PetscErrorCode MatPtAP_SeqAIJ_SeqAIJ(Mat A,Mat P,MatReuse scall,PetscReal fill,Mat *C)
{
PetscErrorCode ierr;
const char *algTypes[2] = {"scalable","nonscalable"};
PetscInt alg=0; /* set default algorithm */
PetscFunctionBegin;
if (scall == MAT_INITIAL_MATRIX) {
/*
Alg 'scalable' determines which implementations to be used:
"nonscalable": do dense axpy in MatPtAPNumeric() - fastest, but requires storage of struct A*P;
"scalable": do two sparse axpy in MatPtAPNumeric() - might slow, does not store structure of A*P.
*/
ierr = PetscObjectOptionsBegin((PetscObject)A);CHKERRQ(ierr);
ierr = PetscOptionsEList("-matptap_via","Algorithmic approach","MatPtAP",algTypes,2,algTypes[0],&alg,NULL);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
ierr = PetscLogEventBegin(MAT_PtAPSymbolic,A,P,0,0);CHKERRQ(ierr);
switch (alg) {
case 1:
ierr = MatPtAPSymbolic_SeqAIJ_SeqAIJ_DenseAxpy(A,P,fill,C);CHKERRQ(ierr);
break;
default:
ierr = MatPtAPSymbolic_SeqAIJ_SeqAIJ_SparseAxpy(A,P,fill,C);CHKERRQ(ierr);
break;
}
ierr = PetscLogEventEnd(MAT_PtAPSymbolic,A,P,0,0);CHKERRQ(ierr);
}
ierr = PetscLogEventBegin(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr);
ierr = (*(*C)->ops->ptapnumeric)(A,P,*C);CHKERRQ(ierr);
ierr = PetscLogEventEnd(MAT_PtAPNumeric,A,P,0,0);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PetscRandomSetFromOptions - Configures the random number generator from the options database.
Collective on PetscRandom
Input Parameter:
. rnd - The random number generator context
Options Database:
. -random_seed <integer> - provide a seed to the random number generater
Notes: To see all options, run your program with the -help option.
Must be called after PetscRandomCreate() but before the rnd is used.
Level: beginner
.keywords: PetscRandom, set, options, database
.seealso: PetscRandomCreate(), PetscRandomSetType()
@*/
PetscErrorCode PetscRandomSetFromOptions(PetscRandom rnd)
{
PetscErrorCode ierr;
PetscBool set;
PetscInt seed;
PetscFunctionBegin;
PetscValidHeaderSpecific(rnd,PETSC_RANDOM_CLASSID,1);
ierr = PetscObjectOptionsBegin((PetscObject)rnd);CHKERRQ(ierr);
/* Handle PetscRandom type options */
ierr = PetscRandomSetTypeFromOptions_Private(rnd);CHKERRQ(ierr);
/* Handle specific random generator's options */
if (rnd->ops->setfromoptions) {
ierr = (*rnd->ops->setfromoptions)(rnd);CHKERRQ(ierr);
}
ierr = PetscOptionsInt("-random_seed","Seed to use to generate random numbers","PetscRandomSetSeed",0,&seed,&set);CHKERRQ(ierr);
if (set) {
ierr = PetscRandomSetSeed(rnd,(unsigned long int)seed);CHKERRQ(ierr);
ierr = PetscRandomSeed(rnd);CHKERRQ(ierr);
}
ierr = PetscOptionsEnd();CHKERRQ(ierr);
ierr = PetscRandomViewFromOptions(rnd,NULL, "-random_view");CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
MatColoringSetFromOptions - Sets MatColoring options from user parameters
Collective on MatColoring
Input Parameters:
. mc - MatColoring context
Options Database Keys:
+ -mat_coloring_type - the type of coloring algorithm used
. -mat_coloring_maxcolors - the maximum number of relevant colors, all nodes not in a color are in maxcolors+1
. -mat_coloring_distance - compute a distance 1,2,... coloring.
. -mat_coloring_view - print information about the coloring and the produced index sets
Level: beginner
.keywords: Coloring, Matrix
.seealso: MatColoring, MatColoringApply()
@*/
PetscErrorCode MatColoringSetFromOptions(MatColoring mc)
{
PetscBool flg;
MatColoringType deft = MATCOLORINGSL;
char type[256];
PetscErrorCode ierr;
PetscInt dist,maxcolors;
PetscFunctionBegin;
PetscValidHeaderSpecific(mc,MAT_COLORING_CLASSID,1);
ierr = MatColoringGetDistance(mc,&dist);CHKERRQ(ierr);
ierr = MatColoringGetMaxColors(mc,&maxcolors);CHKERRQ(ierr);
if (!MatColoringRegisterAllCalled) {ierr = MatColoringRegisterAll();CHKERRQ(ierr);}
ierr = PetscObjectOptionsBegin((PetscObject)mc);CHKERRQ(ierr);
if (((PetscObject)mc)->type_name) deft = ((PetscObject)mc)->type_name;
ierr = PetscOptionsFList("-mat_coloring_type","The coloring method used","MatColoringSetType",MatColoringList,deft,type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = MatColoringSetType(mc,type);CHKERRQ(ierr);
} else if (!((PetscObject)mc)->type_name) {
ierr = MatColoringSetType(mc,deft);CHKERRQ(ierr);
}
ierr = PetscOptionsInt("-mat_coloring_distance","Distance of the coloring","MatColoringSetDistance",dist,&dist,&flg);CHKERRQ(ierr);
if (flg) {ierr = MatColoringSetDistance(mc,dist);CHKERRQ(ierr);}
ierr = PetscOptionsInt("-mat_coloring_maxcolors","Maximum colors returned at the end. 1 returns an independent set","MatColoringSetMaxColors",maxcolors,&maxcolors,&flg);CHKERRQ(ierr);
if (flg) {ierr = MatColoringSetMaxColors(mc,maxcolors);CHKERRQ(ierr);}
if (mc->ops->setfromoptions) {
ierr = (*mc->ops->setfromoptions)(mc);CHKERRQ(ierr);
}
ierr = PetscOptionsBool("-mat_coloring_valid","Check that a valid coloring has been produced","",mc->valid,&mc->valid,NULL);CHKERRQ(ierr);
ierr = PetscOptionsEnum("-mat_coloring_weight_type","Sets the type of vertex weighting used","MatColoringSetWeightType",MatColoringWeightTypes,(PetscEnum)mc->weight_type,(PetscEnum*)&mc->weight_type,NULL);CHKERRQ(ierr);
ierr = PetscObjectProcessOptionsHandlers((PetscObject)mc);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
static PetscErrorCode MatSetFromOptions_MFFD(PetscOptionItems *PetscOptionsObject,Mat mat)
{
MatMFFD mfctx = (MatMFFD)mat->data;
PetscErrorCode ierr;
PetscBool flg;
char ftype[256];
PetscFunctionBegin;
PetscValidHeaderSpecific(mat,MAT_CLASSID,1);
PetscValidHeaderSpecific(mfctx,MATMFFD_CLASSID,1);
ierr = PetscObjectOptionsBegin((PetscObject)mfctx);CHKERRQ(ierr);
ierr = PetscOptionsFList("-mat_mffd_type","Matrix free type","MatMFFDSetType",MatMFFDList,((PetscObject)mfctx)->type_name,ftype,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = MatMFFDSetType(mat,ftype);CHKERRQ(ierr);
}
ierr = PetscOptionsReal("-mat_mffd_err","set sqrt relative error in function","MatMFFDSetFunctionError",mfctx->error_rel,&mfctx->error_rel,0);CHKERRQ(ierr);
ierr = PetscOptionsInt("-mat_mffd_period","how often h is recomputed","MatMFFDSetPeriod",mfctx->recomputeperiod,&mfctx->recomputeperiod,0);CHKERRQ(ierr);
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-mat_mffd_check_positivity","Insure that U + h*a is nonnegative","MatMFFDSetCheckh",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
ierr = MatMFFDSetCheckh(mat,MatMFFDCheckPositivity,0);CHKERRQ(ierr);
}
#if defined(PETSC_USE_COMPLEX)
ierr = PetscOptionsBool("-mat_mffd_complex","Use Lyness complex number trick to compute the matrix-vector product","None",mfctx->usecomplex,&mfctx->usecomplex,NULL);CHKERRQ(ierr);
#endif
if (mfctx->ops->setfromoptions) {
ierr = (*mfctx->ops->setfromoptions)(PetscOptionsObject,mfctx);CHKERRQ(ierr);
}
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
MatFDColoringSetFromOptions - Sets coloring finite difference parameters from
the options database.
