
  }
  PetscCall(VecRestoreArrayWrite(seq, &seqv));
  PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
  sc -= cnt;
  PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
  PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
  PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
  PetscCall(ISDestroy(&isglobal));
  PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
  PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
  PetscCall(VecScatterDestroy(&scatter));
  PetscCall(VecDestroy(&seq));
  PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
  PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
  PetscCall(VecGetArrayRead(par, &parv));
  cnt = 0;
  PetscCall(MatGetSize(mat, NULL, &n));
  for (PetscInt i = 0; i < mat->rmap->n; i++) {
    PetscInt start, end, d = 0, od = 0;

    start = (PetscInt)PetscRealPart(parv[cnt]);
    end   = (PetscInt)PetscRealPart(parv[cnt + 1]);
    cnt += 2;

    if (start < cstart) {
      od += cstart - start + n - cend;
      d += cend - cstart;
    } else if (start < cend) {
      od += n - cend;
      d += cend - start;
    } else od += n - start;
    if (end <= cstart) {
      od -= cstart - end + n - cend;
      d -= cend - cstart;
    } else if (end < cend) {
      od -= n - cend;
      d -= cend - end;
    } else od -= n - end;

    odiag[i] = od;
    diag[i]  = d;
  }
  PetscCall(VecRestoreArrayRead(par, &parv));
  PetscCall(VecDestroy(&par));
  PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
  PetscCall(PetscFree2(diag, odiag));
  PetscCall(PetscFree2(sizes, starts));

  PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
  PetscCall(PetscContainerSetPointer(container, edata));
  PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
  PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
  PetscCall(PetscObjectDereference((PetscObject)container));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A

  Collective

  Input Parameters:
+ A     - the matrix
- reuse - indicates if the `C` matrix was obtained from a previous call to this routine

  Output Parameter:
. C - matrix with inverted block diagonal of `A`

  Level: advanced

  Note:
  For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.

.seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
@*/
PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
{
  PetscContainer   container;
  EnvelopeData    *edata;
  PetscObjectState nonzerostate;

  PetscFunctionBegin;
  PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
  if (!container) {
    PetscCall(MatComputeVariableBlockEnvelope(A));
    PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
  }
  PetscCall(PetscContainerGetPointer(container, (void **)&edata));
  PetscCall(MatGetNonzeroState(A, &nonzerostate));
  PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
  PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");

  PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
  *C = edata->C;

  for (PetscInt i = 0; i < edata->n; i++) {
    Mat          D;
    PetscScalar *dvalues;

    PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
    PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
    PetscCall(MatSeqDenseInvert(D));
    PetscCall(MatDenseGetArray(D, &dvalues));
    PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
    PetscCall(MatDestroy(&D));
  }
  PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
  PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
  PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size

  Not Collective

  Input Parameters:
+ mat     - the matrix
. nblocks - the number of blocks on this process, each block can only exist on a single process
- bsizes  - the block sizes

  Level: intermediate

  Notes:
  Currently used by `PCVPBJACOBI` for `MATAIJ` matrices

  Each variable point-block set of degrees of freedom must live on a single MPI process. That is a point block cannot straddle two MPI processes.

.seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
          `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
@*/
PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
{
  PetscInt ncnt = 0, nlocal;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
  PetscCheck(nblocks >= 0 && nblocks <= nlocal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number of local blocks %" PetscInt_FMT " is not in [0, %" PetscInt_FMT "]", nblocks, nlocal);
  for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
  PetscCheck(ncnt == nlocal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Sum of local block sizes %" PetscInt_FMT " does not equal local size of matrix %" PetscInt_FMT, ncnt, nlocal);
  PetscCall(PetscFree(mat->bsizes));
  mat->nblocks = nblocks;
  PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
  PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size

  Not Collective; No Fortran Support

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ nblocks - the number of blocks on this process
- bsizes  - the block sizes

  Level: intermediate

.seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
@*/
PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (nblocks) *nblocks = mat->nblocks;
  if (bsizes) *bsizes = mat->bsizes;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetBlockSizes - Sets the matrix block row and column sizes.

  Logically Collective

  Input Parameters:
+ mat - the matrix
. rbs - row block size
- cbs - column block size

  Level: intermediate

  Notes:
  Block row formats are `MATBAIJ` and  `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
  If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
  This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

  For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
  are compatible with the matrix local sizes.

  The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.

.seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
@*/
PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidLogicalCollectiveInt(mat, rbs, 2);
  PetscValidLogicalCollectiveInt(mat, cbs, 3);
  PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
  if (mat->rmap->refcnt) {
    ISLocalToGlobalMapping l2g  = NULL;
    PetscLayout            nmap = NULL;

    PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
    if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
    PetscCall(PetscLayoutDestroy(&mat->rmap));
    mat->rmap          = nmap;
    mat->rmap->mapping = l2g;
  }
  if (mat->cmap->refcnt) {
    ISLocalToGlobalMapping l2g  = NULL;
    PetscLayout            nmap = NULL;

    PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
    if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
    PetscCall(PetscLayoutDestroy(&mat->cmap));
    mat->cmap          = nmap;
    mat->cmap->mapping = l2g;
  }
  PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
  PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

  Logically Collective

  Input Parameters:
+ mat     - the matrix
. fromRow - matrix from which to copy row block size
- fromCol - matrix from which to copy column block size (can be same as fromRow)

  Level: developer

.seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
@*/
PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(fromRow, MAT_CLASSID, 2);
  PetscValidHeaderSpecific(fromCol, MAT_CLASSID, 3);
  if (fromRow->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
  if (fromCol->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatResidual - Default routine to calculate the residual r = b - Ax

  Collective

  Input Parameters:
+ mat - the matrix
. b   - the right-hand-side
- x   - the approximate solution

  Output Parameter:
. r - location to store the residual

  Level: developer

.seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
@*/
PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(b, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 3);
  PetscValidHeaderSpecific(r, VEC_CLASSID, 4);
  PetscValidType(mat, 1);
  MatCheckPreallocated(mat, 1);
  PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
  if (!mat->ops->residual) {
    PetscCall(MatMult(mat, x, r));
    PetscCall(VecAYPX(r, -1.0, b));
  } else {
    PetscUseTypeMethod(mat, residual, b, x, r);
  }
  PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*MC
    MatGetRowIJF90 - Obtains the compressed row storage i and j indices for the local rows of a sparse matrix

    Synopsis:
    MatGetRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

    Not Collective

    Input Parameters:
+   A - the matrix
.   shift -  0 or 1 indicating we want the indices starting at 0 or 1
.   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
-   inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
                 inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
                 always used.

    Output Parameters:
+   n - number of local rows in the (possibly compressed) matrix
.   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
.   ja - the column indices
-   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
           are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

    Level: developer

    Note:
    Use  `MatRestoreRowIJF90()` when you no longer need access to the data

.seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatRestoreRowIJF90()`
M*/

/*MC
    MatRestoreRowIJF90 - restores the compressed row storage i and j indices for the local rows of a sparse matrix obtained with `MatGetRowIJF90()`

    Synopsis:
    MatRestoreRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

    Not Collective

    Input Parameters:
+   A - the  matrix
.   shift -  0 or 1 indicating we want the indices starting at 0 or 1
.   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
    inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
                 inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
                 always used.
.   n - number of local rows in the (possibly compressed) matrix
.   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
.   ja - the column indices
-   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
           are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

    Level: developer

.seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatGetRowIJF90()`
M*/

/*@C
  MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix

  Collective

  Input Parameters:
+ mat             - the matrix
. shift           - 0 or 1 indicating we want the indices starting at 0 or 1
. symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
- inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
                 inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
                 always used.

  Output Parameters:
+ n    - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
. ia   - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix, use `NULL` if not needed
. ja   - the column indices, use `NULL` if not needed
- done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
           are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

  Level: developer

  Notes:
  You CANNOT change any of the ia[] or ja[] values.

  Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.

  Fortran Notes:
  Use
.vb
    PetscInt, pointer :: ia(:),ja(:)
    call MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
    ! Access the ith and jth entries via ia(i) and ja(j)
.ve

  `MatGetRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatGetRowIJF90()`

.seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetRowIJF90()`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
@*/
PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (n) PetscAssertPointer(n, 5);
  if (ia) PetscAssertPointer(ia, 6);
  if (ja) PetscAssertPointer(ja, 7);
  if (done) PetscAssertPointer(done, 8);
  MatCheckPreallocated(mat, 1);
  if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
  else {
    if (done) *done = PETSC_TRUE;
    PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
    PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
    PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

  Collective

  Input Parameters:
+ mat             - the matrix
. shift           - 1 or zero indicating we want the indices starting at 0 or 1
. symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
                symmetrized
. inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
                 inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
                 always used.
. n               - number of columns in the (possibly compressed) matrix
. ia              - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
- ja              - the row indices

  Output Parameter:
. done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned

  Level: developer

.seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
@*/
PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(n, 5);
  if (ia) PetscAssertPointer(ia, 6);
  if (ja) PetscAssertPointer(ja, 7);
  PetscAssertPointer(done, 8);
  MatCheckPreallocated(mat, 1);
  if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
  else {
    *done = PETSC_TRUE;
    PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.

