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/petsc/src/mat/tests/output/
H A Dex125_mumps_seq.out6 0-the sparse MatMatSolve
7 1-the sparse MatMatSolve
17 0-the sparse MatMatSolve
18 1-the sparse MatMatSolve
H A Dex125_nsize-1_saddle_point_mumps_lu.out6 0-the sparse MatMatSolve
7 1-the sparse MatMatSolve
17 0-the sparse MatMatSolve
18 1-the sparse MatMatSolve
H A Dex125_nsize-1_saddle_point_mumps_cholesky.out6 0-the sparse MatMatSolve
7 1-the sparse MatMatSolve
17 0-the sparse MatMatSolve
18 1-the sparse MatMatSolve
/petsc/share/petsc/matlab/
H A DPetscBinaryWrite.m3 % Writes in PETSc binary file sparse matrices and vectors.
7 % a sparse matrix: for example PetscBinaryWrite('myfile',sparse(A));
51 % save sparse matrix in special MATLAB format
53 A = sparse(A);
H A DPetscBinaryRead.m6 % emits as MATLAB sparse matrice or vectors.
128 A = sparse(i,j,complex(s(1:2:2*nz),s(2:2:2*nz)),m,n,nz);
130 A = sparse(i,j,s,m,n,nz);
H A DUFgetPetscMat.m4 % (1) gets the selected index file of the UF sparse matrix collection,
/petsc/doc/overview/
H A Dmatrix_table.md20 - Compressed sparse row
56 * - Kronecker product of sparse matrix :math:`A`; :math:`I \otimes S + A \otimes T`
70 - Block compressed sparse row
75 - Upper triangular compressed sparse row
/petsc/src/ts/tutorials/autodiff/adolc-utils/
H A Ddrivers.cxx41 if (adctx->sparse) { in PetscAdolcComputeRHSJacobian()
79 if (adctx->sparse) { in PetscAdolcComputeRHSJacobianLocal()
121 if (adctx->sparse) { in PetscAdolcComputeIJacobian()
136 if (adctx->sparse) { in PetscAdolcComputeIJacobian()
181 if (adctx->sparse) { in PetscAdolcComputeIJacobianIDMass()
225 if (adctx->sparse) { in PetscAdolcComputeIJacobianLocal()
240 if (adctx->sparse) { in PetscAdolcComputeIJacobianLocal()
284 if (adctx->sparse) { in PetscAdolcComputeIJacobianLocalIDMass()
423 if (adctx->sparse) { in PetscAdolcComputeIJacobianAndDiagonalLocal()
436 if (adctx->sparse) { in PetscAdolcComputeIJacobianAndDiagonalLocal()
H A Dcontexts.cxx17 PetscBool sparse, sparse_view, sparse_view_done; member
/petsc/doc/miscellaneous/
H A Dacknowledgements.md70 - LUSOL - sparse LU factorization code (part of MINOS) developed by
77 - MUMPS - MUltifrontal Massively Parallel sparse direct
82 - PaStiX - Parallel sparse LU and Cholesky solvers;
86 - SPAI - for parallel sparse approximate inverse preconditioning;
88 - SuiteSparse - sequential sparse solvers, developed by
93 - SuperLU and SuperLU_Dist - the efficient sparse LU codes
97 <https://portal.nersc.gov/project/sparse/strumpack/>
/petsc/doc/install/
H A Dexternal_software.md13 - [AMD](http://www.cise.ufl.edu/research/sparse/amd/) Approximate minimum degree orderings.
17 …knowledgecenter/en/SSFHY8/essl_welcome.html) IBM's math library for fast sparse direct LU factoriz…
26 - [MUMPS](https://mumps-solver.org/) MUltifrontal Massively Parallel sparse direct Solver.
31 …pringer.com/referenceworkentry/10.1007%2F978-0-387-09766-4_144) Parallel sparse approximate invers…
34 …v/~xiaoye/SuperLU/#superlu_dist) Robust and efficient sequential and parallel direct sparse solves.