Collective on MatFDColoring
The Jacobian, F'(u), is estimated with the differencing approximation
.vb
F'(u)_{:,i} = [F(u+h*dx_{i}) - F(u)]/h where
h = error_rel*u[i] if abs(u[i]) > umin
= +/- error_rel*umin otherwise, with +/- determined by the sign of u[i]
dx_{i} = (0, ... 1, .... 0)
.ve
Input Parameter:
. coloring - the coloring context
Options Database Keys:
+ -mat_fd_coloring_err <err> - Sets <err> (square root of relative error in the function)
. -mat_fd_coloring_umin <umin> - Sets umin, the minimum allowable u-value magnitude
. -mat_fd_type - "wp" or "ds" (see MATMFFD_WP or MATMFFD_DS)
. -mat_fd_coloring_view - Activates basic viewing
. -mat_fd_coloring_view ::ascii_info - Activates viewing info
- -mat_fd_coloring_view draw - Activates drawing
Level: intermediate
.keywords: Mat, finite differences, parameters
.seealso: MatFDColoringCreate(), MatFDColoringView(), MatFDColoringSetParameters()
@*/
PetscErrorCode MatFDColoringSetFromOptions(MatFDColoring matfd)
{
PetscErrorCode ierr;
PetscBool flg;
char value[3];
PetscFunctionBegin;
PetscValidHeaderSpecific(matfd,MAT_FDCOLORING_CLASSID,1);
ierr = PetscObjectOptionsBegin((PetscObject)matfd);CHKERRQ(ierr);
ierr = PetscOptionsReal("-mat_fd_coloring_err","Square root of relative error in function","MatFDColoringSetParameters",matfd->error_rel,&matfd->error_rel,0);CHKERRQ(ierr);
ierr = PetscOptionsReal("-mat_fd_coloring_umin","Minimum allowable u magnitude","MatFDColoringSetParameters",matfd->umin,&matfd->umin,0);CHKERRQ(ierr);
ierr = PetscOptionsString("-mat_fd_type","Algorithm to compute h, wp or ds","MatFDColoringCreate",matfd->htype,value,3,&flg);CHKERRQ(ierr);
if (flg) {
if (value[0] == 'w' && value[1] == 'p') matfd->htype = "wp";
else if (value[0] == 'd' && value[1] == 's') matfd->htype = "ds";
else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Unknown finite differencing type %s",value);
}
ierr = PetscOptionsInt("-mat_fd_coloring_brows","Number of block rows","MatFDColoringSetBlockSize",matfd->brows,&matfd->brows,NULL);CHKERRQ(ierr);
ierr = PetscOptionsInt("-mat_fd_coloring_bcols","Number of block columns","MatFDColoringSetBlockSize",matfd->bcols,&matfd->bcols,&flg);CHKERRQ(ierr);
if (flg && matfd->bcols > matfd->ncolors) {
/* input bcols cannot be > matfd->ncolors, thus set it as ncolors */
matfd->bcols = matfd->ncolors;
}
/* process any options handlers added with PetscObjectAddOptionsHandler() */
ierr = PetscObjectProcessOptionsHandlers((PetscObject)matfd);CHKERRQ(ierr);
PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
PetscErrorCode MatRARt_SeqAIJ_SeqAIJ(Mat A,Mat R,MatReuse scall,PetscReal fill,Mat *C)
{
PetscErrorCode ierr;
const char *algTypes[3] = {"matmatmatmult","matmattransposemult","coloring_rart"};
PetscInt alg=0; /* set default algorithm */
PetscFunctionBegin;
if (scall == MAT_INITIAL_MATRIX) {
ierr = PetscObjectOptionsBegin((PetscObject)A);CHKERRQ(ierr);
ierr = PetscOptionsEList("-matrart_via","Algorithmic approach","MatRARt",algTypes,3,algTypes[0],&alg,NULL);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
ierr = PetscLogEventBegin(MAT_RARtSymbolic,A,R,0,0);CHKERRQ(ierr);
switch (alg) {
case 1:
/* via matmattransposemult: ARt=A*R^T, C=R*ARt - matrix coloring can be applied to A*R^T */
ierr = MatRARtSymbolic_SeqAIJ_SeqAIJ_matmattransposemult(A,R,fill,C);CHKERRQ(ierr);
break;
case 2:
/* via coloring_rart: apply coloring C = R*A*R^T */
ierr = MatRARtSymbolic_SeqAIJ_SeqAIJ_colorrart(A,R,fill,C);CHKERRQ(ierr);
break;
default:
/* via matmatmatmult: Rt=R^T, C=R*A*Rt - avoid inefficient sparse inner products */
ierr = MatRARtSymbolic_SeqAIJ_SeqAIJ(A,R,fill,C);CHKERRQ(ierr);
break;
}
ierr = PetscLogEventEnd(MAT_RARtSymbolic,A,R,0,0);CHKERRQ(ierr);
}
ierr = PetscLogEventBegin(MAT_RARtNumeric,A,R,0,0);CHKERRQ(ierr);
ierr = (*(*C)->ops->rartnumeric)(A,R,*C);CHKERRQ(ierr);
ierr = PetscLogEventEnd(MAT_RARtNumeric,A,R,0,0);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@C
PetscViewerSetFromOptions - Sets the graphics type from the options database.
Defaults to a PETSc X windows graphics.
Collective on PetscViewer
Input Parameter:
. PetscViewer - the graphics context
Level: intermediate
Notes:
Must be called after PetscViewerCreate() before the PetscViewer is used.
Concepts: PetscViewer^setting options
.seealso: PetscViewerCreate(), PetscViewerSetType(), PetscViewerType
@*/
PetscErrorCode PetscViewerSetFromOptions(PetscViewer viewer)
{
PetscErrorCode ierr;
char vtype[256];
PetscBool flg;
PetscFunctionBegin;
PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,1);
if (!PetscViewerList) {
ierr = PetscViewerRegisterAll();CHKERRQ(ierr);
}
ierr = PetscObjectOptionsBegin((PetscObject)viewer);CHKERRQ(ierr);
ierr = PetscOptionsFList("-viewer_type","Type of PetscViewer","None",PetscViewerList,(char*)(((PetscObject)viewer)->type_name ? ((PetscObject)viewer)->type_name : PETSCVIEWERASCII),vtype,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscViewerSetType(viewer,vtype);CHKERRQ(ierr);
}
/* type has not been set? */
if (!((PetscObject)viewer)->type_name) {
ierr = PetscViewerSetType(viewer,PETSCVIEWERASCII);CHKERRQ(ierr);
}
if (viewer->ops->setfromoptions) {
ierr = (*viewer->ops->setfromoptions)(PetscOptionsObject,viewer);CHKERRQ(ierr);
}
/* process any options handlers added with PetscObjectAddOptionsHandler() */
ierr = PetscObjectProcessOptionsHandlers((PetscObject)viewer);CHKERRQ(ierr);
ierr = PetscViewerViewFromOptions(viewer,NULL,"-viewer_view");CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
MatPartitioningSetFromOptions - Sets various partitioning options from the
options database.
Collective on MatPartitioning
Input Parameter:
. part - the partitioning context.
Options Database Command:
$ -mat_partitioning_type <type>
$ Use -help for a list of available methods
$ (for instance, parmetis)
Level: beginner
.keywords: partitioning, set, method, type
@*/
PetscErrorCode MatPartitioningSetFromOptions(MatPartitioning part)
{
PetscErrorCode ierr;
PetscBool flag;
char type[256];
const char *def;
PetscFunctionBegin;
ierr = PetscObjectOptionsBegin((PetscObject)part);CHKERRQ(ierr);
if (!((PetscObject)part)->type_name) {
#if defined(PETSC_HAVE_PARMETIS)
def = MATPARTITIONINGPARMETIS;
#else
def = MATPARTITIONINGCURRENT;
#endif
} else {
def = ((PetscObject)part)->type_name;
}
ierr = PetscOptionsList("-mat_partitioning_type","Type of partitioner","MatPartitioningSetType",MatPartitioningList,def,type,256,&flag);CHKERRQ(ierr);
if (flag) {
ierr = MatPartitioningSetType(part,type);CHKERRQ(ierr);
}
/*
Set the type if it was never set.
*/
if (!((PetscObject)part)->type_name) {
ierr = MatPartitioningSetType(part,def);CHKERRQ(ierr);
}
if (part->ops->setfromoptions) {
ierr = (*part->ops->setfromoptions)(part);CHKERRQ(ierr);
}
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
MatCoarsenSetFromOptions - Sets various coarsen options from the
options database.
Collective on MatCoarsen
Input Parameter:
. coarser - the coarsen context.
Options Database Command:
$ -mat_coarsen_type <type>
$ Use -help for a list of available methods
$ (for instance, mis)
Level: beginner
.keywords: coarsen, set, method, type
@*/
PetscErrorCode MatCoarsenSetFromOptions(MatCoarsen coarser)
{
PetscErrorCode ierr;
PetscBool flag;
char type[256];
const char *def;
PetscFunctionBegin;
ierr = PetscObjectOptionsBegin((PetscObject)coarser);CHKERRQ(ierr);
if (!((PetscObject)coarser)->type_name) {
def = MATCOARSENMIS;
} else {
def = ((PetscObject)coarser)->type_name;
}
ierr = PetscOptionsFList("-mat_coarsen_type","Type of aggregator","MatCoarsenSetType",MatCoarsenList,def,type,256,&flag);CHKERRQ(ierr);
if (flag) {
ierr = MatCoarsenSetType(coarser,type);CHKERRQ(ierr);
}
/*
Set the type if it was never set.
*/
if (!((PetscObject)coarser)->type_name) {
ierr = MatCoarsenSetType(coarser,def);CHKERRQ(ierr);
}
if (coarser->ops->setfromoptions) {
ierr = (*coarser->ops->setfromoptions)(coarser);CHKERRQ(ierr);
}
ierr = PetscOptionsEnd();CHKERRQ(ierr);
ierr = MatCoarsenViewFromOptions(coarser,NULL,"-mat_coarsen_view");CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
RGSetFromOptions - Sets RG options from the options database.
Collective on RG
Input Parameters:
. rg - the region context
Notes:
To see all options, run your program with the -help option.
Level: beginner
@*/
PetscErrorCode RGSetFromOptions(RG rg)
{
PetscErrorCode ierr;
char type[256];
PetscBool flg;
PetscFunctionBegin;
PetscValidHeaderSpecific(rg,RG_CLASSID,1);
if (!RGRegisterAllCalled) { ierr = RGRegisterAll();CHKERRQ(ierr); }
ierr = PetscObjectOptionsBegin((PetscObject)rg);CHKERRQ(ierr);
ierr = PetscOptionsFList("-rg_type","Region type","RGSetType",RGList,(char*)(((PetscObject)rg)->type_name?((PetscObject)rg)->type_name:RGINTERVAL),type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = RGSetType(rg,type);CHKERRQ(ierr);
}
/*
Set the type if it was never set.