  Collective

  Input Parameters:
+ mat             - the matrix
. shift           - 1 or zero indicating we want the indices starting at 0 or 1
. symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
. inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
                    inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
                    always used.
. n               - size of (possibly compressed) matrix
. ia              - the row pointers
- ja              - the column indices

  Output Parameter:
. done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

  Level: developer

  Note:
  This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
  us of the array after it has been restored. If you pass `NULL`, it will
  not zero the pointers.  Use of ia or ja after `MatRestoreRowIJ()` is invalid.

  Fortran Note:
  `MatRestoreRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatRestoreRowIJF90()`

.seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreRowIJF90()`, `MatRestoreColumnIJ()`
@*/
PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (ia) PetscAssertPointer(ia, 6);
  if (ja) PetscAssertPointer(ja, 7);
  if (done) PetscAssertPointer(done, 8);
  MatCheckPreallocated(mat, 1);

  if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
  else {
    if (done) *done = PETSC_TRUE;
    PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
    if (n) *n = 0;
    if (ia) *ia = NULL;
    if (ja) *ja = NULL;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.

  Collective

  Input Parameters:
+ mat             - the matrix
. shift           - 1 or zero indicating we want the indices starting at 0 or 1
. symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
- inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
                    inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
                    always used.

  Output Parameters:
+ n    - size of (possibly compressed) matrix
. ia   - the column pointers
. ja   - the row indices
- done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

  Level: developer

.seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
@*/
PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (ia) PetscAssertPointer(ia, 6);
  if (ja) PetscAssertPointer(ja, 7);
  PetscAssertPointer(done, 8);
  MatCheckPreallocated(mat, 1);

  if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
  else {
    *done = PETSC_TRUE;
    PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
    if (n) *n = 0;
    if (ia) *ia = NULL;
    if (ja) *ja = NULL;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
  `MatGetColumnIJ()`.

  Collective

  Input Parameters:
+ mat        - the matrix
. ncolors    - maximum color value
. n          - number of entries in colorarray
- colorarray - array indicating color for each column

  Output Parameter:
. iscoloring - coloring generated using colorarray information

  Level: developer

.seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
@*/
PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(colorarray, 4);
  PetscAssertPointer(iscoloring, 5);
  MatCheckPreallocated(mat, 1);

  if (!mat->ops->coloringpatch) {
    PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
  } else {
    PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

  Logically Collective

  Input Parameter:
. mat - the factored matrix to be reset

  Level: developer

  Notes:
  This routine should be used only with factored matrices formed by in-place
  factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
  format).  This option can save memory, for example, when solving nonlinear
  systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
  ILU(0) preconditioner.

  One can specify in-place ILU(0) factorization by calling
.vb
     PCType(pc,PCILU);
     PCFactorSeUseInPlace(pc);
.ve
  or by using the options -pc_type ilu -pc_factor_in_place

  In-place factorization ILU(0) can also be used as a local
  solver for the blocks within the block Jacobi or additive Schwarz
  methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
  for details on setting local solver options.

  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

.seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
@*/
PetscErrorCode MatSetUnfactored(Mat mat)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  MatCheckPreallocated(mat, 1);
  mat->factortype = MAT_FACTOR_NONE;
  if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
  PetscUseTypeMethod(mat, setunfactored);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*MC
    MatDenseGetArrayF90 - Accesses a matrix array from Fortran

    Synopsis:
    MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

    Not Collective

    Input Parameter:
.   x - matrix

    Output Parameters:
+   xx_v - the Fortran pointer to the array
-   ierr - error code

    Example of Usage:
.vb
      PetscScalar, pointer xx_v(:,:)
      ....
      call MatDenseGetArrayF90(x,xx_v,ierr)
      a = xx_v(3)
      call MatDenseRestoreArrayF90(x,xx_v,ierr)
.ve

    Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatDenseRestoreArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJGetArrayF90()`
M*/

/*MC
    MatDenseRestoreArrayF90 - Restores a matrix array that has been
    accessed with `MatDenseGetArrayF90()`.

    Synopsis:
    MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

    Not Collective

    Input Parameters:
+   x - matrix
-   xx_v - the Fortran90 pointer to the array

    Output Parameter:
.   ierr - error code

    Example of Usage:
.vb
       PetscScalar, pointer xx_v(:,:)
       ....
       call MatDenseGetArrayF90(x,xx_v,ierr)
       a = xx_v(3)
       call MatDenseRestoreArrayF90(x,xx_v,ierr)
.ve

    Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatDenseGetArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJRestoreArrayF90()`
M*/

/*MC
    MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran.

    Synopsis:
    MatSeqAIJGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

    Not Collective

    Input Parameter:
.   x - matrix

    Output Parameters:
+   xx_v - the Fortran pointer to the array
-   ierr - error code

    Example of Usage:
.vb
      PetscScalar, pointer xx_v(:)
      ....
      call MatSeqAIJGetArrayF90(x,xx_v,ierr)
      a = xx_v(3)
      call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
.ve

    Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseGetArrayF90()`
M*/

/*MC
    MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been
    accessed with `MatSeqAIJGetArrayF90()`.

    Synopsis:
    MatSeqAIJRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

    Not Collective

    Input Parameters:
+   x - matrix
-   xx_v - the Fortran90 pointer to the array

    Output Parameter:
.   ierr - error code

    Example of Usage:
.vb
       PetscScalar, pointer xx_v(:)
       ....
       call MatSeqAIJGetArrayF90(x,xx_v,ierr)
       a = xx_v(3)
       call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
.ve

    Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseRestoreArrayF90()`
M*/

/*@
  MatCreateSubMatrix - Gets a single submatrix on the same number of processors
  as the original matrix.

  Collective

  Input Parameters:
+ mat   - the original matrix
. isrow - parallel `IS` containing the rows this processor should obtain
. iscol - parallel `IS` containing all columns you wish to keep. Each process should list the columns that will be in IT's "diagonal part" in the new matrix.
- cll   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

  Output Parameter:
. newmat - the new submatrix, of the same type as the original matrix

  Level: advanced

  Notes:
  The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.

  Some matrix types place restrictions on the row and column indices, such
  as that they be sorted or that they be equal to each other. For `MATBAIJ` and `MATSBAIJ` matrices the indices must include all rows/columns of a block;
  for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.

  The index sets may not have duplicate entries.

  The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
  the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
  to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
  will reuse the matrix generated the first time.  You should call `MatDestroy()` on `newmat` when
  you are finished using it.

  The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
  the input matrix.

  If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).

  If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
  is used by `PCFIELDSPLIT` to allow easy nesting of its use.

  Example usage:
  Consider the following 8x8 matrix with 34 non-zero values, that is
  assembled across 3 processors. Let's assume that proc0 owns 3 rows,
  proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
  as follows
.vb
            1  2  0  |  0  3  0  |  0  4
    Proc0   0  5  6  |  7  0  0  |  8  0
            9  0 10  | 11  0  0  | 12  0
    -------------------------------------
           13  0 14  | 15 16 17  |  0  0
    Proc1   0 18  0  | 19 20 21  |  0  0
            0  0  0  | 22 23  0  | 24  0
    -------------------------------------
    Proc2  25 26 27  |  0  0 28  | 29  0
           30  0  0  | 31 32 33  |  0 34
.ve

  Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6].  The resulting submatrix is

.vb
            2  0  |  0  3  0  |  0
    Proc0   5  6  |  7  0  0  |  8
    -------------------------------
    Proc1  18  0  | 19 20 21  |  0
    -------------------------------
    Proc2  26 27  |  0  0 28  | 29
            0  0  | 31 32 33  |  0
.ve

.seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
@*/
PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
{
  PetscMPIInt size;
  Mat        *local;
  IS          iscoltmp;
  PetscBool   flg;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(isrow, IS_CLASSID, 2);
  if (iscol) PetscValidHeaderSpecific(iscol, IS_CLASSID, 3);
  PetscAssertPointer(newmat, 5);
  if (cll == MAT_REUSE_MATRIX) PetscValidHeaderSpecific(*newmat, MAT_CLASSID, 5);
  PetscValidType(mat, 1);
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");

  MatCheckPreallocated(mat, 1);
  PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));

  if (!iscol || isrow == iscol) {
    PetscBool   stride;
    PetscMPIInt grabentirematrix = 0, grab;
    PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
    if (stride) {
      PetscInt first, step, n, rstart, rend;
      PetscCall(ISStrideGetInfo(isrow, &first, &step));
      if (step == 1) {
        PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
        if (rstart == first) {
          PetscCall(ISGetLocalSize(isrow, &n));
          if (n == rend - rstart) grabentirematrix = 1;
        }
      }
    }
    PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
    if (grab) {
      PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
      if (cll == MAT_INITIAL_MATRIX) {
        *newmat = mat;
        PetscCall(PetscObjectReference((PetscObject)mat));
      }
      PetscFunctionReturn(PETSC_SUCCESS);
    }
  }

  if (!iscol) {
    PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
  } else {
    iscoltmp = iscol;
  }

  /* if original matrix is on just one processor then use submatrix generated */
  if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
    PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
    goto setproperties;
  } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
    PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
    *newmat = *local;
    PetscCall(PetscFree(local));
    goto setproperties;
  } else if (!mat->ops->createsubmatrix) {
    /* Create a new matrix type that implements the operation using the full matrix */
    PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
    switch (cll) {
    case MAT_INITIAL_MATRIX:
      PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
      break;
    case MAT_REUSE_MATRIX:
      PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
      break;
    default:
      SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
    }
    PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
    goto setproperties;
  }

  PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
  PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));

setproperties:
  if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
    PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
    if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
  }
  if (!iscol) PetscCall(ISDestroy(&iscoltmp));
  if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix

  Not Collective

  Input Parameters:
+ A - the matrix we wish to propagate options from
- B - the matrix we wish to propagate options to

  Level: beginner

  Note:
  Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`

.seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
@*/
PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(B, MAT_CLASSID, 2);
  B->symmetry_eternal            = A->symmetry_eternal;
  B->structural_symmetry_eternal = A->structural_symmetry_eternal;
  B->symmetric                   = A->symmetric;
  B->structurally_symmetric      = A->structurally_symmetric;
  B->spd                         = A->spd;
  B->hermitian                   = A->hermitian;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatStashSetInitialSize - sets the sizes of the matrix stash, that is
  used during the assembly process to store values that belong to
  other processors.