/petsc/doc/developers/
H A Dmatrices.md41 freedom per cell), blocking is advantageous. The PETSc sparse matrix
45 - Storing the matrices using a generic sparse matrix format, but
54 a standard sparse matrix format and brings a large percentage of the
101 several times that of the basic sparse implementations.
105 PETSc offers a variety of both sparse and dense matrix types.
109 The default matrix representation within PETSc is the general sparse AIJ
110 format (also called the compressed sparse row format, CSR).
114 The AIJ sparse matrix type, is the default parallel matrix format;
H A Dobjects.md4 collection of data (for instance, a sparse matrix) is stored in a way
12 `Mat` (matrices, both dense and sparse). Each class is implemented by
31 compressed sparse row) has its own data fields for storing the actual
51 for matrices it is shared by dense, sparse, parallel, and sequential
67 One or more actual implementations of the class (for example, sparse
/petsc/doc/manualpages/MANSECHeaders/
H A DMatFD3 The `MatFD` tools handle the approximation of Jacobians via sparse finite differences.
H A DMatGraphOperations3 These tools compute reorderings (`MatOrdering`) (for sparse matrix factorizations), colorings (`Mat…
H A DMat3 PETSc matrices (`Mat` objects) are used to store Jacobians and other sparse matrices
/petsc/share/petsc/datafiles/matrices/
H A Damesos2_test_mat0.mtx5 % brief: To use for testing Amesos2. This is a 13 by 13 sparse matrix with
H A DLFAT5.mtx4 % http://www.cise.ufl.edu/research/sparse/matrices/Oberwolfach/LFAT5
/petsc/src/dm/interface/
H A Ddmperiodicity.c289 PetscErrorCode DMGetSparseLocalize(DM dm, PetscBool *sparse) in DMGetSparseLocalize() argument
293 PetscAssertPointer(sparse, 2); in DMGetSparseLocalize()
294 *sparse = dm->sparseLocalize; in DMGetSparseLocalize()
311 PetscErrorCode DMSetSparseLocalize(DM dm, PetscBool sparse) in DMSetSparseLocalize() argument
315 PetscValidLogicalCollectiveBool(dm, sparse, 2); in DMSetSparseLocalize()
316 dm->sparseLocalize = sparse; in DMSetSparseLocalize()
/petsc/src/tao/leastsquares/tutorials/
H A DtomographyGenerateData.m11 % L: Forward Model, sparse matrix of NTheta*NTau by Ny*Nx
77 …A', A, 'precision', 'float64'); % do NOT need to convert A to sparse, always write as sparse matrix
/petsc/lib/petsc/bin/
H A DPetscBinaryIO.py347 from scipy.sparse import csr_matrix
353 from scipy.sparse import csr_matrix
508 import scipy.sparse
509 mat = scipy.sparse.load_npz(infile)
/petsc/src/benchmarks/results/
H A Dlap2d.m5 % * matrix vector product for very sparse matrix, and
/petsc/src/ksp/ksp/tutorials/
H A Dex41.m23 A = sparse([3 2 1; 1 3 2; 1 2 3]);
/petsc/src/tao/leastsquares/tutorials/matlab/more_wild_probs/
H A Djacobian.m38 J = sparse(J)';
/petsc/doc/manual/
H A Dmat.md7 dense storage and compressed sparse row storage (both sequential and
67 the sparse AIJ format, which is discussed in detail in
102 *Warning*: Several of the sparse implementations do *not* currently
111 When using the block compressed sparse row matrix format (`MATSEQBAIJ`
158 In the sparse matrix implementations, once the assembly routines have
218 The default matrix representation within PETSc is the general sparse AIJ
219 format (also called the compressed sparse
237 To create a sequential AIJ sparse matrix, `A`, with `m` rows and
264 preallocate the memory needed for the sparse matrix. The user has two
305 Thus, when assembling a sparse matrix with very different numbers of
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