/*@
TSPseudoSetMaxTimeStep - Sets the maximum time step
when using the TSPseudoTimeStepDefault() routine.
Logically Collective on TS
Input Parameters:
+ ts - the timestep context
- maxdt - the maximum time step, use a non-positive value to deactivate
Options Database Key:
$ -ts_pseudo_max_dt <increment>
Level: advanced
.keywords: timestep, pseudo, set
.seealso: TSPseudoSetTimeStep(), TSPseudoTimeStepDefault()
@*/
PetscErrorCode TSPseudoSetMaxTimeStep(TS ts,PetscReal maxdt)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(ts,TS_CLASSID,1);
PetscValidLogicalCollectiveReal(ts,maxdt,2);
ierr = PetscTryMethod(ts,"TSPseudoSetMaxTimeStep_C",(TS,PetscReal),(ts,maxdt));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
EPSArnoldiSetDelayed - Activates or deactivates delayed reorthogonalization
in the Arnoldi iteration.
Logically Collective on EPS
Input Parameters:
+ eps - the eigenproblem solver context
- delayed - boolean flag
Options Database Key:
. -eps_arnoldi_delayed - Activates delayed reorthogonalization in Arnoldi
Note:
Delayed reorthogonalization is an aggressive optimization for the Arnoldi
eigensolver than may provide better scalability, but sometimes makes the
solver converge less than the default algorithm.
Level: advanced
.seealso: EPSArnoldiGetDelayed()
@*/
PetscErrorCode EPSArnoldiSetDelayed(EPS eps,PetscBool delayed)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveBool(eps,delayed,2);
ierr = PetscTryMethod(eps,"EPSArnoldiSetDelayed_C",(EPS,PetscBool),(eps,delayed));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
EPSJDSetKrylovStart - Activates or deactivates starting the searching
subspace with a Krylov basis.
Logically Collective on EPS
Input Parameters:
+ eps - the eigenproblem solver context
- krylovstart - boolean flag
Options Database Key:
. -eps_jd_krylov_start - Activates starting the searching subspace with a
Krylov basis
Level: advanced
.seealso: EPSJDGetKrylovStart()
@*/
PetscErrorCode EPSJDSetKrylovStart(EPS eps,PetscBool krylovstart)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveBool(eps,krylovstart,2);
ierr = PetscTryMethod(eps,"EPSJDSetKrylovStart_C",(EPS,PetscBool),(eps,krylovstart));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
MatPartitioningChacoSetEigenTol - Sets the tolerance for the eigensolver.
Collective on MatPartitioning
Input Parameters:
+ part - the partitioning context
- tol - the tolerance
Options Database:
. -mat_partitioning_chaco_eigen_tol <tol>: Tolerance for eigensolver
Note:
Must be positive. The default value is 0.001.
Level: advanced
.seealso: MatPartitioningChacoSetEigenSolver(), MatPartitioningChacoGetEigenTol()
@*/
PetscErrorCode MatPartitioningChacoSetEigenTol(MatPartitioning part,PetscReal tol)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(part,MAT_PARTITIONING_CLASSID,1);
PetscValidLogicalCollectiveReal(part,tol,2);
ierr = PetscTryMethod(part,"MatPartitioningChacoSetEigenTol_C",(MatPartitioning,PetscReal),(part,tol));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PCRedundantSetNumber - Sets the number of redundant preconditioner contexts.
Logically Collective on PC
Input Parameters:
+ pc - the preconditioner context
- nredundant - number of redundant preconditioner contexts; for example if you are using 64 MPI processes and
use an nredundant of 4 there will be 4 parallel solves each on 16 = 64/4 processes.