*/
if (!((PetscObject)rg)->type_name) {
ierr = RGSetType(rg,RGINTERVAL);CHKERRQ(ierr);
}
ierr = PetscOptionsBool("-rg_complement","Whether region is complemented or not","RGSetComplement",rg->complement,&rg->complement,&flg);CHKERRQ(ierr);
if (rg->ops->setfromoptions) {
ierr = (*rg->ops->setfromoptions)(rg);CHKERRQ(ierr);
}
ierr = PetscObjectProcessOptionsHandlers((PetscObject)rg);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@C
PetscDrawSetFromOptions - Sets the graphics type from the options database.
Defaults to a PETSc X windows graphics.
Collective on PetscDraw
Input Parameter:
. draw - the graphics context
Options Database Keys:
+ -nox - do not use X graphics (ignore graphics calls, but run program correctly)
- -nox_warning - when X windows support is not installed this prevents the warning message
from being printed
Level: intermediate
Notes:
Must be called after PetscDrawCreate() before the PetscDrawtor is used.
Concepts: drawing^setting options
Concepts: graphics^setting options
.seealso: PetscDrawCreate(), PetscDrawSetType()
@*/
PetscErrorCode PetscDrawSetFromOptions(PetscDraw draw)
{
PetscErrorCode ierr;
PetscBool flg,nox;
char vtype[256];
const char *def;
PetscBool save;
#if !defined(PETSC_USE_WINDOWS_GRAPHICS) && !defined(PETSC_HAVE_X)
PetscBool warn;
#endif
char filename[PETSC_MAX_PATH_LEN];
PetscBool movie = PETSC_FALSE;
PetscFunctionBegin;
PetscValidHeaderSpecific(draw,PETSC_DRAW_CLASSID,1);
if (!PetscDrawList) {
ierr = PetscDrawRegisterAll(PETSC_NULL);CHKERRQ(ierr);
}
if (((PetscObject)draw)->type_name) {
def = ((PetscObject)draw)->type_name;
} else {
ierr = PetscOptionsHasName(PETSC_NULL,"-nox",&nox);CHKERRQ(ierr);
def = PETSC_DRAW_NULL;
#if defined(PETSC_USE_WINDOWS_GRAPHICS) && !defined(PETSC_HAVE_X)
if (!nox) def = PETSC_DRAW_WIN32;
#elif defined(PETSC_HAVE_X)
if (!nox) def = PETSC_DRAW_X;
#else
ierr = PetscOptionsHasName(PETSC_NULL,"-nox_warning",&warn);CHKERRQ(ierr);
if (!nox && !warn) {
(*PetscErrorPrintf)("PETSc installed without X windows on this machine\nproceeding without graphics\n");
}
#endif
}
ierr = PetscObjectOptionsBegin((PetscObject)draw);CHKERRQ(ierr);
ierr = PetscOptionsList("-draw_type","Type of graphical output","PetscDrawSetType",PetscDrawList,def,vtype,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscDrawSetType(draw,vtype);CHKERRQ(ierr);
} else if (!((PetscObject)draw)->type_name) {
ierr = PetscDrawSetType(draw,def);CHKERRQ(ierr);
}
ierr = PetscOptionsName("-nox","Run without graphics","None",&nox);CHKERRQ(ierr);
ierr = PetscOptionsBool("-draw_save_movie","Make a movie from the images saved","PetscDrawSetSave",movie,&movie,PETSC_NULL);CHKERRQ(ierr);
ierr = PetscOptionsString("-draw_save","Save graphics to file","PetscDrawSetSave",filename,filename,PETSC_MAX_PATH_LEN,&save);CHKERRQ(ierr);
if (save) {
ierr = PetscDrawSetSave(draw,filename,movie);CHKERRQ(ierr);
}
/* process any options handlers added with PetscObjectAddOptionsHandler() */
ierr = PetscObjectProcessOptionsHandlers((PetscObject)draw);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PetscRandomSetFromOptions - Configures the random number generator from the options database.
Collective on PetscRandom
Input Parameter:
. rnd - The random number generator context
Options Database:
+ -random_seed <integer> - provide a seed to the random number generater
- -random_no_imaginary_part - makes the imaginary part of the random number zero, this is useful when you want the
same code to produce the same result when run with real numbers or complex numbers for regression testing purposes
Notes: To see all options, run your program with the -help option.
Must be called after PetscRandomCreate() but before the rnd is used.
Level: beginner
.keywords: PetscRandom, set, options, database
.seealso: PetscRandomCreate(), PetscRandomSetType()
@*/
PetscErrorCode PetscRandomSetFromOptions(PetscRandom rnd)
{
PetscErrorCode ierr;
PetscBool set,noimaginary = PETSC_FALSE;
PetscInt seed;
PetscFunctionBegin;
PetscValidHeaderSpecific(rnd,PETSC_RANDOM_CLASSID,1);
ierr = PetscObjectOptionsBegin((PetscObject)rnd);
CHKERRQ(ierr);
/* Handle PetscRandom type options */
ierr = PetscRandomSetTypeFromOptions_Private(PetscOptionsObject,rnd);
CHKERRQ(ierr);
/* Handle specific random generator's options */
if (rnd->ops->setfromoptions) {
ierr = (*rnd->ops->setfromoptions)(PetscOptionsObject,rnd);
CHKERRQ(ierr);
}
ierr = PetscOptionsInt("-random_seed","Seed to use to generate random numbers","PetscRandomSetSeed",0,&seed,&set);
CHKERRQ(ierr);
if (set) {
ierr = PetscRandomSetSeed(rnd,(unsigned long int)seed);
CHKERRQ(ierr);
ierr = PetscRandomSeed(rnd);
CHKERRQ(ierr);
}
ierr = PetscOptionsBool("-random_no_imaginary_part","The imaginary part of the random number will be zero","PetscRandomSetInterval",noimaginary,&noimaginary,&set);
CHKERRQ(ierr);
#if defined(PETSC_HAVE_COMPLEX)
if (set) {
if (noimaginary) {
PetscScalar low,high;
ierr = PetscRandomGetInterval(rnd,&low,&high);
CHKERRQ(ierr);
low = low - PetscImaginaryPart(low);
high = high - PetscImaginaryPart(high);
ierr = PetscRandomSetInterval(rnd,low,high);
CHKERRQ(ierr);
}
}
#endif
ierr = PetscOptionsEnd();
CHKERRQ(ierr);
ierr = PetscRandomViewFromOptions(rnd,NULL, "-random_view");
CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
MatSetFromOptions - Creates a matrix where the type is determined
from the options database. Generates a parallel MPI matrix if the
communicator has more than one processor. The default matrix type is
AIJ, using the routines MatCreateSeqAIJ() and MatCreateAIJ() if
you do not select a type in the options database.
Collective on Mat
Input Parameter:
. A - the matrix
Options Database Keys:
+ -mat_type seqaij - AIJ type, uses MatCreateSeqAIJ()
. -mat_type mpiaij - AIJ type, uses MatCreateAIJ()
. -mat_type seqdense - dense type, uses MatCreateSeqDense()
. -mat_type mpidense - dense type, uses MatCreateDense()
. -mat_type seqbaij - block AIJ type, uses MatCreateSeqBAIJ()
- -mat_type mpibaij - block AIJ type, uses MatCreateBAIJ()
Even More Options Database Keys:
See the manpages for particular formats (e.g., MatCreateSeqAIJ())
for additional format-specific options.