  Not Collective

  Input Parameters:
+ mat   - the matrix
. size  - the initial size of the stash.
- bsize - the initial size of the block-stash(if used).

  Options Database Keys:
+ -matstash_initial_size <size> or <size0,size1,...sizep-1>            - set initial size
- -matstash_block_initial_size <bsize>  or <bsize0,bsize1,...bsizep-1> - set initial block size

  Level: intermediate

  Notes:
  The block-stash is used for values set with `MatSetValuesBlocked()` while
  the stash is used for values set with `MatSetValues()`

  Run with the option -info and look for output of the form
  MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
  to determine the appropriate value, MM, to use for size and
  MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
  to determine the value, BMM to use for bsize

.seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
@*/
PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
  PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
  the matrix

  Neighbor-wise Collective

  Input Parameters:
+ A - the matrix
. x - the vector to be multiplied by the interpolation operator
- y - the vector to be added to the result

  Output Parameter:
. w - the resulting vector

  Level: intermediate

  Notes:
  `w` may be the same vector as `y`.

  This allows one to use either the restriction or interpolation (its transpose)
  matrix to do the interpolation

.seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
@*/
PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
{
  PetscInt M, N, Ny;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(y, VEC_CLASSID, 3);
  PetscValidHeaderSpecific(w, VEC_CLASSID, 4);
  PetscCall(MatGetSize(A, &M, &N));
  PetscCall(VecGetSize(y, &Ny));
  if (M == Ny) {
    PetscCall(MatMultAdd(A, x, y, w));
  } else {
    PetscCall(MatMultTransposeAdd(A, x, y, w));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
  the matrix

  Neighbor-wise Collective

  Input Parameters:
+ A - the matrix
- x - the vector to be interpolated

  Output Parameter:
. y - the resulting vector

  Level: intermediate

  Note:
  This allows one to use either the restriction or interpolation (its transpose)
  matrix to do the interpolation

.seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
@*/
PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
{
  PetscInt M, N, Ny;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(y, VEC_CLASSID, 3);
  PetscCall(MatGetSize(A, &M, &N));
  PetscCall(VecGetSize(y, &Ny));
  if (M == Ny) {
    PetscCall(MatMult(A, x, y));
  } else {
    PetscCall(MatMultTranspose(A, x, y));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatRestrict - $y = A*x$ or $A^T*x$

  Neighbor-wise Collective

  Input Parameters:
+ A - the matrix
- x - the vector to be restricted

  Output Parameter:
. y - the resulting vector

  Level: intermediate

  Note:
  This allows one to use either the restriction or interpolation (its transpose)
  matrix to do the restriction

.seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
@*/
PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
{
  PetscInt M, N, Nx;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(x, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(y, VEC_CLASSID, 3);
  PetscCall(MatGetSize(A, &M, &N));
  PetscCall(VecGetSize(x, &Nx));
  if (M == Nx) {
    PetscCall(MatMultTranspose(A, x, y));
  } else {
    PetscCall(MatMult(A, x, y));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`

  Neighbor-wise Collective

  Input Parameters:
+ A - the matrix
. x - the input dense matrix to be multiplied
- w - the input dense matrix to be added to the result

  Output Parameter:
. y - the output dense matrix

  Level: intermediate

  Note:
  This allows one to use either the restriction or interpolation (its transpose)
  matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
  otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

.seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
@*/
PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
{
  PetscInt  M, N, Mx, Nx, Mo, My = 0, Ny = 0;
  PetscBool trans = PETSC_TRUE;
  MatReuse  reuse = MAT_INITIAL_MATRIX;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(x, MAT_CLASSID, 2);
  PetscValidType(x, 2);
  if (w) PetscValidHeaderSpecific(w, MAT_CLASSID, 3);
  if (*y) PetscValidHeaderSpecific(*y, MAT_CLASSID, 4);
  PetscCall(MatGetSize(A, &M, &N));
  PetscCall(MatGetSize(x, &Mx, &Nx));
  if (N == Mx) trans = PETSC_FALSE;
  else PetscCheck(M == Mx, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Size mismatch: A %" PetscInt_FMT "x%" PetscInt_FMT ", X %" PetscInt_FMT "x%" PetscInt_FMT, M, N, Mx, Nx);
  Mo = trans ? N : M;
  if (*y) {
    PetscCall(MatGetSize(*y, &My, &Ny));
    if (Mo == My && Nx == Ny) {
      reuse = MAT_REUSE_MATRIX;
    } else {
      PetscCheck(w || *y != w, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot reuse y and w, size mismatch: A %" PetscInt_FMT "x%" PetscInt_FMT ", X %" PetscInt_FMT "x%" PetscInt_FMT ", Y %" PetscInt_FMT "x%" PetscInt_FMT, M, N, Mx, Nx, My, Ny);
      PetscCall(MatDestroy(y));
    }
  }

  if (w && *y == w) { /* this is to minimize changes in PCMG */
    PetscBool flg;

    PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
    if (w) {
      PetscInt My, Ny, Mw, Nw;

      PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
      PetscCall(MatGetSize(*y, &My, &Ny));
      PetscCall(MatGetSize(w, &Mw, &Nw));
      if (!flg || My != Mw || Ny != Nw) w = NULL;
    }
    if (!w) {
      PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
      PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
      PetscCall(PetscObjectDereference((PetscObject)w));
    } else {
      PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
    }
  }
  if (!trans) {
    PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
  } else {
    PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
  }
  if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`

  Neighbor-wise Collective

  Input Parameters:
+ A - the matrix
- x - the input dense matrix

  Output Parameter:
. y - the output dense matrix

  Level: intermediate

  Note:
  This allows one to use either the restriction or interpolation (its transpose)
  matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
  otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

.seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
@*/
PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
{
  PetscFunctionBegin;
  PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`

  Neighbor-wise Collective

  Input Parameters:
+ A - the matrix
- x - the input dense matrix

  Output Parameter:
. y - the output dense matrix

  Level: intermediate

  Note:
  This allows one to use either the restriction or interpolation (its transpose)
  matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
  otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

.seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
@*/
PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
{
  PetscFunctionBegin;
  PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetNullSpace - retrieves the null space of a matrix.

  Logically Collective

  Input Parameters:
+ mat    - the matrix
- nullsp - the null space object

  Level: developer

.seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
@*/
PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscAssertPointer(nullsp, 2);
  *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices

  Logically Collective

  Input Parameters:
+ n   - the number of matrices
- mat - the array of matrices

  Output Parameters:
. nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`

  Level: developer

  Note:
  Call `MatRestoreNullspaces()` to provide these to another array of matrices

.seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
          `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
@*/
PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
{
  PetscFunctionBegin;
  PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
  PetscAssertPointer(mat, 2);
  PetscAssertPointer(nullsp, 3);

  PetscCall(PetscCalloc1(3 * n, nullsp));
  for (PetscInt i = 0; i < n; i++) {
    PetscValidHeaderSpecific(mat[i], MAT_CLASSID, 2);
    (*nullsp)[i] = mat[i]->nullsp;
    PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
    (*nullsp)[n + i] = mat[i]->nearnullsp;
    PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
    (*nullsp)[2 * n + i] = mat[i]->transnullsp;
    PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices

  Logically Collective

  Input Parameters:
+ n      - the number of matrices
. mat    - the array of matrices
- nullsp - an array of null spaces

  Level: developer

  Note:
  Call `MatGetNullSpaces()` to create `nullsp`

.seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
          `MatNullSpaceRemove()`, `MatGetNullSpaces()`
@*/
PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
{
  PetscFunctionBegin;
  PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
  PetscAssertPointer(mat, 2);
  PetscAssertPointer(nullsp, 3);
  PetscAssertPointer(*nullsp, 3);

  for (PetscInt i = 0; i < n; i++) {
    PetscValidHeaderSpecific(mat[i], MAT_CLASSID, 2);
    PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
    PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
    PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
    PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
    PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
    PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
  }
  PetscCall(PetscFree(*nullsp));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetNullSpace - attaches a null space to a matrix.