Level: advanced
.keywords: PC, redundant solve
@*/
PetscErrorCode PCRedundantSetNumber(PC pc,PetscInt nredundant)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pc,PC_CLASSID,1);
if (nredundant <= 0) SETERRQ1(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONG, "num of redundant pc %D must be positive",nredundant);
ierr = PetscTryMethod(pc,"PCRedundantSetNumber_C",(PC,PetscInt),(pc,nredundant));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@C
PetscViewerFileSetMode - Sets the type of file to be open
Logically Collective on PetscViewer
Input Parameters:
+ viewer - the PetscViewer; must be a binary, Matlab, hdf, or netcdf PetscViewer
- type - type of file
$ FILE_MODE_WRITE - create new file for binary output
$ FILE_MODE_READ - open existing file for binary input
$ FILE_MODE_APPEND - open existing file for binary output
Level: advanced
.seealso: PetscViewerFileSetMode(), PetscViewerCreate(), PetscViewerSetType(), PetscViewerBinaryOpen()
@*/
PetscErrorCode PetscViewerFileSetMode(PetscViewer viewer,PetscFileMode type)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,1);
PetscValidLogicalCollectiveEnum(viewer,type,2);
ierr = PetscTryMethod(viewer,"PetscViewerFileSetMode_C",(PetscViewer,PetscFileMode),(viewer,type));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PEPLinearSetExplicitMatrix - Indicate if the matrices A and B for the
linearization of the problem must be built explicitly.
Logically Collective on PEP
Input Parameters:
+ pep - polynomial eigenvalue solver
- explicit - boolean flag indicating if the matrices are built explicitly
Options Database Key:
. -pep_linear_explicitmatrix <boolean> - Indicates the boolean flag
Level: advanced
.seealso: PEPLinearGetExplicitMatrix()
@*/
PetscErrorCode PEPLinearSetExplicitMatrix(PEP pep,PetscBool explicitmatrix)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pep,PEP_CLASSID,1);
PetscValidLogicalCollectiveBool(pep,explicitmatrix,2);
ierr = PetscTryMethod(pep,"PEPLinearSetExplicitMatrix_C",(PEP,PetscBool),(pep,explicitmatrix));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
SVDTRLanczosSetOneSide - Indicate if the variant of the Lanczos method
to be used is one-sided or two-sided.
Logically Collective on SVD
Input Parameters:
+ svd - singular value solver
- oneside - boolean flag indicating if the method is one-sided or not
Options Database Key:
. -svd_trlanczos_oneside <boolean> - Indicates the boolean flag
Note:
By default, a two-sided variant is selected, which is sometimes slightly
more robust. However, the one-sided variant is faster because it avoids
the orthogonalization associated to left singular vectors.
Level: advanced
.seealso: SVDLanczosSetOneSide()
@*/
PetscErrorCode SVDTRLanczosSetOneSide(SVD svd,PetscBool oneside)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(svd,SVD_CLASSID,1);
PetscValidLogicalCollectiveBool(svd,oneside,2);
ierr = PetscTryMethod(svd,"SVDTRLanczosSetOneSide_C",(SVD,PetscBool),(svd,oneside));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
KSPCGUseSingleReduction - Merge the two inner products needed in CG into a single MPI_Allreduce() call.
Logically Collective on KSP
Input Parameters:
+ ksp - the iterative context
- flg - turn on or off the single reduction
Options Database:
. -ksp_cg_single_reduction
Level: intermediate
The algorithm used in this case is described as Method 1 in Lapack Working Note 56, "Conjugate Gradient Algorithms with Reduced Synchronization Overhead
Distributed Memory Multiprocessors", by E. F. D'Azevedo, V. L. Eijkhout, and C. H. Romine, December 3, 1999. V. Eijkhout creates the algorithm
initially to Chronopoulos and Gear.
It requires two extra work vectors than the conventional implementation in PETSc.
.keywords: CG, conjugate gradient, Hermitian, symmetric, set, type
@*/
PetscErrorCode KSPCGUseSingleReduction(KSP ksp,PetscBool flg)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(ksp,KSP_CLASSID,1);
PetscValidLogicalCollectiveBool(ksp,flg,2);
ierr = PetscTryMethod(ksp,"KSPCGUseSingleReduction_C",(KSP,PetscBool),(ksp,flg));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
TSAlpha2UseAdapt - Use time-step adaptivity with the Alpha method
Logically Collective on TS
Input Parameter:
+ ts - timestepping context
- use - flag to use adaptivity
Options Database:
. -ts_alpha_adapt
Level: intermediate
.seealso: TSAdapt, TSADAPTBASIC
@*/
PetscErrorCode TSAlpha2UseAdapt(TS ts,PetscBool use)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(ts,TS_CLASSID,1);
PetscValidLogicalCollectiveBool(ts,use,2);
ierr = PetscTryMethod(ts,"TSAlpha2UseAdapt_C",(TS,PetscBool),(ts,use));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@C
SNESCompositeSetType - Sets the type of composite preconditioner.