Level: beginner
.keywords: matrix, create
.seealso: MatCreateSeqAIJ((), MatCreateAIJ(),
MatCreateSeqDense(), MatCreateDense(),
MatCreateSeqBAIJ(), MatCreateBAIJ(),
MatCreateSeqSBAIJ(), MatCreateSBAIJ(),
MatConvert()
@*/
PetscErrorCode MatSetFromOptions(Mat B)
{
PetscErrorCode ierr;
const char *deft = MATAIJ;
char type[256];
PetscBool flg,set;
PetscFunctionBegin;
PetscValidHeaderSpecific(B,MAT_CLASSID,1);
ierr = PetscObjectOptionsBegin((PetscObject)B);CHKERRQ(ierr);
if (B->rmap->bs < 0) {
PetscInt newbs = -1;
ierr = PetscOptionsInt("-mat_block_size","Set the blocksize used to store the matrix","MatSetBlockSize",newbs,&newbs,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscLayoutSetBlockSize(B->rmap,newbs);CHKERRQ(ierr);
ierr = PetscLayoutSetBlockSize(B->cmap,newbs);CHKERRQ(ierr);
}
}
ierr = PetscOptionsFList("-mat_type","Matrix type","MatSetType",MatList,deft,type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = MatSetType(B,type);CHKERRQ(ierr);
} else if (!((PetscObject)B)->type_name) {
ierr = MatSetType(B,deft);CHKERRQ(ierr);
}
ierr = PetscOptionsName("-mat_is_symmetric","Checks if mat is symmetric on MatAssemblyEnd()","MatIsSymmetric",&B->checksymmetryonassembly);CHKERRQ(ierr);
ierr = PetscOptionsReal("-mat_is_symmetric","Checks if mat is symmetric on MatAssemblyEnd()","MatIsSymmetric",0.0,&B->checksymmetrytol,NULL);CHKERRQ(ierr);
ierr = PetscOptionsBool("-mat_null_space_test","Checks if provided null space is correct in MatAssemblyEnd()","MatSetNullSpaceTest",PETSC_FALSE,&B->checknullspaceonassembly,NULL);CHKERRQ(ierr);
if (B->ops->setfromoptions) {
ierr = (*B->ops->setfromoptions)(B);CHKERRQ(ierr);
}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-mat_new_nonzero_location_err","Generate an error if new nonzeros are created in the matrix structure (useful to test preallocation)","MatSetOption",flg,&flg,&set);CHKERRQ(ierr);
if (set) {ierr = MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,flg);CHKERRQ(ierr);}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-mat_new_nonzero_allocation_err","Generate an error if new nonzeros are allocated in the matrix structure (useful to test preallocation)","MatSetOption",flg,&flg,&set);CHKERRQ(ierr);
if (set) {ierr = MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,flg);CHKERRQ(ierr);}
/* process any options handlers added with PetscObjectAddOptionsHandler() */
ierr = PetscObjectProcessOptionsHandlers((PetscObject)B);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@C
PetscSFSetFromOptions - set PetscSF options using the options database
Logically Collective
Input Arguments:
. sf - star forest
Options Database Keys:
+ -sf_type - implementation type, see PetscSFSetType()
- -sf_rank_order - sort composite points for gathers and scatters in rank order, gathers are non-deterministic otherwise
Level: intermediate
.keywords: KSP, set, from, options, database
.seealso: PetscSFWindowSetSyncType()
@*/
PetscErrorCode PetscSFSetFromOptions(PetscSF sf)
{
PetscSFType deft;
char type[256];
PetscErrorCode ierr;
PetscBool flg;
PetscFunctionBegin;
PetscValidHeaderSpecific(sf,PETSCSF_CLASSID,1);
ierr = PetscObjectOptionsBegin((PetscObject)sf);CHKERRQ(ierr);
deft = ((PetscObject)sf)->type_name ? ((PetscObject)sf)->type_name : PETSCSFBASIC;
ierr = PetscOptionsFList("-sf_type","PetscSF implementation type","PetscSFSetType",PetscSFList,deft,type,256,&flg);CHKERRQ(ierr);
ierr = PetscSFSetType(sf,flg ? type : deft);CHKERRQ(ierr);
ierr = PetscOptionsBool("-sf_rank_order","sort composite points for gathers and scatters in rank order, gathers are non-deterministic otherwise","PetscSFSetRankOrder",sf->rankorder,&sf->rankorder,NULL);CHKERRQ(ierr);
if (sf->ops->SetFromOptions) {ierr = (*sf->ops->SetFromOptions)(PetscOptionsObject,sf);CHKERRQ(ierr);}
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
TSTrajectorySetFromOptions - Sets various TSTrajectory parameters from user options.
Collective on TSTrajectory
Input Parameter:
+ tj - the TSTrajectory context obtained from TSTrajectoryCreate()
- ts - the TS context
Options Database Keys:
+ -ts_trajectory_type <type> - TSTRAJECTORYBASIC, TSTRAJECTORYMEMORY, TSTRAJECTORYSINGLEFILE, TSTRAJECTORYVISUALIZATION
- -ts_trajectory_monitor - print TSTrajectory information
Level: advanced
Notes: This is not normally called directly by users
.keywords: TS, trajectory, timestep, set, options, database
.seealso: TSSetSaveTrajectory(), TSTrajectorySetUp()
@*/
PetscErrorCode TSTrajectorySetFromOptions(TSTrajectory tj,TS ts)
{
PetscErrorCode ierr;
PetscBool set,flg;
PetscFunctionBegin;
PetscValidHeaderSpecific(tj,TSTRAJECTORY_CLASSID,1);
PetscValidHeaderSpecific(ts,TS_CLASSID,2);
ierr = PetscObjectOptionsBegin((PetscObject)tj);CHKERRQ(ierr);
ierr = TSTrajectorySetTypeFromOptions_Private(PetscOptionsObject,tj,ts);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ts_trajectory_monitor","Print checkpointing schedules","TSTrajectorySetMonitor",tj->monitor ? PETSC_TRUE:PETSC_FALSE,&flg,&set);CHKERRQ(ierr);
if (set) {ierr = TSTrajectorySetMonitor(tj,flg);CHKERRQ(ierr);}
/* Handle specific TS options */
if (tj->ops->setfromoptions) {
ierr = (*tj->ops->setfromoptions)(PetscOptionsObject,tj);CHKERRQ(ierr);
}
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@C
AOSetFromOptions - Sets AO options from the options database.
Collective on AO
Input Parameter:
. ao - the application ordering
Level: beginner
.keywords: AO, options, database
.seealso: AOCreate(), AOSetType(), AODestroy(), AOPetscToApplication(), AOApplicationToPetsc()
@*/
PetscErrorCode AOSetFromOptions(AO ao)
{
PetscErrorCode ierr;
char type[256];
const char *def=AOBASIC;
PetscBool flg;
PetscFunctionBegin;
PetscValidHeaderSpecific(ao,AO_CLASSID,1);
ierr = PetscObjectOptionsBegin((PetscObject)ao);CHKERRQ(ierr);
ierr = PetscOptionsFList("-ao_type","AO type","AOSetType",AOList,def,type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = AOSetType(ao,type);CHKERRQ(ierr);
} else if (!((PetscObject)ao)->type_name) {
ierr = AOSetType(ao,def);CHKERRQ(ierr);
}
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PCSetFromOptions - Sets PC options from the options database.
This routine must be called before PCSetUp() if the user is to be
allowed to set the preconditioner method.
Collective on PC
Input Parameter:
. pc - the preconditioner context
Options Database:
. -pc_use_amat true,false see PCSetUseAmat()
Level: developer
.keywords: PC, set, from, options, database
.seealso: PCSetUseAmat()
@*/
PetscErrorCode PCSetFromOptions(PC pc)
{
PetscErrorCode ierr;
char type[256];
const char *def;
PetscBool flg;
PetscFunctionBegin;
PetscValidHeaderSpecific(pc,PC_CLASSID,1);
ierr = PCRegisterAll();CHKERRQ(ierr);
ierr = PetscObjectOptionsBegin((PetscObject)pc);CHKERRQ(ierr);
if (!((PetscObject)pc)->type_name) {
ierr = PCGetDefaultType_Private(pc,&def);CHKERRQ(ierr);
} else {
def = ((PetscObject)pc)->type_name;
}
ierr = PetscOptionsFList("-pc_type","Preconditioner","PCSetType",PCList,def,type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PCSetType(pc,type);CHKERRQ(ierr);
} else if (!((PetscObject)pc)->type_name) {
ierr = PCSetType(pc,def);CHKERRQ(ierr);
}
ierr = PetscObjectTypeCompare((PetscObject)pc,PCNONE,&flg);CHKERRQ(ierr);
if (flg) goto skipoptions;
ierr = PetscOptionsBool("-pc_use_amat","use Amat (instead of Pmat) to define preconditioner in nested inner solves","PCSetUseAmat",pc->useAmat,&pc->useAmat,NULL);CHKERRQ(ierr);
if (pc->ops->setfromoptions) {
ierr = (*pc->ops->setfromoptions)(PetscOptionsObject,pc);CHKERRQ(ierr);
}
skipoptions:
/* process any options handlers added with PetscObjectAddOptionsHandler() */
ierr = PetscObjectProcessOptionsHandlers((PetscObject)pc);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
pc->setfromoptionscalled++;
PetscFunctionReturn(0);
}
/*@
PFSetFromOptions - Sets PF options from the options database.
Collective on PF
Input Parameters:
. pf - the mathematical function context
Options Database Keys:
Notes:
To see all options, run your program with the -help option
or consult the users manual.
Level: intermediate
.keywords: PF, set, from, options, database
.seealso:
@*/
PetscErrorCode PFSetFromOptions(PF pf)
{
PetscErrorCode ierr;
char type[256];
PetscBool flg;
PetscFunctionBegin;
PetscValidHeaderSpecific(pf,PF_CLASSID,1);
ierr = PetscObjectOptionsBegin((PetscObject)pf);CHKERRQ(ierr);
ierr = PetscOptionsFList("-pf_type","Type of function","PFSetType",PFList,0,type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PFSetType(pf,type,NULL);CHKERRQ(ierr);
}
if (pf->ops->setfromoptions) {
ierr = (*pf->ops->setfromoptions)(PetscOptionsObject,pf);CHKERRQ(ierr);
}
/* process any options handlers added with PetscObjectAddOptionsHandler() */
ierr = PetscObjectProcessOptionsHandlers(PetscOptionsObject,(PetscObject)pf);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
TaoLineSearchSetFromOptions - Sets various TaoLineSearch parameters from user
options.