  Logically Collective

  Input Parameters:
+ mat    - the matrix
- nullsp - the null space object

  Level: advanced

  Notes:
  This null space is used by the `KSP` linear solvers to solve singular systems.

  Overwrites any previous null space that may have been attached. You can remove the null space from the matrix object by calling this routine with an nullsp of `NULL`

  For inconsistent singular systems (linear systems where the right-hand side is not in the range of the operator) the `KSP` residuals will not converge to
  to zero but the linear system will still be solved in a least squares sense.

  The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
  the domain of a matrix A (from $R^n$ to $R^m$ (m rows, n columns) $R^n$ = the direct sum of the null space of A, n(A), + the range of $A^T$, $R(A^T)$.
  Similarly $R^m$ = direct sum n($A^T$) + R(A).  Hence the linear system $A x = b$ has a solution only if b in R(A) (or correspondingly b is orthogonal to
  n($A^T$)) and if x is a solution then x + alpha n(A) is a solution for any alpha. The minimum norm solution is orthogonal to n(A). For problems without a solution
  the solution that minimizes the norm of the residual (the least squares solution) can be obtained by solving A x = \hat{b} where \hat{b} is b orthogonalized to the n($A^T$).
  This  \hat{b} can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.

  If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one as called
  `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
  routine also automatically calls `MatSetTransposeNullSpace()`.

  The user should call `MatNullSpaceDestroy()`.

.seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
          `KSPSetPCSide()`
@*/
PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (nullsp) PetscValidHeaderSpecific(nullsp, MAT_NULLSPACE_CLASSID, 2);
  if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
  PetscCall(MatNullSpaceDestroy(&mat->nullsp));
  mat->nullsp = nullsp;
  if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.

  Logically Collective

  Input Parameters:
+ mat    - the matrix
- nullsp - the null space object

  Level: developer

.seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
@*/
PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(nullsp, 2);
  *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix

  Logically Collective

  Input Parameters:
+ mat    - the matrix
- nullsp - the null space object

  Level: advanced

  Notes:
  This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.

  See `MatSetNullSpace()`

.seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
@*/
PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (nullsp) PetscValidHeaderSpecific(nullsp, MAT_NULLSPACE_CLASSID, 2);
  if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
  PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
  mat->transnullsp = nullsp;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
  This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.

  Logically Collective

  Input Parameters:
+ mat    - the matrix
- nullsp - the null space object

  Level: advanced

  Notes:
  Overwrites any previous near null space that may have been attached

  You can remove the null space by calling this routine with an `nullsp` of `NULL`

.seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
@*/
PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (nullsp) PetscValidHeaderSpecific(nullsp, MAT_NULLSPACE_CLASSID, 2);
  MatCheckPreallocated(mat, 1);
  if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
  PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
  mat->nearnullsp = nullsp;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`

  Not Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. nullsp - the null space object, `NULL` if not set

  Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
@*/
PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(nullsp, 2);
  MatCheckPreallocated(mat, 1);
  *nullsp = mat->nearnullsp;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.

  Collective

  Input Parameters:
+ mat  - the matrix
. row  - row/column permutation
- info - information on desired factorization process

  Level: developer

  Notes:
  Probably really in-place only when level of fill is zero, otherwise allocates
  new space to store factored matrix and deletes previous memory.

  Most users should employ the `KSP` interface for linear solvers
  instead of working directly with matrix algebra routines such as this.
  See, e.g., `KSPCreate()`.

  Developer Note:
  The Fortran interface is not autogenerated as the
  interface definition cannot be generated correctly [due to `MatFactorInfo`]

.seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
@*/
PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (row) PetscValidHeaderSpecific(row, IS_CLASSID, 2);
  PetscAssertPointer(info, 3);
  PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);
  PetscUseTypeMethod(mat, iccfactor, row, info);
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
  ghosted ones.

  Not Collective

  Input Parameters:
+ mat  - the matrix
- diag - the diagonal values, including ghost ones

  Level: developer

  Notes:
  Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices

  This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`

.seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
@*/
PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
{
  PetscMPIInt size;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(diag, VEC_CLASSID, 2);
  PetscValidType(mat, 1);

  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
  PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
  PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
  if (size == 1) {
    PetscInt n, m;
    PetscCall(VecGetSize(diag, &n));
    PetscCall(MatGetSize(mat, NULL, &m));
    PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
    PetscCall(MatDiagonalScale(mat, NULL, diag));
  } else {
    PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
  }
  PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
  PetscCall(PetscObjectStateIncrease((PetscObject)mat));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetInertia - Gets the inertia from a factored matrix

  Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ nneg  - number of negative eigenvalues
. nzero - number of zero eigenvalues
- npos  - number of positive eigenvalues

  Level: advanced

  Note:
  Matrix must have been factored by `MatCholeskyFactor()`

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
@*/
PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
  PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors

  Neighbor-wise Collective

  Input Parameters:
+ mat - the factored matrix obtained with `MatGetFactor()`
- b   - the right-hand-side vectors

  Output Parameter:
. x - the result vectors

  Level: developer

  Note:
  The vectors `b` and `x` cannot be the same.  I.e., one cannot
  call `MatSolves`(A,x,x).

.seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
@*/
PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
  PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
  if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);

  MatCheckPreallocated(mat, 1);
  PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, solves, b, x);
  PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatIsSymmetric - Test whether a matrix is symmetric

  Collective

  Input Parameters:
+ A   - the matrix to test
- tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)

  Output Parameter:
. flg - the result

  Level: intermediate

  Notes:
  For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

  If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`

  One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
  after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

.seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
          `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
@*/
PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscAssertPointer(flg, 3);
  if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
  else {
    if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
    else PetscCall(MatIsTranspose(A, A, tol, flg));
    if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatIsHermitian - Test whether a matrix is Hermitian

  Collective

  Input Parameters:
+ A   - the matrix to test
- tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)

  Output Parameter:
. flg - the result

  Level: intermediate

  Notes:
  For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

  If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`

  One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
  after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)

.seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
          `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
@*/
PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscAssertPointer(flg, 3);
  if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
  else {
    if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
    else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
    if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state

  Not Collective

  Input Parameter:
. A - the matrix to check

  Output Parameters:
+ set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
- flg - the result (only valid if set is `PETSC_TRUE`)

  Level: advanced

  Notes:
  Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
  if you want it explicitly checked

  One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
  after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

.seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
@*/
PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscAssertPointer(set, 2);
  PetscAssertPointer(flg, 3);
  if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
    *set = PETSC_TRUE;
    *flg = PetscBool3ToBool(A->symmetric);
  } else {
    *set = PETSC_FALSE;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state

  Not Collective

  Input Parameter:
. A - the matrix to check

  Output Parameters:
+ set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
- flg - the result (only valid if set is `PETSC_TRUE`)

  Level: advanced

  Notes:
  Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).

  One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
  after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)

.seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
@*/
PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscAssertPointer(set, 2);
  PetscAssertPointer(flg, 3);
  if (A->spd != PETSC_BOOL3_UNKNOWN) {
    *set = PETSC_TRUE;
    *flg = PetscBool3ToBool(A->spd);
  } else {
    *set = PETSC_FALSE;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state

  Not Collective

  Input Parameter:
. A - the matrix to check

  Output Parameters:
+ set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
- flg - the result (only valid if set is `PETSC_TRUE`)

  Level: advanced

  Notes:
  Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
  if you want it explicitly checked

  One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
  after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

.seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
@*/
PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscAssertPointer(set, 2);
  PetscAssertPointer(flg, 3);
  if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
    *set = PETSC_TRUE;
    *flg = PetscBool3ToBool(A->hermitian);
  } else {
    *set = PETSC_FALSE;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric

  Collective

  Input Parameter:
. A - the matrix to test

  Output Parameter:
. flg - the result

  Level: intermediate

  Notes:
  If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`

  One can declare that a matrix is structurally symmetric with `MatSetOption`(mat,`MAT_STRUCTURALLY_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain structurally
  symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

.seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
@*/
PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscAssertPointer(flg, 2);
  if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
    *flg = PetscBool3ToBool(A->structurally_symmetric);
  } else {
    PetscUseTypeMethod(A, isstructurallysymmetric, flg);
    PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state

  Not Collective

  Input Parameter:
. A - the matrix to check

  Output Parameters:
+ set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
- flg - the result (only valid if set is PETSC_TRUE)

  Level: advanced

  Notes:
  One can declare that a matrix is structurally symmetric with `MatSetOption`(mat,`MAT_STRUCTURALLY_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain structurally
  symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

  Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)

.seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
@*/
PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscAssertPointer(set, 2);
  PetscAssertPointer(flg, 3);
  if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
    *set = PETSC_TRUE;
    *flg = PetscBool3ToBool(A->structurally_symmetric);
  } else {
    *set = PETSC_FALSE;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
  to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process

  Not Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ nstash    - the size of the stash
. reallocs  - the number of additional mallocs incurred.
. bnstash   - the size of the block stash
- breallocs - the number of additional mallocs incurred.in the block stash

  Level: advanced

.seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
@*/
PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
{
  PetscFunctionBegin;
  PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
  PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
  parallel layout, `PetscLayout` for rows and columns

  Collective

  Input Parameter:
. mat - the matrix

  Output Parameters:
+ right - (optional) vector that the matrix can be multiplied against
- left  - (optional) vector that the matrix vector product can be stored in

  Level: advanced

  Notes:
  The blocksize of the returned vectors is determined by the row and column block sizes set with `MatSetBlockSizes()` or the single blocksize (same for both) set by `MatSetBlockSize()`.