Logically Collective on SNES
Input Parameter:
+ snes - the preconditioner context
- type - SNES_COMPOSITE_ADDITIVE (default), SNES_COMPOSITE_MULTIPLICATIVE
Options Database Key:
. -snes_composite_type <type: one of multiplicative, additive, special> - Sets composite preconditioner type
Level: Developer
.keywords: SNES, set, type, composite preconditioner, additive, multiplicative
@*/
PetscErrorCode SNESCompositeSetType(SNES snes,SNESCompositeType type)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(snes,SNES_CLASSID,1);
PetscValidLogicalCollectiveEnum(snes,type,2);
ierr = PetscTryMethod(snes,"SNESCompositeSetType_C",(SNES,SNESCompositeType),(snes,type));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
KSPQCGSetTrustRegionRadius - Sets the radius of the trust region.
Logically Collective on KSP
Input Parameters:
+ ksp - the iterative context
- delta - the trust region radius (Infinity is the default)
Options Database Key:
. -ksp_qcg_trustregionradius <delta>
Level: advanced
.keywords: KSP, QCG, set, trust region radius
@*/
PetscErrorCode KSPQCGSetTrustRegionRadius(KSP ksp,PetscReal delta)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(ksp,KSP_CLASSID,1);
if (delta < 0.0) SETERRQ(PetscObjectComm((PetscObject)ksp),PETSC_ERR_ARG_OUTOFRANGE,"Tolerance must be non-negative");
ierr = PetscTryMethod(ksp,"KSPQCGSetTrustRegionRadius_C",(KSP,PetscReal),(ksp,delta));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
KSPChebyshevEstEigSetRandom - set random context for estimating eigenvalues
Logically Collective
Input Arguments:
+ ksp - linear solver context
- random - random number context or NULL to use default
Level: intermediate
.seealso: KSPChebyshevEstEigSet(), PetscRandomCreate()
@*/
PetscErrorCode KSPChebyshevEstEigSetRandom(KSP ksp,PetscRandom random)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(ksp,KSP_CLASSID,1);
if (random) PetscValidHeaderSpecific(random,PETSC_RANDOM_CLASSID,2);
ierr = PetscTryMethod(ksp,"KSPChebyshevEstEigSetRandom_C",(KSP,PetscRandom),(ksp,random));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
SNESMultiblockSetBlockSize - Sets the block size for structured mesh block division. If not set the matrix block size is used.
Logically Collective on SNES
Input Parameters:
+ snes - the solver context
- bs - the block size
Level: intermediate
.seealso: SNESMultiblockGetSubSNES(), SNESMULTIBLOCK, SNESMultiblockSetFields()
@*/
PetscErrorCode SNESMultiblockSetBlockSize(SNES snes, PetscInt bs)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(snes, SNES_CLASSID, 1);
PetscValidLogicalCollectiveInt(snes, bs, 2);
ierr = PetscTryMethod(snes, "SNESMultiblockSetBlockSize_C", (SNES, PetscInt), (snes,bs));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PCFactorSetShiftType - adds a particular type of quantity to the diagonal of the matrix during
numerical factorization, thus the matrix has nonzero pivots
Logically Collective on PC
Input Parameters:
+ pc - the preconditioner context
- shifttype - type of shift; one of MAT_SHIFT_NONE, MAT_SHIFT_NONZERO, MAT_SHIFT_POSITIVE_DEFINITE, MAT_SHIFT_INBLOCKS
Options Database Key:
. -pc_factor_shift_type <shifttype> - Sets shift type or PETSC_DECIDE for the default; use '-help' for a list of available types
Level: intermediate
.keywords: PC, set, factorization,
.seealso: PCFactorSetZeroPivot(), PCFactorSetShiftAmount()
@*/
PetscErrorCode PCFactorSetShiftType(PC pc,MatFactorShiftType shifttype)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pc,PC_CLASSID,1);
PetscValidLogicalCollectiveEnum(pc,shifttype,2);
ierr = PetscTryMethod(pc,"PCFactorSetShiftType_C",(PC,MatFactorShiftType),(pc,shifttype));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
MatPartitioningPartySetBipart - Activate or deactivate recursive bisection.