Collective on TaoLineSearch
Input Paremeter:
. ls - the TaoLineSearch context
Options Database Keys:
+ -tao_ls_type <type> - The algorithm that TAO uses (more-thuente, gpcg, unit)
. -tao_ls_ftol <tol> - tolerance for sufficient decrease
. -tao_ls_gtol <tol> - tolerance for curvature condition
. -tao_ls_rtol <tol> - relative tolerance for acceptable step
. -tao_ls_stepmin <step> - minimum steplength allowed
. -tao_ls_stepmax <step> - maximum steplength allowed
. -tao_ls_max_funcs <n> - maximum number of function evaluations allowed
- -tao_ls_view - display line-search results to standard output
Level: beginner
@*/
PetscErrorCode TaoLineSearchSetFromOptions(TaoLineSearch ls)
{
PetscErrorCode ierr;
const char *default_type=TAOLINESEARCHMT;
char type[256];
PetscBool flg;
PetscFunctionBegin;
PetscValidHeaderSpecific(ls,TAOLINESEARCH_CLASSID,1);
ierr = PetscObjectOptionsBegin((PetscObject)ls);CHKERRQ(ierr);
if (!TaoLineSearchInitialized) {
ierr = TaoLineSearchInitializePackage();CHKERRQ(ierr);
}
if (((PetscObject)ls)->type_name) {
default_type = ((PetscObject)ls)->type_name;
}
/* Check for type from options */
ierr = PetscOptionsFList("-tao_ls_type","Tao Line Search type","TaoLineSearchSetType",TaoLineSearchList,default_type,type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = TaoLineSearchSetType(ls,type);CHKERRQ(ierr);
} else if (!((PetscObject)ls)->type_name) {
ierr = TaoLineSearchSetType(ls,default_type);
}
ierr = PetscOptionsInt("-tao_ls_max_funcs","max function evals in line search","",ls->max_funcs,&ls->max_funcs,0);CHKERRQ(ierr);
ierr = PetscOptionsReal("-tao_ls_ftol","tol for sufficient decrease","",ls->ftol,&ls->ftol,0);CHKERRQ(ierr);
ierr = PetscOptionsReal("-tao_ls_gtol","tol for curvature condition","",ls->gtol,&ls->gtol,0);CHKERRQ(ierr);
ierr = PetscOptionsReal("-tao_ls_rtol","relative tol for acceptable step","",ls->rtol,&ls->rtol,0);CHKERRQ(ierr);
ierr = PetscOptionsReal("-tao_ls_stepmin","lower bound for step","",ls->stepmin,&ls->stepmin,0);CHKERRQ(ierr);
ierr = PetscOptionsReal("-tao_ls_stepmax","upper bound for step","",ls->stepmax,&ls->stepmax,0);CHKERRQ(ierr);
ierr = PetscOptionsBool("-tao_ls_view","view TaoLineSearch info after each line search has completed","TaoLineSearchView",PETSC_FALSE,&ls->viewls,NULL);CHKERRQ(ierr);
if (ls->ops->setfromoptions) {
ierr = (*ls->ops->setfromoptions)(ls);CHKERRQ(ierr);
}
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@C
AOSetFromOptions - Sets AO options from the options database.
Collective on AO
Input Parameter:
. ao - the application ordering
Level: beginner
.keywords: AO, options, database
.seealso: AOCreate(), AOSetType(), AODestroy(), AOPetscToApplication(), AOApplicationToPetsc()
@*/
PetscErrorCode AOSetFromOptions(AO ao)
{
PetscErrorCode ierr;
char type[256];
const char *def=AOBASIC;
PetscBool flg;
PetscFunctionBegin;
PetscValidHeaderSpecific(ao,AO_CLASSID,1);
ierr = PetscObjectOptionsBegin((PetscObject)ao);CHKERRQ(ierr);
ierr = PetscOptionsList("-ao_type","AO type","AOSetType",AOList,def,type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = AOSetType(ao,type);CHKERRQ(ierr);
} else if (!((PetscObject)ao)->type_name){
ierr = AOSetType(ao,def);CHKERRQ(ierr);
}
/* not used here, but called so will go into help messaage */
ierr = PetscOptionsName("-ao_view","Print detailed information on AO used","AOView",0);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
KSPSetFromOptions - Sets KSP options from the options database.
This routine must be called before KSPSetUp() if the user is to be
allowed to set the Krylov type.
Collective on KSP
Input Parameters:
. ksp - the Krylov space context
Options Database Keys:
+ -ksp_max_it - maximum number of linear iterations
. -ksp_rtol rtol - relative tolerance used in default determination of convergence, i.e.
if residual norm decreases by this factor than convergence is declared
. -ksp_atol abstol - absolute tolerance used in default convergence test, i.e. if residual
norm is less than this then convergence is declared
. -ksp_divtol tol - if residual norm increases by this factor than divergence is declared
. -ksp_converged_use_initial_residual_norm - see KSPConvergedDefaultSetUIRNorm()
. -ksp_converged_use_min_initial_residual_norm - see KSPConvergedDefaultSetUMIRNorm()
. -ksp_norm_type - none - skip norms used in convergence tests (useful only when not using
convergence test (say you always want to run with 5 iterations) to
save on communication overhead
preconditioned - default for left preconditioning
unpreconditioned - see KSPSetNormType()
natural - see KSPSetNormType()
. -ksp_check_norm_iteration it - do not compute residual norm until iteration number it (does compute at 0th iteration)
works only for PCBCGS, PCIBCGS and and PCCG
. -ksp_lag_norm - compute the norm of the residual for the ith iteration on the i+1 iteration; this means that one can use
the norm of the residual for convergence test WITHOUT an extra MPI_Allreduce() limiting global synchronizations.
This will require 1 more iteration of the solver than usual.
. -ksp_guess_type - Type of initial guess generator for repeated linear solves
. -ksp_fischer_guess <model,size> - uses the Fischer initial guess generator for repeated linear solves
. -ksp_constant_null_space - assume the operator (matrix) has the constant vector in its null space
. -ksp_test_null_space - tests the null space set with MatSetNullSpace() to see if it truly is a null space
. -ksp_knoll - compute initial guess by applying the preconditioner to the right hand side
. -ksp_monitor_cancel - cancel all previous convergene monitor routines set
. -ksp_monitor <optional filename> - print residual norm at each iteration
. -ksp_monitor_lg_residualnorm - plot residual norm at each iteration
. -ksp_monitor_solution [ascii binary or draw][:filename][:format option] - plot solution at each iteration
- -ksp_monitor_singular_value - monitor extreme singular values at each iteration
Notes:
To see all options, run your program with the -help option
or consult Users-Manual: ch_ksp
Level: beginner
.keywords: KSP, set, from, options, database
.seealso: KSPSetOptionsPrefix(), KSPResetFromOptions(), KSPSetUseFischerGuess()
@*/
PetscErrorCode KSPSetFromOptions(KSP ksp)
{
PetscInt indx;
const char *convtests[] = {"default","skip","lsqr"};
char type[256], guesstype[256], monfilename[PETSC_MAX_PATH_LEN];
PetscBool flg,flag,reuse,set;
PetscInt model[2]={0,0},nmax;
KSPNormType normtype;
PCSide pcside;
void *ctx;
MPI_Comm comm;
const char *prefix;
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(ksp,KSP_CLASSID,1);
ierr = PetscObjectGetComm((PetscObject) ksp, &comm);CHKERRQ(ierr);
ierr = PetscObjectGetOptionsPrefix((PetscObject) ksp, &prefix);CHKERRQ(ierr);
if (!ksp->skippcsetfromoptions) {
if (!ksp->pc) {ierr = KSPGetPC(ksp,&ksp->pc);CHKERRQ(ierr);}
ierr = PCSetFromOptions(ksp->pc);CHKERRQ(ierr);
}
ierr = KSPRegisterAll();CHKERRQ(ierr);
ierr = PetscObjectOptionsBegin((PetscObject)ksp);CHKERRQ(ierr);
ierr = PetscOptionsFList("-ksp_type","Krylov method","KSPSetType",KSPList,(char*)(((PetscObject)ksp)->type_name ? ((PetscObject)ksp)->type_name : KSPGMRES),type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetType(ksp,type);CHKERRQ(ierr);
}
/*
Set the type if it was never set.