  These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed

.seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
@*/
PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  if (mat->ops->getvecs) {
    PetscUseTypeMethod(mat, getvecs, right, left);
  } else {
    if (right) {
      PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
      PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
      PetscCall(VecSetType(*right, mat->defaultvectype));
#if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
      if (mat->boundtocpu && mat->bindingpropagates) {
        PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
        PetscCall(VecBindToCPU(*right, PETSC_TRUE));
      }
#endif
    }
    if (left) {
      PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
      PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
      PetscCall(VecSetType(*left, mat->defaultvectype));
#if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
      if (mat->boundtocpu && mat->bindingpropagates) {
        PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
        PetscCall(VecBindToCPU(*left, PETSC_TRUE));
      }
#endif
    }
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
  with default values.

  Not Collective

  Input Parameter:
. info - the `MatFactorInfo` data structure

  Level: developer

  Notes:
  The solvers are generally used through the `KSP` and `PC` objects, for example
  `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`

  Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
@*/
PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
{
  PetscFunctionBegin;
  PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed

  Collective

  Input Parameters:
+ mat - the factored matrix
- is  - the index set defining the Schur indices (0-based)

  Level: advanced

  Notes:
  Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.

  You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.

  This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
          `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
@*/
PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
{
  PetscErrorCode (*f)(Mat, IS);

  PetscFunctionBegin;
  PetscValidType(mat, 1);
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(is, 2);
  PetscValidHeaderSpecific(is, IS_CLASSID, 2);
  PetscCheckSameComm(mat, 1, is, 2);
  PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
  PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
  PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
  PetscCall(MatDestroy(&mat->schur));
  PetscCall((*f)(mat, is));
  PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step

  Logically Collective

  Input Parameters:
+ F      - the factored matrix obtained by calling `MatGetFactor()`
. S      - location where to return the Schur complement, can be `NULL`
- status - the status of the Schur complement matrix, can be `NULL`

  Level: advanced

  Notes:
  You must call `MatFactorSetSchurIS()` before calling this routine.

  This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

  The routine provides a copy of the Schur matrix stored within the solver data structures.
  The caller must destroy the object when it is no longer needed.
  If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.

  Use `MatFactorGetSchurComplement()` to get access to the Schur complement matrix inside the factored matrix instead of making a copy of it (which this function does)

  See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

  Developer Note:
  The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
  matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
@*/
PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(F, MAT_CLASSID, 1);
  if (S) PetscAssertPointer(S, 2);
  if (status) PetscAssertPointer(status, 3);
  if (S) {
    PetscErrorCode (*f)(Mat, Mat *);

    PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
    if (f) {
      PetscCall((*f)(F, S));
    } else {
      PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
    }
  }
  if (status) *status = F->schur_status;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix

  Logically Collective

  Input Parameters:
+ F      - the factored matrix obtained by calling `MatGetFactor()`
. S      - location where to return the Schur complement, can be `NULL`
- status - the status of the Schur complement matrix, can be `NULL`

  Level: advanced

  Notes:
  You must call `MatFactorSetSchurIS()` before calling this routine.

  Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`

  The routine returns a the Schur Complement stored within the data structures of the solver.

  If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.

  The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.

  Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix

  See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
@*/
PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(F, MAT_CLASSID, 1);
  if (S) {
    PetscAssertPointer(S, 2);
    *S = F->schur;
  }
  if (status) {
    PetscAssertPointer(status, 3);
    *status = F->schur_status;
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
{
  Mat S = F->schur;

  PetscFunctionBegin;
  switch (F->schur_status) {
  case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
  case MAT_FACTOR_SCHUR_INVERTED:
    if (S) {
      S->ops->solve             = NULL;
      S->ops->matsolve          = NULL;
      S->ops->solvetranspose    = NULL;
      S->ops->matsolvetranspose = NULL;
      S->ops->solveadd          = NULL;
      S->ops->solvetransposeadd = NULL;
      S->factortype             = MAT_FACTOR_NONE;
      PetscCall(PetscFree(S->solvertype));
    }
  case MAT_FACTOR_SCHUR_FACTORED: // fall-through
    break;
  default:
    SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`

  Logically Collective

  Input Parameters:
+ F      - the factored matrix obtained by calling `MatGetFactor()`
. S      - location where the Schur complement is stored
- status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)

  Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
@*/
PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(F, MAT_CLASSID, 1);
  if (S) {
    PetscValidHeaderSpecific(*S, MAT_CLASSID, 2);
    *S = NULL;
  }
  F->schur_status = status;
  PetscCall(MatFactorUpdateSchurStatus_Private(F));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step

  Logically Collective

  Input Parameters:
+ F   - the factored matrix obtained by calling `MatGetFactor()`
. rhs - location where the right-hand side of the Schur complement system is stored
- sol - location where the solution of the Schur complement system has to be returned

  Level: advanced

  Notes:
  The sizes of the vectors should match the size of the Schur complement

  Must be called after `MatFactorSetSchurIS()`

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
@*/
PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
{
  PetscFunctionBegin;
  PetscValidType(F, 1);
  PetscValidType(rhs, 2);
  PetscValidType(sol, 3);
  PetscValidHeaderSpecific(F, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(rhs, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(sol, VEC_CLASSID, 3);
  PetscCheckSameComm(F, 1, rhs, 2);
  PetscCheckSameComm(F, 1, sol, 3);
  PetscCall(MatFactorFactorizeSchurComplement(F));
  switch (F->schur_status) {
  case MAT_FACTOR_SCHUR_FACTORED:
    PetscCall(MatSolveTranspose(F->schur, rhs, sol));
    break;
  case MAT_FACTOR_SCHUR_INVERTED:
    PetscCall(MatMultTranspose(F->schur, rhs, sol));
    break;
  default:
    SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step

  Logically Collective

  Input Parameters:
+ F   - the factored matrix obtained by calling `MatGetFactor()`
. rhs - location where the right-hand side of the Schur complement system is stored
- sol - location where the solution of the Schur complement system has to be returned

  Level: advanced

  Notes:
  The sizes of the vectors should match the size of the Schur complement

  Must be called after `MatFactorSetSchurIS()`

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
@*/
PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
{
  PetscFunctionBegin;
  PetscValidType(F, 1);
  PetscValidType(rhs, 2);
  PetscValidType(sol, 3);
  PetscValidHeaderSpecific(F, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(rhs, VEC_CLASSID, 2);
  PetscValidHeaderSpecific(sol, VEC_CLASSID, 3);
  PetscCheckSameComm(F, 1, rhs, 2);
  PetscCheckSameComm(F, 1, sol, 3);
  PetscCall(MatFactorFactorizeSchurComplement(F));
  switch (F->schur_status) {
  case MAT_FACTOR_SCHUR_FACTORED:
    PetscCall(MatSolve(F->schur, rhs, sol));
    break;
  case MAT_FACTOR_SCHUR_INVERTED:
    PetscCall(MatMult(F->schur, rhs, sol));
    break;
  default:
    SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

PETSC_EXTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
#if PetscDefined(HAVE_CUDA)
PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
#endif

/* Schur status updated in the interface */
static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
{
  Mat S = F->schur;

  PetscFunctionBegin;
  if (S) {
    PetscMPIInt size;
    PetscBool   isdense, isdensecuda;

    PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
    PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
    PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
    PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
    PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
    PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
    if (isdense) {
      PetscCall(MatSeqDenseInvertFactors_Private(S));
    } else if (isdensecuda) {
#if defined(PETSC_HAVE_CUDA)
      PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
#endif
    }
    // HIP??????????????
    PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step

  Logically Collective

  Input Parameter:
. F - the factored matrix obtained by calling `MatGetFactor()`

  Level: advanced

  Notes:
  Must be called after `MatFactorSetSchurIS()`.

  Call `MatFactorGetSchurComplement()` or  `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
@*/
PetscErrorCode MatFactorInvertSchurComplement(Mat F)
{
  PetscFunctionBegin;
  PetscValidType(F, 1);
  PetscValidHeaderSpecific(F, MAT_CLASSID, 1);
  if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
  PetscCall(MatFactorFactorizeSchurComplement(F));
  PetscCall(MatFactorInvertSchurComplement_Private(F));
  F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step

  Logically Collective

  Input Parameter:
. F - the factored matrix obtained by calling `MatGetFactor()`

  Level: advanced

  Note:
  Must be called after `MatFactorSetSchurIS()`

.seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
@*/
PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
{
  MatFactorInfo info;

  PetscFunctionBegin;
  PetscValidType(F, 1);
  PetscValidHeaderSpecific(F, MAT_CLASSID, 1);
  if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
  PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
  PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
  if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
    PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
  } else {
    PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
  }
  PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
  F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatPtAP - Creates the matrix product $C = P^T * A * P$

  Neighbor-wise Collective

  Input Parameters:
+ A     - the matrix
. P     - the projection matrix
. scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
- fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
          if the result is a dense matrix this is irrelevant

  Output Parameter:
. C - the product matrix

  Level: intermediate

  Notes:
  C will be created and must be destroyed by the user with `MatDestroy()`.