Collective on MatPartitioning
Input Parameters:
+ part - the partitioning context
- bp - boolean flag
Options Database:
- -mat_partitioning_party_bipart - Bipartitioning option on/off
Level: advanced
@*/
PetscErrorCode MatPartitioningPartySetBipart(MatPartitioning part,PetscBool bp)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(part,MAT_PARTITIONING_CLASSID,1);
PetscValidLogicalCollectiveBool(part,bp,2);
ierr = PetscTryMethod(part,"MatPartitioningPartySetBipart_C",(MatPartitioning,PetscBool),(part,bp));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PCCompositeSpecialSetAlpha - Sets alpha for the special composite preconditioner
for alphaI + R + S
Logically Collective on PC
Input Parameter:
+ pc - the preconditioner context
- alpha - scale on identity
Level: Developer
.keywords: PC, set, type, composite preconditioner, additive, multiplicative
@*/
PetscErrorCode PCCompositeSpecialSetAlpha(PC pc,PetscScalar alpha)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pc,PC_CLASSID,1);
PetscValidLogicalCollectiveScalar(pc,alpha,2);
ierr = PetscTryMethod(pc,"PCCompositeSpecialSetAlpha_C",(PC,PetscScalar),(pc,alpha));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PCFactorSetShiftAmount - adds a quantity to the diagonal of the matrix during
numerical factorization, thus the matrix has nonzero pivots
Logically Collective on PC
Input Parameters:
+ pc - the preconditioner context
- shiftamount - amount of shift
Options Database Key:
. -pc_factor_shift_amount <shiftamount> - Sets shift amount or PETSC_DECIDE for the default
Level: intermediate
.keywords: PC, set, factorization,
.seealso: PCFactorSetZeroPivot(), PCFactorSetShiftType()
@*/
PetscErrorCode PCFactorSetShiftAmount(PC pc,PetscReal shiftamount)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pc,PC_CLASSID,1);
PetscValidLogicalCollectiveReal(pc,shiftamount,2);
ierr = PetscTryMethod(pc,"PCFactorSetShiftAmount_C",(PC,PetscReal),(pc,shiftamount));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PEPLinearSetCompanionForm - Choose between the two companion forms available
for the linearization of a quadratic eigenproblem.
Logically Collective on PEP
Input Parameters:
+ pep - polynomial eigenvalue solver
- cform - 1 or 2 (first or second companion form)
Options Database Key:
. -pep_linear_cform <int> - Choose the companion form
Level: advanced
.seealso: PEPLinearGetCompanionForm()
@*/
PetscErrorCode PEPLinearSetCompanionForm(PEP pep,PetscInt cform)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pep,PEP_CLASSID,1);
PetscValidLogicalCollectiveInt(pep,cform,2);
ierr = PetscTryMethod(pep,"PEPLinearSetCompanionForm_C",(PEP,PetscInt),(pep,cform));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PCFactorReorderForNonzeroDiagonal - reorders rows/columns of matrix to remove zeros from diagonal
Logically Collective on PC
Input Parameters:
+ pc - the preconditioner context
- tol - diagonal entries smaller than this in absolute value are considered zero
Options Database Key:
. -pc_factor_nonzeros_along_diagonal
Level: intermediate
.keywords: PC, set, factorization, direct, fill
.seealso: PCFactorSetFill(), PCFactorSetShiftNonzero(), PCFactorSetZeroPivot(), MatReorderForNonzeroDiagonal()
@*/
PetscErrorCode PCFactorReorderForNonzeroDiagonal(PC pc,PetscReal rtol)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pc,PC_CLASSID,1);
PetscValidLogicalCollectiveReal(pc,rtol,2);
ierr = PetscTryMethod(pc,"PCFactorReorderForNonzeroDiagonal_C",(PC,PetscReal),(pc,rtol));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
MatPartitioningChacoSetEigenSolver - Set eigensolver method for Chaco partitioner.
Collective on MatPartitioning
Input Parameters:
+ part - the partitioning context
- method - one of MP_CHACO_LANCZOS or MP_CHACO_RQI
Options Database:
. -mat_partitioning_chaco_eigen_solver <method> - the eigensolver
Level: advanced
Notes:
The default is to use a Lanczos method. See Chaco documentation for details.