*/
if (!((PetscObject)ksp)->type_name) {
ierr = KSPSetType(ksp,KSPGMRES);CHKERRQ(ierr);
}
ierr = KSPResetViewers(ksp);CHKERRQ(ierr);
ierr = PetscObjectTypeCompare((PetscObject)ksp,KSPPREONLY,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PCGetReusePreconditioner(ksp->pc,&reuse);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_error_if_not_converged","Generate error if solver does not converge","KSPSetErrorIfNotConverged",ksp->errorifnotconverged,&ksp->errorifnotconverged,NULL);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_reuse_preconditioner","Use initial preconditioner and don't ever compute a new one ","KSPReusePreconditioner",reuse,&reuse,NULL);CHKERRQ(ierr);
ierr = KSPSetReusePreconditioner(ksp,reuse);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view", &ksp->viewer, &ksp->format, &ksp->view);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_converged_reason", &ksp->viewerReason, &ksp->formatReason, &ksp->viewReason);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_mat", &ksp->viewerMat, &ksp->formatMat, &ksp->viewMat);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_pmat", &ksp->viewerPMat, &ksp->formatPMat, &ksp->viewPMat);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_rhs", &ksp->viewerRhs, &ksp->formatRhs, &ksp->viewRhs);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_solution", &ksp->viewerSol, &ksp->formatSol, &ksp->viewSol);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_mat_explicit", &ksp->viewerMatExp, &ksp->formatMatExp, &ksp->viewMatExp);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_final_residual", &ksp->viewerFinalRes,&ksp->formatFinalRes,&ksp->viewFinalRes);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_preconditioned_operator_explicit",&ksp->viewerPOpExp, &ksp->formatPOpExp, &ksp->viewPOpExp);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_diagonal_scale", &ksp->viewerDScale, &ksp->formatDScale, &ksp->viewDScale);CHKERRQ(ierr);
ierr = KSPGetDiagonalScale(ksp,&flag);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_diagonal_scale","Diagonal scale matrix before building preconditioner","KSPSetDiagonalScale",flag,&flag,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetDiagonalScale(ksp,flag);CHKERRQ(ierr);
}
ierr = KSPGetDiagonalScaleFix(ksp,&flag);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_diagonal_scale_fix","Fix diagonally scaled matrix after solve","KSPSetDiagonalScaleFix",flag,&flag,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetDiagonalScaleFix(ksp,flag);CHKERRQ(ierr);
}
goto skipoptions;
}
ierr = PetscOptionsInt("-ksp_max_it","Maximum number of iterations","KSPSetTolerances",ksp->max_it,&ksp->max_it,NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-ksp_rtol","Relative decrease in residual norm","KSPSetTolerances",ksp->rtol,&ksp->rtol,NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-ksp_atol","Absolute value of residual norm","KSPSetTolerances",ksp->abstol,&ksp->abstol,NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-ksp_divtol","Residual norm increase cause divergence","KSPSetTolerances",ksp->divtol,&ksp->divtol,NULL);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_converged_use_initial_residual_norm","Use initial residual norm for computing relative convergence","KSPConvergedDefaultSetUIRNorm",PETSC_FALSE,&flag,&set);CHKERRQ(ierr);
if (set && flag) {ierr = KSPConvergedDefaultSetUIRNorm(ksp);CHKERRQ(ierr);}
ierr = PetscOptionsBool("-ksp_converged_use_min_initial_residual_norm","Use minimum of initial residual norm and b for computing relative convergence","KSPConvergedDefaultSetUMIRNorm",PETSC_FALSE,&flag,&set);CHKERRQ(ierr);
if (set && flag) {ierr = KSPConvergedDefaultSetUMIRNorm(ksp);CHKERRQ(ierr);}
ierr = PetscOptionsBool("-ksp_initial_guess_nonzero","Use the contents of the solution vector for initial guess","KSPSetInitialNonzero",ksp->guess_zero ? PETSC_FALSE : PETSC_TRUE,&flag,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetInitialGuessNonzero(ksp,flag);CHKERRQ(ierr);
}
ierr = PCGetReusePreconditioner(ksp->pc,&reuse);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_reuse_preconditioner","Use initial preconditioner and don't ever compute a new one ","KSPReusePreconditioner",reuse,&reuse,NULL);CHKERRQ(ierr);
ierr = KSPSetReusePreconditioner(ksp,reuse);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_knoll","Use preconditioner applied to b for initial guess","KSPSetInitialGuessKnoll",ksp->guess_knoll,&ksp->guess_knoll,NULL);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_error_if_not_converged","Generate error if solver does not converge","KSPSetErrorIfNotConverged",ksp->errorifnotconverged,&ksp->errorifnotconverged,NULL);CHKERRQ(ierr);
ierr = PetscOptionsFList("-ksp_guess_type","Initial guess in Krylov method",NULL,KSPGuessList,NULL,guesstype,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPGetGuess(ksp,&ksp->guess);CHKERRQ(ierr);
ierr = KSPGuessSetType(ksp->guess,guesstype);CHKERRQ(ierr);
ierr = KSPGuessSetFromOptions(ksp->guess);CHKERRQ(ierr);
} else { /* old option for KSP */
nmax = 2;
ierr = PetscOptionsIntArray("-ksp_fischer_guess","Use Paul Fischer's algorithm for initial guess","KSPSetUseFischerGuess",model,&nmax,&flag);CHKERRQ(ierr);
if (flag) {
if (nmax != 2) SETERRQ(comm,PETSC_ERR_ARG_OUTOFRANGE,"Must pass in model,size as arguments");
ierr = KSPSetUseFischerGuess(ksp,model[0],model[1]);CHKERRQ(ierr);
}
}
ierr = PetscOptionsEList("-ksp_convergence_test","Convergence test","KSPSetConvergenceTest",convtests,3,"default",&indx,&flg);CHKERRQ(ierr);
if (flg) {
switch (indx) {
case 0:
ierr = KSPConvergedDefaultCreate(&ctx);CHKERRQ(ierr);
ierr = KSPSetConvergenceTest(ksp,KSPConvergedDefault,ctx,KSPConvergedDefaultDestroy);CHKERRQ(ierr);
break;
case 1: ierr = KSPSetConvergenceTest(ksp,KSPConvergedSkip,NULL,NULL);CHKERRQ(ierr); break;
case 2:
ierr = KSPConvergedDefaultCreate(&ctx);CHKERRQ(ierr);
ierr = KSPSetConvergenceTest(ksp,KSPLSQRConvergedDefault,ctx,KSPConvergedDefaultDestroy);CHKERRQ(ierr);
break;
}
}
ierr = KSPSetUpNorms_Private(ksp,PETSC_FALSE,&normtype,NULL);CHKERRQ(ierr);
ierr = PetscOptionsEnum("-ksp_norm_type","KSP Norm type","KSPSetNormType",KSPNormTypes,(PetscEnum)normtype,(PetscEnum*)&normtype,&flg);CHKERRQ(ierr);
if (flg) { ierr = KSPSetNormType(ksp,normtype);CHKERRQ(ierr); }
ierr = PetscOptionsInt("-ksp_check_norm_iteration","First iteration to compute residual norm","KSPSetCheckNormIteration",ksp->chknorm,&ksp->chknorm,NULL);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_lag_norm","Lag the calculation of the residual norm","KSPSetLagNorm",ksp->lagnorm,&flag,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetLagNorm(ksp,flag);CHKERRQ(ierr);
}
ierr = KSPGetDiagonalScale(ksp,&flag);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_diagonal_scale","Diagonal scale matrix before building preconditioner","KSPSetDiagonalScale",flag,&flag,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetDiagonalScale(ksp,flag);CHKERRQ(ierr);
}
ierr = KSPGetDiagonalScaleFix(ksp,&flag);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_diagonal_scale_fix","Fix diagonally scaled matrix after solve","KSPSetDiagonalScaleFix",flag,&flag,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetDiagonalScaleFix(ksp,flag);CHKERRQ(ierr);
}
ierr = PetscOptionsBool("-ksp_constant_null_space","Add constant null space to Krylov solver matrix","MatSetNullSpace",PETSC_FALSE,&flg,&set);CHKERRQ(ierr);
if (set && flg) {
MatNullSpace nsp;
Mat Amat;
ierr = MatNullSpaceCreate(comm,PETSC_TRUE,0,NULL,&nsp);CHKERRQ(ierr);
ierr = PCGetOperators(ksp->pc,&Amat,NULL);CHKERRQ(ierr);
if (Amat) {
ierr = MatSetNullSpace(Amat,nsp);CHKERRQ(ierr);
ierr = MatNullSpaceDestroy(&nsp);CHKERRQ(ierr);
} else SETERRQ(comm,PETSC_ERR_ARG_WRONGSTATE,"Cannot set nullspace, matrix has not yet been provided");
}
ierr = PetscOptionsBool("-ksp_monitor_cancel","Remove any hardwired monitor routines","KSPMonitorCancel",PETSC_FALSE,&flg,&set);CHKERRQ(ierr);
/* -----------------------------------------------------------------------*/
/*
Cancels all monitors hardwired into code before call to KSPSetFromOptions()
*/
if (set && flg) {
ierr = KSPMonitorCancel(ksp);CHKERRQ(ierr);
}
ierr = KSPMonitorSetFromOptions(ksp,"-ksp_monitor","Monitor the (preconditioned) residual norm","KSPMonitorDefault",KSPMonitorDefault);CHKERRQ(ierr);
ierr = KSPMonitorSetFromOptions(ksp,"-ksp_monitor_range","Monitor the percentage of large entries in the residual","KSPMonitorRange",KSPMonitorRange);CHKERRQ(ierr);
ierr = KSPMonitorSetFromOptions(ksp,"-ksp_monitor_true_residual","Monitor the unprecondiitoned residual norm","KSPMOnitorTrueResidual",KSPMonitorTrueResidualNorm);CHKERRQ(ierr);
ierr = KSPMonitorSetFromOptions(ksp,"-ksp_monitor_max","Monitor the maximum norm of the residual","KSPMonitorTrueResidualMaxNorm",KSPMonitorTrueResidualMaxNorm);CHKERRQ(ierr);