  An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

  The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

  Developer Note:
  For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.

.seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
@*/
PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
{
  PetscFunctionBegin;
  if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
  PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

  if (scall == MAT_INITIAL_MATRIX) {
    PetscCall(MatProductCreate(A, P, NULL, C));
    PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
    PetscCall(MatProductSetAlgorithm(*C, "default"));
    PetscCall(MatProductSetFill(*C, fill));

    (*C)->product->api_user = PETSC_TRUE;
    PetscCall(MatProductSetFromOptions(*C));
    PetscCheck((*C)->ops->productsymbolic, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "MatProduct %s not supported for A %s and P %s", MatProductTypes[MATPRODUCT_PtAP], ((PetscObject)A)->type_name, ((PetscObject)P)->type_name);
    PetscCall(MatProductSymbolic(*C));
  } else { /* scall == MAT_REUSE_MATRIX */
    PetscCall(MatProductReplaceMats(A, P, NULL, *C));
  }

  PetscCall(MatProductNumeric(*C));
  (*C)->symmetric = A->symmetric;
  (*C)->spd       = A->spd;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatRARt - Creates the matrix product $C = R * A * R^T$

  Neighbor-wise Collective

  Input Parameters:
+ A     - the matrix
. R     - the projection matrix
. scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
- fill  - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
          if the result is a dense matrix this is irrelevant

  Output Parameter:
. C - the product matrix

  Level: intermediate

  Notes:
  `C` will be created and must be destroyed by the user with `MatDestroy()`.

  An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

  This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
  which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
  parallel `MatRARt()` is implemented via explicit transpose of `R`, which could be very expensive.
  We recommend using `MatPtAP()`.

  The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

.seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
@*/
PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
{
  PetscFunctionBegin;
  if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
  PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

  if (scall == MAT_INITIAL_MATRIX) {
    PetscCall(MatProductCreate(A, R, NULL, C));
    PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
    PetscCall(MatProductSetAlgorithm(*C, "default"));
    PetscCall(MatProductSetFill(*C, fill));

    (*C)->product->api_user = PETSC_TRUE;
    PetscCall(MatProductSetFromOptions(*C));
    PetscCheck((*C)->ops->productsymbolic, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "MatProduct %s not supported for A %s and R %s", MatProductTypes[MATPRODUCT_RARt], ((PetscObject)A)->type_name, ((PetscObject)R)->type_name);
    PetscCall(MatProductSymbolic(*C));
  } else { /* scall == MAT_REUSE_MATRIX */
    PetscCall(MatProductReplaceMats(A, R, NULL, *C));
  }

  PetscCall(MatProductNumeric(*C));
  if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
{
  PetscBool flg = PETSC_TRUE;

  PetscFunctionBegin;
  PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
  if (scall == MAT_INITIAL_MATRIX) {
    PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
    PetscCall(MatProductCreate(A, B, NULL, C));
    PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
    PetscCall(MatProductSetFill(*C, fill));
  } else { /* scall == MAT_REUSE_MATRIX */
    Mat_Product *product = (*C)->product;

    PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
    if (flg && product && product->type != ptype) {
      PetscCall(MatProductClear(*C));
      product = NULL;
    }
    PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
    if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
      PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
      PetscCall(MatProductCreate_Private(A, B, NULL, *C));
      product        = (*C)->product;
      product->fill  = fill;
      product->clear = PETSC_TRUE;
    } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
      flg = PETSC_FALSE;
      PetscCall(MatProductReplaceMats(A, B, NULL, *C));
    }
  }
  if (flg) {
    (*C)->product->api_user = PETSC_TRUE;
    PetscCall(MatProductSetType(*C, ptype));
    PetscCall(MatProductSetFromOptions(*C));
    PetscCall(MatProductSymbolic(*C));
  }
  PetscCall(MatProductNumeric(*C));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMatMult - Performs matrix-matrix multiplication C=A*B.

  Neighbor-wise Collective

  Input Parameters:
+ A     - the left matrix
. B     - the right matrix
. scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
- fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
          if the result is a dense matrix this is irrelevant

  Output Parameter:
. C - the product matrix

  Notes:
  Unless scall is `MAT_REUSE_MATRIX` C will be created.

  `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call and C was obtained from a previous
  call to this function with `MAT_INITIAL_MATRIX`.

  To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.

  In the special case where matrix `B` (and hence `C`) are dense you can create the correctly sized matrix `C` yourself and then call this routine with `MAT_REUSE_MATRIX`,
  rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.

  The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

  Example of Usage:
.vb
     MatProductCreate(A,B,NULL,&C);
     MatProductSetType(C,MATPRODUCT_AB);
     MatProductSymbolic(C);
     MatProductNumeric(C); // compute C=A * B
     MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
     MatProductNumeric(C);
     MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
     MatProductNumeric(C);
.ve

  Level: intermediate

.seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
@*/
PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
{
  PetscFunctionBegin;
  PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.

  Neighbor-wise Collective

  Input Parameters:
+ A     - the left matrix
. B     - the right matrix
. scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
- fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known

  Output Parameter:
. C - the product matrix

  Options Database Key:
. -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
              first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
              the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.

  Level: intermediate

  Notes:
  C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

  `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call

  To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
  actually needed.

  This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
  and for pairs of `MATMPIDENSE` matrices.

  This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`

  The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

.seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
@*/
PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
{
  PetscFunctionBegin;
  PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
  if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.

  Neighbor-wise Collective

  Input Parameters:
+ A     - the left matrix
. B     - the right matrix
. scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
- fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known

  Output Parameter:
. C - the product matrix

  Level: intermediate

  Notes:
  `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

  `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.

  This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`

  To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
  actually needed.

  This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
  which inherit from `MATSEQAIJ`.  `C` will be of the same type as the input matrices.

  The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

.seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
@*/
PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
{
  PetscFunctionBegin;
  PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.

  Neighbor-wise Collective

  Input Parameters:
+ A     - the left matrix
. B     - the middle matrix
. C     - the right matrix
. scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
- fill  - expected fill as ratio of nnz(D)/(nnz(A) + nnz(B)+nnz(C)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
          if the result is a dense matrix this is irrelevant

  Output Parameter:
. D - the product matrix

  Level: intermediate

  Notes:
  Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.

  `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call

  This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`

  To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
  actually needed.

  If you have many matrices with the same non-zero structure to multiply, you
  should use `MAT_REUSE_MATRIX` in all calls but the first

  The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

.seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
@*/
PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
{
  PetscFunctionBegin;
  if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
  PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

  if (scall == MAT_INITIAL_MATRIX) {
    PetscCall(MatProductCreate(A, B, C, D));
    PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
    PetscCall(MatProductSetAlgorithm(*D, "default"));
    PetscCall(MatProductSetFill(*D, fill));

    (*D)->product->api_user = PETSC_TRUE;
    PetscCall(MatProductSetFromOptions(*D));
    PetscCheck((*D)->ops->productsymbolic, PetscObjectComm((PetscObject)*D), PETSC_ERR_SUP, "MatProduct %s not supported for A %s, B %s and C %s", MatProductTypes[MATPRODUCT_ABC], ((PetscObject)A)->type_name, ((PetscObject)B)->type_name,
               ((PetscObject)C)->type_name);
    PetscCall(MatProductSymbolic(*D));
  } else { /* user may change input matrices when REUSE */
    PetscCall(MatProductReplaceMats(A, B, C, *D));
  }
  PetscCall(MatProductNumeric(*D));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.

  Collective

  Input Parameters:
+ mat      - the matrix
. nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
. subcomm  - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
- reuse    - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

  Output Parameter:
. matredundant - redundant matrix

  Level: advanced

  Notes:
  `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
  original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.

  This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
  calling it.

  `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.

.seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
@*/
PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
{
  MPI_Comm       comm;
  PetscMPIInt    size;
  PetscInt       mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
  Mat_Redundant *redund     = NULL;
  PetscSubcomm   psubcomm   = NULL;
  MPI_Comm       subcomm_in = subcomm;
  Mat           *matseq;
  IS             isrow, iscol;
  PetscBool      newsubcomm = PETSC_FALSE;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
    PetscAssertPointer(*matredundant, 5);
    PetscValidHeaderSpecific(*matredundant, MAT_CLASSID, 5);
  }

  PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
  if (size == 1 || nsubcomm == 1) {
    if (reuse == MAT_INITIAL_MATRIX) {
      PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
    } else {
      PetscCheck(*matredundant != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
      PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
    }
    PetscFunctionReturn(PETSC_SUCCESS);
  }

  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  MatCheckPreallocated(mat, 1);

  PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
  if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
    /* create psubcomm, then get subcomm */
    PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
    PetscCallMPI(MPI_Comm_size(comm, &size));
    PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);

    PetscCall(PetscSubcommCreate(comm, &psubcomm));
    PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
    PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
    PetscCall(PetscSubcommSetFromOptions(psubcomm));
    PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
    newsubcomm = PETSC_TRUE;
    PetscCall(PetscSubcommDestroy(&psubcomm));
  }

  /* get isrow, iscol and a local sequential matrix matseq[0] */
  if (reuse == MAT_INITIAL_MATRIX) {
    mloc_sub = PETSC_DECIDE;
    nloc_sub = PETSC_DECIDE;
    if (bs < 1) {
      PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
      PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
    } else {
      PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
      PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
    }
    PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
    rstart = rend - mloc_sub;
    PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
    PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
    PetscCall(ISSetIdentity(iscol));
  } else { /* reuse == MAT_REUSE_MATRIX */
    PetscCheck(*matredundant != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
    /* retrieve subcomm */
    PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
    redund = (*matredundant)->redundant;
    isrow  = redund->isrow;
    iscol  = redund->iscol;
    matseq = redund->matseq;
  }
  PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));

  /* get matredundant over subcomm */
  if (reuse == MAT_INITIAL_MATRIX) {
    PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));

    /* create a supporting struct and attach it to C for reuse */
    PetscCall(PetscNew(&redund));
    (*matredundant)->redundant = redund;
    redund->isrow              = isrow;
    redund->iscol              = iscol;
    redund->matseq             = matseq;
    if (newsubcomm) {
      redund->subcomm = subcomm;
    } else {
      redund->subcomm = MPI_COMM_NULL;
    }
  } else {
    PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
  }
#if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
  if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
    PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
    PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
  }
#endif
  PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
  a given `Mat`. Each submatrix can span multiple procs.

  Collective

  Input Parameters:
+ mat     - the matrix
. subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
- scall   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

  Output Parameter:
. subMat - parallel sub-matrices each spanning a given `subcomm`

  Level: advanced

  Notes:
  The submatrix partition across processors is dictated by `subComm` a
  communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
  is not restricted to be grouped with consecutive original MPI processes.

  Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
  map directly to the layout of the original matrix [wrt the local
  row,col partitioning]. So the original 'DiagonalMat' naturally maps
  into the 'DiagonalMat' of the `subMat`, hence it is used directly from
  the `subMat`. However the offDiagMat looses some columns - and this is
  reconstructed with `MatSetValues()`

  This is used by `PCBJACOBI` when a single block spans multiple MPI processes.

.seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
@*/
PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
{
  PetscMPIInt commsize, subCommSize;

  PetscFunctionBegin;
  PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
  PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
  PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);

  PetscCheck(scall != MAT_REUSE_MATRIX || *subMat != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
  PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
  PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
  PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering

  Not Collective

  Input Parameters:
+ mat   - matrix to extract local submatrix from
. isrow - local row indices for submatrix
- iscol - local column indices for submatrix

  Output Parameter:
. submat - the submatrix

  Level: intermediate

  Notes:
  `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.

  Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`.  Its communicator may be
  the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.

  `submat` always implements `MatSetValuesLocal()`.  If `isrow` and `iscol` have the same block size, then
  `MatSetValuesBlockedLocal()` will also be implemented.

  `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
  Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.

.seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
@*/
PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(isrow, IS_CLASSID, 2);
  PetscValidHeaderSpecific(iscol, IS_CLASSID, 3);
  PetscCheckSameComm(isrow, 2, iscol, 3);
  PetscAssertPointer(submat, 4);
  PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");

  if (mat->ops->getlocalsubmatrix) {
    PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
  } else {
    PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`

  Not Collective

  Input Parameters:
+ mat    - matrix to extract local submatrix from
. isrow  - local row indices for submatrix
. iscol  - local column indices for submatrix
- submat - the submatrix

  Level: intermediate

.seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
@*/
PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidHeaderSpecific(isrow, IS_CLASSID, 2);
  PetscValidHeaderSpecific(iscol, IS_CLASSID, 3);
  PetscCheckSameComm(isrow, 2, iscol, 3);
  PetscAssertPointer(submat, 4);
  if (*submat) PetscValidHeaderSpecific(*submat, MAT_CLASSID, 4);

  if (mat->ops->restorelocalsubmatrix) {
    PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
  } else {
    PetscCall(MatDestroy(submat));
  }
  *submat = NULL;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix

  Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. is - if any rows have zero diagonals this contains the list of them

  Level: developer

.seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
@*/
PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

  if (!mat->ops->findzerodiagonals) {
    Vec                diag;
    const PetscScalar *a;
    PetscInt          *rows;
    PetscInt           rStart, rEnd, r, nrow = 0;

    PetscCall(MatCreateVecs(mat, &diag, NULL));
    PetscCall(MatGetDiagonal(mat, diag));
    PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
    PetscCall(VecGetArrayRead(diag, &a));
    for (r = 0; r < rEnd - rStart; ++r)
      if (a[r] == 0.0) ++nrow;
    PetscCall(PetscMalloc1(nrow, &rows));
    nrow = 0;
    for (r = 0; r < rEnd - rStart; ++r)
      if (a[r] == 0.0) rows[nrow++] = r + rStart;
    PetscCall(VecRestoreArrayRead(diag, &a));
    PetscCall(VecDestroy(&diag));
    PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
  } else {
    PetscUseTypeMethod(mat, findzerodiagonals, is);
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)

  Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. is - contains the list of rows with off block diagonal entries

  Level: developer

.seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
@*/
PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

  PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatInvertBlockDiagonal - Inverts the block diagonal entries.

  Collective; No Fortran Support

  Input Parameter:
. mat - the matrix

  Output Parameter:
. values - the block inverses in column major order (FORTRAN-like)

  Level: advanced

  Notes:
  The size of the blocks is determined by the block size of the matrix.

  The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

  The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size

.seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
@*/
PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscUseTypeMethod(mat, invertblockdiagonal, values);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.

  Collective; No Fortran Support

  Input Parameters:
+ mat     - the matrix
. nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
- bsizes  - the size of each block on the process, set with `MatSetVariableBlockSizes()`

  Output Parameter:
. values - the block inverses in column major order (FORTRAN-like)

  Level: advanced

  Notes:
  Use `MatInvertBlockDiagonal()` if all blocks have the same size

  The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

.seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
@*/
PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
  PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
  PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A

  Collective

  Input Parameters:
+ A - the matrix
- C - matrix with inverted block diagonal of `A`.  This matrix should be created and may have its type set.

  Level: advanced

  Note:
  The blocksize of the matrix is used to determine the blocks on the diagonal of `C`

.seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
@*/
PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
{
  const PetscScalar *vals;
  PetscInt          *dnnz;
  PetscInt           m, rstart, rend, bs, i, j;

  PetscFunctionBegin;
  PetscCall(MatInvertBlockDiagonal(A, &vals));
  PetscCall(MatGetBlockSize(A, &bs));
  PetscCall(MatGetLocalSize(A, &m, NULL));
  PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
  PetscCall(PetscMalloc1(m / bs, &dnnz));
  for (j = 0; j < m / bs; j++) dnnz[j] = 1;
  PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
  PetscCall(PetscFree(dnnz));
  PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
  PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
  for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
  PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
  PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
  PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
  via `MatTransposeColoringCreate()`.

  Collective

  Input Parameter:
. c - coloring context

  Level: intermediate

.seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
@*/
PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
{
  MatTransposeColoring matcolor = *c;

  PetscFunctionBegin;
  if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
  if (--((PetscObject)matcolor)->refct > 0) {
    matcolor = NULL;
    PetscFunctionReturn(PETSC_SUCCESS);
  }

  PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
  PetscCall(PetscFree(matcolor->rows));
  PetscCall(PetscFree(matcolor->den2sp));
  PetscCall(PetscFree(matcolor->colorforcol));
  PetscCall(PetscFree(matcolor->columns));
  if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
  PetscCall(PetscHeaderDestroy(c));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
  a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
  `MatTransposeColoring` to sparse `B`.

  Collective

  Input Parameters:
+ coloring - coloring context created with `MatTransposeColoringCreate()`
- B        - sparse matrix

  Output Parameter:
. Btdense - dense matrix $B^T$

  Level: developer

  Note:
  These are used internally for some implementations of `MatRARt()`

.seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
@*/
PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(coloring, MAT_TRANSPOSECOLORING_CLASSID, 1);
  PetscValidHeaderSpecific(B, MAT_CLASSID, 2);
  PetscValidHeaderSpecific(Btdense, MAT_CLASSID, 3);

  PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
  a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
  in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
  $C_{sp}$ from $C_{den}$.

  Collective

  Input Parameters:
+ matcoloring - coloring context created with `MatTransposeColoringCreate()`
- Cden        - matrix product of a sparse matrix and a dense matrix Btdense

  Output Parameter:
. Csp - sparse matrix

  Level: developer

  Note:
  These are used internally for some implementations of `MatRARt()`

.seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
@*/
PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(matcoloring, MAT_TRANSPOSECOLORING_CLASSID, 1);
  PetscValidHeaderSpecific(Cden, MAT_CLASSID, 2);
  PetscValidHeaderSpecific(Csp, MAT_CLASSID, 3);

  PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
  PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
  PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.