.seealso: MatPartitioningChacoSetEigenTol(),MatPartitioningChacoSetEigenNumber(),
MatPartitioningChacoGetEigenSolver()
@*/
PetscErrorCode MatPartitioningChacoSetEigenSolver(MatPartitioning part,MPChacoEigenType method)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(part,MAT_PARTITIONING_CLASSID,1);
PetscValidLogicalCollectiveEnum(part,method,2);
ierr = PetscTryMethod(part,"MatPartitioningChacoSetEigenSolver_C",(MatPartitioning,MPChacoEigenType),(part,method));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PCFactorSetFill - Indicate the amount of fill you expect in the factored matrix,
fill = number nonzeros in factor/number nonzeros in original matrix.
Not Collective, each process can expect a different amount of fill
Input Parameters:
+ pc - the preconditioner context
- fill - amount of expected fill
Options Database Key:
. -pc_factor_fill <fill> - Sets fill amount
Level: intermediate
Note:
For sparse matrix factorizations it is difficult to predict how much
fill to expect. By running with the option -info PETSc will print the
actual amount of fill used; allowing you to set the value accurately for
future runs. Default PETSc uses a value of 5.0
This parameter has NOTHING to do with the levels-of-fill of ILU(). That is set with PCFactorSetLevels() or -pc_factor_levels.
.keywords: PC, set, factorization, direct, fill
@*/
PetscErrorCode PCFactorSetFill(PC pc,PetscReal fill)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pc,PC_CLASSID,1);
if (fill < 1.0) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_OUTOFRANGE,"Fill factor cannot be less then 1.0");
ierr = PetscTryMethod(pc,"PCFactorSetFill_C",(PC,PetscReal),(pc,fill));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
MatPartitioningChacoSetEigenNumber - Sets the number of eigenvectors to compute
during partitioning.
Collective on MatPartitioning
Input Parameters:
+ part - the partitioning context
- num - the number of eigenvectors
Options Database:
. -mat_partitioning_chaco_eigen_number <n>: Number of eigenvectors
Note:
Accepted values are 1, 2 or 3, indicating partitioning by bisection,
quadrisection, or octosection.
Level: advanced
.seealso: MatPartitioningChacoSetEigenSolver(), MatPartitioningChacoGetEigenTol()
@*/
PetscErrorCode MatPartitioningChacoSetEigenNumber(MatPartitioning part,PetscInt num)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(part,MAT_PARTITIONING_CLASSID,1);
PetscValidLogicalCollectiveInt(part,num,2);
ierr = PetscTryMethod(part,"MatPartitioningChacoSetEigenNumber_C",(MatPartitioning,PetscInt),(part,num));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PCFactorSetZeroPivot - Sets the size at which smaller pivots are declared to be zero
Logically Collective on PC
Input Parameters:
+ pc - the preconditioner context
- zero - all pivots smaller than this will be considered zero
Options Database Key:
. -pc_factor_zeropivot <zero> - Sets tolerance for what is considered a zero pivot
Level: intermediate
.keywords: PC, set, factorization, direct, fill
.seealso: PCFactorSetShiftType(), PCFactorSetShiftAmount()
@*/
PetscErrorCode PCFactorSetZeroPivot(PC pc,PetscReal zero)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pc,PC_CLASSID,1);
PetscValidLogicalCollectiveReal(pc,zero,2);
ierr = PetscTryMethod(pc,"PCFactorSetZeroPivot_C",(PC,PetscReal),(pc,zero));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
NEPInterpolSetDegree - Sets the degree of the interpolation polynomial.
Collective on NEP
Input Parameters:
+ nep - nonlinear eigenvalue solver
- deg - polynomial degree
Level: advanced
.seealso: NEPInterpolGetDegree()
@*/
PetscErrorCode NEPInterpolSetDegree(NEP nep,PetscInt deg)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(nep,NEP_CLASSID,1);
PetscValidLogicalCollectiveInt(nep,deg,2);
ierr = PetscTryMethod(nep,"NEPInterpolSetDegree_C",(NEP,PetscInt),(nep,deg));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PCFactorSetPivotInBlocks - Determines if pivoting is done while factoring each block
with BAIJ or SBAIJ matrices
Logically Collective on PC
Input Parameters:
+ pc - the preconditioner context
- pivot - PETSC_TRUE or PETSC_FALSE
Options Database Key:
. -pc_factor_pivot_in_blocks <true,false>
Level: intermediate
.seealso: PCILUSetMatOrdering(), PCFactorSetColumnPivot()
@*/
PetscErrorCode PCFactorSetPivotInBlocks(PC pc,PetscBool pivot)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pc,PC_CLASSID,1);
PetscValidLogicalCollectiveBool(pc,pivot,2);
ierr = PetscTryMethod(pc,"PCFactorSetPivotInBlocks_C",(PC,PetscBool),(pc,pivot));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PCPARMSSetLocal - Sets the local preconditioner to be used in PARMS.