ierr = KSPMonitorSetFromOptions(ksp,"-ksp_monitor_short","Monitor preconditioned residual norm with fewer digits","KSPMonitorDefaultShort",KSPMonitorDefaultShort);CHKERRQ(ierr);
ierr = KSPMonitorSetFromOptions(ksp,"-ksp_monitor_solution","Monitor the solution","KSPMonitorSolution",KSPMonitorSolution);CHKERRQ(ierr);
ierr = KSPMonitorSetFromOptions(ksp,"-ksp_monitor_singular_value","Monitor singular values","KSPMonitorSingularValue",KSPMonitorSingularValue);CHKERRQ(ierr);
ierr = PetscOptionsHasName(NULL,((PetscObject)ksp)->prefix,"-ksp_monitor_singular_value",&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetComputeSingularValues(ksp,PETSC_TRUE);CHKERRQ(ierr);
}
ierr = PetscObjectTypeCompare((PetscObject)ksp->pc,PCKSP,&flg);CHKERRQ(ierr);
ierr = PetscObjectTypeCompare((PetscObject)ksp->pc,PCBJACOBI,&flag);CHKERRQ(ierr);
if (flg || flag) {
/* A hack for using dynamic tolerance in preconditioner */
ierr = PetscOptionsString("-sub_ksp_dynamic_tolerance","Use dynamic tolerance for PC if PC is a KSP","KSPMonitorDynamicTolerance","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
KSPDynTolCtx *scale;
ierr = PetscMalloc1(1,&scale);CHKERRQ(ierr);
scale->bnrm = -1.0;
scale->coef = 1.0;
ierr = PetscOptionsReal("-sub_ksp_dynamic_tolerance_param","Parameter of dynamic tolerance for inner PCKSP","KSPMonitorDynamicToleranceParam",scale->coef,&scale->coef,&flg);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorDynamicTolerance,scale,KSPMonitorDynamicToleranceDestroy);CHKERRQ(ierr);
}
}
/*
Calls Python function
*/
ierr = PetscOptionsString("-ksp_monitor_python","Use Python function","KSPMonitorSet",0,monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {ierr = PetscPythonMonitorSet((PetscObject)ksp,monfilename);CHKERRQ(ierr);}
/*
Graphically plots preconditioned residual norm
*/
ierr = PetscOptionsBool("-ksp_monitor_lg_residualnorm","Monitor graphically preconditioned residual norm","KSPMonitorSet",PETSC_FALSE,&flg,&set);CHKERRQ(ierr);
if (set && flg) {
PetscDrawLG ctx;
ierr = KSPMonitorLGResidualNormCreate(comm,NULL,NULL,PETSC_DECIDE,PETSC_DECIDE,400,300,&ctx);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorLGResidualNorm,ctx,(PetscErrorCode (*)(void**))PetscDrawLGDestroy);CHKERRQ(ierr);
}
/*
Graphically plots preconditioned and true residual norm
*/
ierr = PetscOptionsBool("-ksp_monitor_lg_true_residualnorm","Monitor graphically true residual norm","KSPMonitorSet",PETSC_FALSE,&flg,&set);CHKERRQ(ierr);
if (set && flg) {
PetscDrawLG ctx;
ierr = KSPMonitorLGTrueResidualNormCreate(comm,NULL,NULL,PETSC_DECIDE,PETSC_DECIDE,400,300,&ctx);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorLGTrueResidualNorm,ctx,(PetscErrorCode (*)(void**))PetscDrawLGDestroy);CHKERRQ(ierr);
}
/*
Graphically plots preconditioned residual norm and range of residual element values
*/
ierr = PetscOptionsBool("-ksp_monitor_lg_range","Monitor graphically range of preconditioned residual norm","KSPMonitorSet",PETSC_FALSE,&flg,&set);CHKERRQ(ierr);
if (set && flg) {
PetscViewer ctx;
ierr = PetscViewerDrawOpen(comm,NULL,NULL,PETSC_DECIDE,PETSC_DECIDE,400,300,&ctx);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorLGRange,ctx,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
}
/* TODO Do these show up in help? */
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view", &ksp->viewer, &ksp->format, &ksp->view);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_pre", &ksp->viewerPre, &ksp->formatPre, &ksp->viewPre);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_converged_reason", &ksp->viewerReason, &ksp->formatReason, &ksp->viewReason);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_mat", &ksp->viewerMat, &ksp->formatMat, &ksp->viewMat);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_pmat", &ksp->viewerPMat, &ksp->formatPMat, &ksp->viewPMat);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_rhs", &ksp->viewerRhs, &ksp->formatRhs, &ksp->viewRhs);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_solution", &ksp->viewerSol, &ksp->formatSol, &ksp->viewSol);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_mat_explicit", &ksp->viewerMatExp, &ksp->formatMatExp, &ksp->viewMatExp);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_eigenvalues", &ksp->viewerEV, &ksp->formatEV, &ksp->viewEV);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_singularvalues", &ksp->viewerSV, &ksp->formatSV, &ksp->viewSV);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_eigenvalues_explicit", &ksp->viewerEVExp, &ksp->formatEVExp, &ksp->viewEVExp);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_final_residual", &ksp->viewerFinalRes,&ksp->formatFinalRes,&ksp->viewFinalRes);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_preconditioned_operator_explicit",&ksp->viewerPOpExp, &ksp->formatPOpExp, &ksp->viewPOpExp);CHKERRQ(ierr);
ierr = PetscOptionsGetViewer(comm,((PetscObject) ksp)->options,prefix,"-ksp_view_diagonal_scale", &ksp->viewerDScale, &ksp->formatDScale, &ksp->viewDScale);CHKERRQ(ierr);
/* Deprecated options */
if (!ksp->viewEV) {ierr = PetscOptionsGetViewer(comm, ((PetscObject) ksp)->options,prefix, "-ksp_compute_eigenvalues", &ksp->viewerEV, &ksp->formatEV, &ksp->viewEV);CHKERRQ(ierr);}
if (!ksp->viewEV) {
ierr = PetscOptionsName("-ksp_plot_eigenvalues", "[deprecated since PETSc 3.9; use -ksp_view_eigenvalues draw]", "KSPView", &ksp->viewEV);CHKERRQ(ierr);
if (ksp->viewEV) {
ksp->formatEV = PETSC_VIEWER_DEFAULT;
ksp->viewerEV = PETSC_VIEWER_DRAW_(comm);
ierr = PetscObjectReference((PetscObject) ksp->viewerEV);CHKERRQ(ierr);
}
}
if (!ksp->viewEV) {
ierr = PetscOptionsName("-ksp_plot_eigencontours", "[deprecated since PETSc 3.9; use -ksp_view_eigenvalues draw::draw_contour]", "KSPView", &ksp->viewEV);CHKERRQ(ierr);
if (ksp->viewEV) {
ksp->formatEV = PETSC_VIEWER_DRAW_CONTOUR;
ksp->viewerEV = PETSC_VIEWER_DRAW_(comm);
ierr = PetscObjectReference((PetscObject) ksp->viewerEV);CHKERRQ(ierr);
}
}
if (!ksp->viewEVExp) {ierr = PetscOptionsGetViewer(comm, ((PetscObject) ksp)->options,prefix, "-ksp_compute_eigenvalues_explicitly", &ksp->viewerEVExp, &ksp->formatEVExp, &ksp->viewEVExp);CHKERRQ(ierr);}
if (!ksp->viewEVExp) {
ierr = PetscOptionsName("-ksp_plot_eigenvalues_explicitly", "[deprecated since PETSc 3.9; use -ksp_view_eigenvalues_explicit draw]", "KSPView", &ksp->viewEVExp);CHKERRQ(ierr);
if (ksp->viewEVExp) {
ksp->formatEVExp = PETSC_VIEWER_DEFAULT;
ksp->viewerEVExp = PETSC_VIEWER_DRAW_(comm);
ierr = PetscObjectReference((PetscObject) ksp->viewerEVExp);CHKERRQ(ierr);
}
}
if (!ksp->viewSV) {ierr = PetscOptionsGetViewer(comm, ((PetscObject) ksp)->options,prefix, "-ksp_compute_singularvalues", &ksp->viewerSV, &ksp->formatSV, &ksp->viewSV);CHKERRQ(ierr);}
if (!ksp->viewFinalRes) {ierr = PetscOptionsGetViewer(comm, ((PetscObject) ksp)->options,prefix, "-ksp_final_residual", &ksp->viewerFinalRes, &ksp->formatFinalRes, &ksp->viewFinalRes);CHKERRQ(ierr);}
#if defined(PETSC_HAVE_SAWS)
/*
Publish convergence information using AMS
*/
ierr = PetscOptionsBool("-ksp_monitor_saws","Publish KSP progress using SAWs","KSPMonitorSet",PETSC_FALSE,&flg,&set);CHKERRQ(ierr);
if (set && flg) {
void *ctx;
ierr = KSPMonitorSAWsCreate(ksp,&ctx);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorSAWs,ctx,KSPMonitorSAWsDestroy);CHKERRQ(ierr);
ierr = KSPSetComputeSingularValues(ksp,PETSC_TRUE);CHKERRQ(ierr);
}
#endif
/* -----------------------------------------------------------------------*/
ierr = KSPSetUpNorms_Private(ksp,PETSC_FALSE,NULL,&pcside);CHKERRQ(ierr);
ierr = PetscOptionsEnum("-ksp_pc_side","KSP preconditioner side","KSPSetPCSide",PCSides,(PetscEnum)pcside,(PetscEnum*)&pcside,&flg);CHKERRQ(ierr);
if (flg) {ierr = KSPSetPCSide(ksp,pcside);CHKERRQ(ierr);}
ierr = PetscOptionsBool("-ksp_compute_singularvalues","Compute singular values of preconditioned operator","KSPSetComputeSingularValues",ksp->calc_sings,&flg,&set);CHKERRQ(ierr);
if (set) { ierr = KSPSetComputeSingularValues(ksp,flg);CHKERRQ(ierr); }
ierr = PetscOptionsBool("-ksp_compute_eigenvalues","Compute eigenvalues of preconditioned operator","KSPSetComputeSingularValues",ksp->calc_sings,&flg,&set);CHKERRQ(ierr);
if (set) { ierr = KSPSetComputeSingularValues(ksp,flg);CHKERRQ(ierr); }
ierr = PetscOptionsBool("-ksp_plot_eigenvalues","Scatter plot extreme eigenvalues","KSPSetComputeSingularValues",PETSC_FALSE,&flg,&set);CHKERRQ(ierr);
if (set) { ierr = KSPSetComputeSingularValues(ksp,flg);CHKERRQ(ierr); }
#if defined(PETSC_HAVE_SAWS)
{
PetscBool set;
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_saws_block","Block for SAWs at end of KSPSolve","PetscObjectSAWsBlock",((PetscObject)ksp)->amspublishblock,&flg,&set);CHKERRQ(ierr);
if (set) {
ierr = PetscObjectSAWsSetBlock((PetscObject)ksp,flg);CHKERRQ(ierr);
}
}
#endif
if (ksp->ops->setfromoptions) {
ierr = (*ksp->ops->setfromoptions)(PetscOptionsObject,ksp);CHKERRQ(ierr);
}
skipoptions:
