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/petsc/src/mat/tests/output/
H A Dex66_view_cuda.out72 Memory consumption GB (CPU): 0. (dense) 0. (low rank) 0. (total)
73 Memory consumption GB (GPU): 3.2368e-05 (dense) 2.8392e-05 (low rank) 6.076e-05 (total)
H A Dex66_view.out72 Memory consumption GB (CPU): 3.2368e-05 (dense) 2.8392e-05 (low rank) 6.076e-05 (total)
/petsc/lib/petsc/bin/
H A DPetscBinaryIO_tests.py86 dense = np.array([1.1,2.1,0.0,3.1])
95 self.assertTrue(np.allclose(dense, mat[:,:].ravel()))
/petsc/src/ksp/ksp/tutorials/output/
H A Dex82_1.out43 (minimum, mean, maximum) dense block sizes: (36, 39.341459, 169)
92 (minimum, mean, maximum) dense block sizes: (36, 39.341459, 169)
137 (minimum, mean, maximum) dense block sizes: (36, 39.341459, 169)
/petsc/src/dm/impls/composite/
H A Dpackm.c49 PetscBool dense = PETSC_FALSE; in DMCreateMatrix_Composite_AIJ() local
62 …PetscObject)dm)->options, ((PetscObject)dm)->prefix, "-dmcomposite_dense_jacobian", &dense, NULL)); in DMCreateMatrix_Composite_AIJ()
63 if (dense) { in DMCreateMatrix_Composite_AIJ()
H A Dpack.c1488 PetscBool dense = PETSC_FALSE; in DMCreateColoring_Composite() local
1499 …PetscObject)dm)->options, ((PetscObject)dm)->prefix, "-dmcomposite_dense_jacobian", &dense, NULL)); in DMCreateColoring_Composite()
1500 if (dense) { in DMCreateColoring_Composite()
/petsc/doc/developers/
H A Dmatrices.md105 PETSc offers a variety of both sparse and dense matrix types.
122 dense `nb` $\times$ `nb` blocks. The stored row and column
181 PETSc provides both sequential and parallel dense matrix formats, where
187 The parallel dense matrices are partitioned by rows across the
189 dense format described above.
H A Dobjects.md12 `Mat` (matrices, both dense and sparse). Each class is implemented by
51 for matrices it is shared by dense, sparse, parallel, and sequential
/petsc/doc/manual/
H A Dblas-lapack.md7 2. BLAS 2 operations - dense matrix with vector operations, generally the dense matrices are very s…
10 dense matrices may be of modest size, going up to thousands of rows and columns.
H A Dmat.md7 dense storage and compressed sparse row storage (both sequential and
86 This routine inserts or adds a logically dense subblock of dimension
615 Restricted Broyden Family, DFP and BFGS methods, including their dense
658 The dense implementations are numerically equivalent to DFP and BFGS,
660 {cite}`keyprefix-ErwayMarcia2017`. Generally, dense formulations of DFP
663 that `MatMult` of dense BFGS, and `MatSolve` of dense DFP requires
683 PETSc provides both sequential and parallel dense matrix formats, where
685 Fortran style. To create a sequential, dense PETSc matrix, `A` of
696 create a parallel, dense matrix, `A`, the user should call
709 PETSc does not provide parallel dense direct solvers, instead
H A Dadvanced.md80 in a sparse factorization; it does not make much sense for a dense
194 phase.) In general, calling `XXXFactorSymbolic` with a dense matrix
H A Dts.md398 …y properties (IM), stiff accuracy (SA), the existence of an embedded scheme, and dense output (DO).
527 …roperties (IM), stiff accuracy (SA), the existence of an embedded scheme, dense output (DO), the c…
1101 While the true mass matrix generally has a dense inverse and thus must be solved iteratively, the l…
/petsc/src/ts/tutorials/
H A Dgasoline.inp45 # use direct solver (treats Jacobian as dense)
/petsc/doc/changes/
H A D314.md140 - Add support for distributed dense matrices on GPUs
144 - Add MatDense{Get|Restore}ColumnVec to access memory of a dense
147 contiguous subset of columns of a dense matrix as a Mat
152 matrices with mult (multtranspose) operation defined and B dense
H A D312.md135 - MatLoad() now supports loading dense matrices from HDF5/MAT files.
143 - Added MATSEQDENSECUDA class to use GPUs for dense linear algebra.
H A D321.md102 - Add dense representations of symmetric Broyden matrices `MATLMVMDBFGS`, `MATLMVMDDFP`, and `MATLM…
H A D315.md137 built-in implementation uses LAPACK on sequential dense matrices
H A D319.md171 …` and `MatDenseRestoreArrayWriteAndMemType()` to return the array and memory type of a dense matrix
H A D317.md177 - Add `TSSundialsSetUseDense()` and options database option `-ts_sundials_use_dense` to use a dense…
/petsc/doc/miscellaneous/
H A Dacknowledgements.md46 - LINPACK - dense matrix factorization and solve; converted to C using
63 - Elemental - Jack Poulson’s parallel dense matrix solver package;
/petsc/doc/overview/
H A Dnutshell.md18 - {any}`Multiple sparse and dense matrix storage formats<doc_matrix>`,
/petsc/share/petsc/matlab/
H A DPetscBinaryWrite.m4 % If the array is multidimensional and dense it is saved
/petsc/lib/petsc/bin/maint/
H A Dxclude126 petsc-dist/src/mat/impls/dense/mpi/mdnfact.c
/petsc/src/binding/petsc4py/src/petsc4py/PETSc/
H A DMat.pyx1208 """Set the array used for storing matrix elements for a dense matrix.
4353 result is a dense matrix this is irrelevant.
4400 result is a dense matrix this is irrelevant.
4447 result is a dense matrix this is irrelevant.
4494 result is a dense matrix this is irrelevant.
4544 you do not have a good estimate. If the result is a dense
4594 you do not have a good estimate. If the result is a dense
5060 The first dense rectangular matrix.
5064 The second dense rectangular matrix.
5092 The first dense rectangular matrix.
[all …]
/petsc/doc/faq/
H A Dindex.md185 `MatMult()` regardless of whether your matrix is dense, sparse, parallel or
980 The inverse of a matrix (dense or sparse) is essentially always dense, so begin by
981 creating a dense matrix B and fill it with the identity matrix (ones along the diagonal),
982 also create a dense matrix X of the same size that will hold the solution. Then factor the
1021 Like the inverse, the Schur complement of a matrix (dense or sparse) is essentially always
1022 dense, so assuming you wish to calculate $S_A = D - C \underbrace{
1025 1. Forming a dense matrix $B$
1026 2. Also create another dense matrix $V$ of the same size.
1051 Since $S$ is generally dense, standard preconditioning methods cannot typically be

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