Collective on PC
Input Parameters:
+ pc - the preconditioner context
- type - the local preconditioner type, one of
.vb
PC_PARMS_LOCAL_ILU0 - ILU0 preconditioner
PC_PARMS_LOCAL_ILUK - ILU(k) preconditioner
PC_PARMS_LOCAL_ILUT - ILUT preconditioner
PC_PARMS_LOCAL_ARMS - ARMS preconditioner
.ve
Options Database Keys:
-pc_parms_local [ilu0,iluk,ilut,arms] - Sets local preconditioner
Level: intermediate
Notes:
For the ARMS preconditioner, one can use either the symmetric ARMS or the non-symmetric
variant (ARMS-ddPQ) by setting the permutation type with PCPARMSSetNonsymPerm().
See the pARMS function parms_PCILUSetType for more information.
.seealso: PCPARMS, PCPARMSSetGlobal(), PCPARMSSetNonsymPerm()
@*/
PetscErrorCode PCPARMSSetLocal(PC pc,PCPARMSLocalType type)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pc,PC_CLASSID,1);
PetscValidLogicalCollectiveEnum(pc,type,2);
ierr = PetscTryMethod(pc,"PCPARMSSetLocal_C",(PC,PCPARMSLocalType),(pc,type));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PCFactorSetReuseFill - When matrices with same different nonzero structure are factored,
this causes later ones to use the fill ratio computed in the initial factorization.
Logically Collective on PC
Input Parameters:
+ pc - the preconditioner context
- flag - PETSC_TRUE to reuse else PETSC_FALSE
Options Database Key:
. -pc_factor_reuse_fill - Activates PCFactorSetReuseFill()
Level: intermediate
.keywords: PC, levels, reordering, factorization, incomplete, Cholesky
.seealso: PCFactorSetReuseOrdering()
@*/
PetscErrorCode PCFactorSetReuseFill(PC pc,PetscBool flag)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(pc,PC_CLASSID,2);
PetscValidLogicalCollectiveBool(pc,flag,2);
ierr = PetscTryMethod(pc,"PCFactorSetReuseFill_C",(PC,PetscBool),(pc,flag));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
EPSJDSetInitialSize - Sets the initial size of the searching space.
Logically Collective on EPS
Input Parameters:
+ eps - the eigenproblem solver context
- initialsize - number of vectors of the initial searching subspace
Options Database Key:
. -eps_jd_initial_size - number of vectors of the initial searching subspace
Notes:
If EPSJDGetKrylovStart() is PETSC_FALSE and the user provides vectors with
EPSSetInitialSpace(), up to initialsize vectors will be used; and if the
provided vectors are not enough, the solver completes the subspace with
random vectors. In the case of EPSJDGetKrylovStart() being PETSC_TRUE, the solver
gets the first vector provided by the user or, if not available, a random vector,
and expands the Krylov basis up to initialsize vectors.
Level: advanced
.seealso: EPSJDGetInitialSize(), EPSJDGetKrylovStart()
@*/
PetscErrorCode EPSJDSetInitialSize(EPS eps,PetscInt initialsize)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveInt(eps,initialsize,2);
ierr = PetscTryMethod(eps,"EPSJDSetInitialSize_C",(EPS,PetscInt),(eps,initialsize));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
TSPseudoSetTimeStepIncrement - Sets the scaling increment applied to
dt when using the TSPseudoTimeStepDefault() routine.
Logically Collective on TS
Input Parameters:
+ ts - the timestep context
- inc - the scaling factor >= 1.0
Options Database Key:
$ -ts_pseudo_increment <increment>
Level: advanced
.keywords: timestep, pseudo, set, increment
.seealso: TSPseudoSetTimeStep(), TSPseudoTimeStepDefault()
@*/
PetscErrorCode TSPseudoSetTimeStepIncrement(TS ts,PetscReal inc)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(ts,TS_CLASSID,1);
PetscValidLogicalCollectiveReal(ts,inc,2);
ierr = PetscTryMethod(ts,"TSPseudoSetTimeStepIncrement_C",(TS,PetscReal),(ts,inc));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