  Collective

  Input Parameters:
+ mat        - the matrix product C
- iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`

  Output Parameter:
. color - the new coloring context

  Level: intermediate

.seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
          `MatTransColoringApplyDenToSp()`
@*/
PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
{
  MatTransposeColoring c;
  MPI_Comm             comm;

  PetscFunctionBegin;
  PetscAssertPointer(color, 3);

  PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
  PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
  PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
  c->ctype = iscoloring->ctype;
  PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
  *color = c;
  PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
  matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.

  Not Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. state - the current state

  Level: intermediate

  Notes:
  You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
  different matrices

  Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix

  Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.

.seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
@*/
PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  *state = mat->nonzerostate;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
  matrices from each processor

  Collective

  Input Parameters:
+ comm   - the communicators the parallel matrix will live on
. seqmat - the input sequential matrices
. n      - number of local columns (or `PETSC_DECIDE`)
- reuse  - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

  Output Parameter:
. mpimat - the parallel matrix generated

  Level: developer

  Note:
  The number of columns of the matrix in EACH processor MUST be the same.

.seealso: [](ch_matrices), `Mat`
@*/
PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
{
  PetscMPIInt size;

  PetscFunctionBegin;
  PetscCallMPI(MPI_Comm_size(comm, &size));
  if (size == 1) {
    if (reuse == MAT_INITIAL_MATRIX) {
      PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
    } else {
      PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
    }
    PetscFunctionReturn(PETSC_SUCCESS);
  }

  PetscCheck(reuse != MAT_REUSE_MATRIX || seqmat != *mpimat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");

  PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
  PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
  PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.

  Collective

  Input Parameters:
+ A - the matrix to create subdomains from
- N - requested number of subdomains

  Output Parameters:
+ n   - number of subdomains resulting on this MPI process
- iss - `IS` list with indices of subdomains on this MPI process

  Level: advanced

  Note:
  The number of subdomains must be smaller than the communicator size

.seealso: [](ch_matrices), `Mat`, `IS`
@*/
PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
{
  MPI_Comm    comm, subcomm;
  PetscMPIInt size, rank, color;
  PetscInt    rstart, rend, k;

  PetscFunctionBegin;
  PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
  PetscCallMPI(MPI_Comm_size(comm, &size));
  PetscCallMPI(MPI_Comm_rank(comm, &rank));
  PetscCheck(N >= 1 && N < size, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "number of subdomains must be > 0 and < %d, got N = %" PetscInt_FMT, size, N);
  *n    = 1;
  k     = size / N + (size % N > 0); /* There are up to k ranks to a color */
  color = rank / k;
  PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
  PetscCall(PetscMalloc1(1, iss));
  PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
  PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
  PetscCallMPI(MPI_Comm_free(&subcomm));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.

  If the interpolation and restriction operators are the same, uses `MatPtAP()`.
  If they are not the same, uses `MatMatMatMult()`.

  Once the coarse grid problem is constructed, correct for interpolation operators
  that are not of full rank, which can legitimately happen in the case of non-nested
  geometric multigrid.

  Input Parameters:
+ restrct     - restriction operator
. dA          - fine grid matrix
. interpolate - interpolation operator
. reuse       - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
- fill        - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate

  Output Parameter:
. A - the Galerkin coarse matrix

  Options Database Key:
. -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used

  Level: developer

  Note:
  The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

.seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
@*/
PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
{
  IS  zerorows;
  Vec diag;

  PetscFunctionBegin;
  PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
  /* Construct the coarse grid matrix */
  if (interpolate == restrct) {
    PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
  } else {
    PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
  }

  /* If the interpolation matrix is not of full rank, A will have zero rows.
     This can legitimately happen in the case of non-nested geometric multigrid.
     In that event, we set the rows of the matrix to the rows of the identity,
     ignoring the equations (as the RHS will also be zero). */

  PetscCall(MatFindZeroRows(*A, &zerorows));

  if (zerorows != NULL) { /* if there are any zero rows */
    PetscCall(MatCreateVecs(*A, &diag, NULL));
    PetscCall(MatGetDiagonal(*A, diag));
    PetscCall(VecISSet(diag, zerorows, 1.0));
    PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
    PetscCall(VecDestroy(&diag));
    PetscCall(ISDestroy(&zerorows));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatSetOperation - Allows user to set a matrix operation for any matrix type

  Logically Collective

  Input Parameters:
+ mat - the matrix
. op  - the name of the operation
- f   - the function that provides the operation

  Level: developer

  Example Usage:
.vb
  extern PetscErrorCode usermult(Mat, Vec, Vec);

  PetscCall(MatCreateXXX(comm, ..., &A));
  PetscCall(MatSetOperation(A, MATOP_MULT, (PetscVoidFn *)usermult));
.ve

  Notes:
  See the file `include/petscmat.h` for a complete list of matrix
  operations, which all have the form MATOP_<OPERATION>, where
  <OPERATION> is the name (in all capital letters) of the
  user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

  All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
  sequence as the usual matrix interface routines, since they
  are intended to be accessed via the usual matrix interface
  routines, e.g.,
.vb
  MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
.ve

  In particular each function MUST return `PETSC_SUCCESS` on success and
  nonzero on failure.

  This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.

.seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
@*/
PetscErrorCode MatSetOperation(Mat mat, MatOperation op, void (*f)(void))
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))mat->ops->view) mat->ops->viewnative = mat->ops->view;
  (((void (**)(void))mat->ops)[op]) = f;
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@C
  MatGetOperation - Gets a matrix operation for any matrix type.

  Not Collective

  Input Parameters:
+ mat - the matrix
- op  - the name of the operation

  Output Parameter:
. f - the function that provides the operation

  Level: developer

  Example Usage:
.vb
  PetscErrorCode (*usermult)(Mat, Vec, Vec);

  MatGetOperation(A, MATOP_MULT, (void (**)(void))&usermult);
.ve

  Notes:
  See the file include/petscmat.h for a complete list of matrix
  operations, which all have the form MATOP_<OPERATION>, where
  <OPERATION> is the name (in all capital letters) of the
  user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

  This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.

.seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
@*/
PetscErrorCode MatGetOperation(Mat mat, MatOperation op, void (**f)(void))
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  *f = (((void (**)(void))mat->ops)[op]);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatHasOperation - Determines whether the given matrix supports the particular operation.

  Not Collective

  Input Parameters:
+ mat - the matrix
- op  - the operation, for example, `MATOP_GET_DIAGONAL`

  Output Parameter:
. has - either `PETSC_TRUE` or `PETSC_FALSE`

  Level: advanced

  Note:
  See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.

.seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
@*/
PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscAssertPointer(has, 3);
  if (mat->ops->hasoperation) {
    PetscUseTypeMethod(mat, hasoperation, op, has);
  } else {
    if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
    else {
      *has = PETSC_FALSE;
      if (op == MATOP_CREATE_SUBMATRIX) {
        PetscMPIInt size;

        PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
        if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
      }
    }
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent

  Collective

  Input Parameter:
. mat - the matrix

  Output Parameter:
. cong - either `PETSC_TRUE` or `PETSC_FALSE`

  Level: beginner

.seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
@*/
PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(mat, MAT_CLASSID, 1);
  PetscValidType(mat, 1);
  PetscAssertPointer(cong, 2);
  if (!mat->rmap || !mat->cmap) {
    *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
    PetscFunctionReturn(PETSC_SUCCESS);
  }
  if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
    PetscCall(PetscLayoutSetUp(mat->rmap));
    PetscCall(PetscLayoutSetUp(mat->cmap));
    PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
    if (*cong) mat->congruentlayouts = 1;
    else mat->congruentlayouts = 0;
  } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
  PetscFunctionReturn(PETSC_SUCCESS);
}

PetscErrorCode MatSetInf(Mat A)
{
  PetscFunctionBegin;
  PetscUseTypeMethod(A, setinf);
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatCreateGraph - create a scalar matrix (that is a matrix with one vertex for each block vertex in the original matrix), for use in graph algorithms
  and possibly removes small values from the graph structure.

  Collective

  Input Parameters:
+ A       - the matrix
. sym     - `PETSC_TRUE` indicates that the graph should be symmetrized
. scale   - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
. filter  - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
. num_idx - size of 'index' array
- index   - array of block indices to use for graph strength of connection weight

  Output Parameter:
. graph - the resulting graph

  Level: advanced

.seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
@*/
PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscValidType(A, 1);
  PetscValidLogicalCollectiveBool(A, scale, 3);
  PetscAssertPointer(graph, 7);
  PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
  PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
  PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*@
  MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
  meaning the same memory is used for the matrix, and no new memory is allocated.

  Collective

  Input Parameters:
+ A    - the matrix
- keep - if for a given row of `A`, the diagonal coefficient is zero, indicates whether it should be left in the structure or eliminated as well

  Level: intermediate

  Developer Note:
  The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
  of the arrays in the data structure are unneeded.

.seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
@*/
PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
{
  PetscFunctionBegin;
  PetscValidHeaderSpecific(A, MAT_CLASSID, 1);
  PetscUseTypeMethod(A, eliminatezeros, keep);
  PetscFunctionReturn(PETSC_SUCCESS);
}