/* process any options handlers added with PetscObjectAddOptionsHandler() */
ierr = PetscObjectProcessOptionsHandlers(PetscOptionsObject,(PetscObject)ksp);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
ksp->setfromoptionscalled++;
PetscFunctionReturn(0);
}
PetscErrorCode MatPtAPSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat P,PetscReal fill,Mat *C)
{
PetscErrorCode ierr;
Mat_SeqAIJ *ap,*c;
PetscInt *api,*apj,*ci,pn=P->cmap->N;
MatScalar *ca;
Mat_PtAP *ptap;
Mat Pt,AP;
PetscBool sparse_axpy=PETSC_TRUE;
PetscFunctionBegin;
ierr = PetscObjectOptionsBegin((PetscObject)A);CHKERRQ(ierr);
/* flag 'sparse_axpy' determines which implementations to be used:
0: do dense axpy in MatPtAPNumeric() - fastest, but requires storage of struct A*P;
1: do two sparse axpy in MatPtAPNumeric() - slowest, does not store structure of A*P. */
ierr = PetscOptionsBool("-matptap_scalable","Use sparse axpy but slower MatPtAPNumeric()","",sparse_axpy,&sparse_axpy,NULL);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
if (sparse_axpy) {
ierr = MatPtAPSymbolic_SeqAIJ_SeqAIJ_SparseAxpy(A,P,fill,C);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/* Get symbolic Pt = P^T */
ierr = MatTransposeSymbolic_SeqAIJ(P,&Pt);CHKERRQ(ierr);
/* Get symbolic AP = A*P */
ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(A,P,fill,&AP);CHKERRQ(ierr);
ap = (Mat_SeqAIJ*)AP->data;
api = ap->i;
apj = ap->j;
ap->free_ij = PETSC_FALSE; /* api and apj are kept in struct ptap, cannot be destroyed with AP */
/* Get C = Pt*AP */
ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(Pt,AP,fill,C);CHKERRQ(ierr);
c = (Mat_SeqAIJ*)(*C)->data;
ci = c->i;
ierr = PetscMalloc((ci[pn]+1)*sizeof(MatScalar),&ca);CHKERRQ(ierr);
ierr = PetscMemzero(ca,(ci[pn]+1)*sizeof(MatScalar));CHKERRQ(ierr);
c->a = ca;
c->free_a = PETSC_TRUE;
/* Create a supporting struct for reuse by MatPtAPNumeric() */
ierr = PetscNew(Mat_PtAP,&ptap);CHKERRQ(ierr);
c->ptap = ptap;
ptap->destroy = (*C)->ops->destroy;
(*C)->ops->destroy = MatDestroy_SeqAIJ_PtAP;
/* Allocate temporary array for storage of one row of A*P */
ierr = PetscMalloc((pn+1)*sizeof(PetscScalar),&ptap->apa);CHKERRQ(ierr);
ierr = PetscMemzero(ptap->apa,(pn+1)*sizeof(PetscScalar));CHKERRQ(ierr);
(*C)->ops->ptapnumeric = MatPtAPNumeric_SeqAIJ_SeqAIJ;
ptap->api = api;
ptap->apj = apj;
/* Clean up. */
ierr = MatDestroy(&Pt);CHKERRQ(ierr);
ierr = MatDestroy(&AP);CHKERRQ(ierr);
#if defined(PETSC_USE_INFO)
ierr = PetscInfo2((*C),"given fill %G, use scalable %d\n",fill,sparse_axpy);CHKERRQ(ierr);
#endif
PetscFunctionReturn(0);
}
/*@
EPSSetFromOptions - Sets EPS options from the options database.
This routine must be called before EPSSetUp() if the user is to be
allowed to set the solver type.
Collective on EPS
Input Parameters:
. eps - the eigensolver context
Notes:
To see all options, run your program with the -help option.
Level: beginner
@*/
PetscErrorCode EPSSetFromOptions(EPS eps)
{
PetscErrorCode ierr;
char type[256],monfilename[PETSC_MAX_PATH_LEN];
PetscBool flg,flg1,flg2,flg3;
PetscReal r,array[2]={0,0};
PetscScalar s;
PetscInt i,j,k;
PetscViewer monviewer;
SlepcConvMonitor ctx;
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
if (!EPSRegisterAllCalled) { ierr = EPSRegisterAll();CHKERRQ(ierr); }
ierr = PetscObjectOptionsBegin((PetscObject)eps);CHKERRQ(ierr);
ierr = PetscOptionsFList("-eps_type","Eigenvalue Problem Solver method","EPSSetType",EPSList,(char*)(((PetscObject)eps)->type_name?((PetscObject)eps)->type_name:EPSKRYLOVSCHUR),type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = EPSSetType(eps,type);CHKERRQ(ierr);
}
/*
Set the type if it was never set.
*/
if (!((PetscObject)eps)->type_name) {
ierr = EPSSetType(eps,EPSKRYLOVSCHUR);CHKERRQ(ierr);
}
ierr = PetscOptionsBoolGroupBegin("-eps_hermitian","hermitian eigenvalue problem","EPSSetProblemType",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetProblemType(eps,EPS_HEP);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_gen_hermitian","generalized hermitian eigenvalue problem","EPSSetProblemType",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetProblemType(eps,EPS_GHEP);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_non_hermitian","non-hermitian eigenvalue problem","EPSSetProblemType",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetProblemType(eps,EPS_NHEP);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_gen_non_hermitian","generalized non-hermitian eigenvalue problem","EPSSetProblemType",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetProblemType(eps,EPS_GNHEP);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_pos_gen_non_hermitian","generalized non-hermitian eigenvalue problem with positive semi-definite B","EPSSetProblemType",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetProblemType(eps,EPS_PGNHEP);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroupEnd("-eps_gen_indefinite","generalized hermitian-indefinite eigenvalue problem","EPSSetProblemType",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetProblemType(eps,EPS_GHIEP);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroupBegin("-eps_ritz","Rayleigh-Ritz extraction","EPSSetExtraction",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetExtraction(eps,EPS_RITZ);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_harmonic","harmonic Ritz extraction","EPSSetExtraction",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetExtraction(eps,EPS_HARMONIC);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_harmonic_relative","relative harmonic Ritz extraction","EPSSetExtraction",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetExtraction(eps,EPS_HARMONIC_RELATIVE);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_harmonic_right","right harmonic Ritz extraction","EPSSetExtraction",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetExtraction(eps,EPS_HARMONIC_RIGHT);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_harmonic_largest","largest harmonic Ritz extraction","EPSSetExtraction",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetExtraction(eps,EPS_HARMONIC_LARGEST);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_refined","refined Ritz extraction","EPSSetExtraction",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetExtraction(eps,EPS_REFINED);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroupEnd("-eps_refined_harmonic","refined harmonic Ritz extraction","EPSSetExtraction",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetExtraction(eps,EPS_REFINED_HARMONIC);CHKERRQ(ierr); }
ierr = PetscOptionsEnum("-eps_balance","Balancing method","EPSSetBalance",EPSBalanceTypes,(PetscEnum)eps->balance,(PetscEnum*)&eps->balance,NULL);CHKERRQ(ierr);
j = eps->balance_its;
ierr = PetscOptionsInt("-eps_balance_its","Number of iterations in balancing","EPSSetBalance",eps->balance_its,&j,&flg1);CHKERRQ(ierr);
r = eps->balance_cutoff;
ierr = PetscOptionsReal("-eps_balance_cutoff","Cutoff value in balancing","EPSSetBalance",eps->balance_cutoff,&r,&flg2);CHKERRQ(ierr);
if (flg1 || flg2) {
ierr = EPSSetBalance(eps,eps->balance,j,r);CHKERRQ(ierr);
}
i = eps->max_it? eps->max_it: PETSC_DEFAULT;
ierr = PetscOptionsInt("-eps_max_it","Maximum number of iterations","EPSSetTolerances",eps->max_it,&i,&flg1);CHKERRQ(ierr);
r = eps->tol;
ierr = PetscOptionsReal("-eps_tol","Tolerance","EPSSetTolerances",eps->tol==PETSC_DEFAULT?SLEPC_DEFAULT_TOL:eps->tol,&r,&flg2);CHKERRQ(ierr);
if (flg1 || flg2) {
ierr = EPSSetTolerances(eps,r,i);CHKERRQ(ierr);
}
ierr = PetscOptionsBoolGroupBegin("-eps_conv_eig","Relative error convergence test","EPSSetConvergenceTest",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetConvergenceTest(eps,EPS_CONV_EIG);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_conv_norm","Convergence test relative to the eigenvalue and the matrix norms","EPSSetConvergenceTest",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetConvergenceTest(eps,EPS_CONV_NORM);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_conv_abs","Absolute error convergence test","EPSSetConvergenceTest",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetConvergenceTest(eps,EPS_CONV_ABS);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroupEnd("-eps_conv_user","User-defined convergence test","EPSSetConvergenceTest",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetConvergenceTest(eps,EPS_CONV_USER);CHKERRQ(ierr); }
i = eps->nev;
ierr = PetscOptionsInt("-eps_nev","Number of eigenvalues to compute","EPSSetDimensions",eps->nev,&i,&flg1);CHKERRQ(ierr);
j = eps->ncv? eps->ncv: PETSC_DEFAULT;
ierr = PetscOptionsInt("-eps_ncv","Number of basis vectors","EPSSetDimensions",eps->ncv,&j,&flg2);CHKERRQ(ierr);
k = eps->mpd? eps->mpd: PETSC_DEFAULT;
ierr = PetscOptionsInt("-eps_mpd","Maximum dimension of projected problem","EPSSetDimensions",eps->mpd,&k,&flg3);CHKERRQ(ierr);
if (flg1 || flg2 || flg3) {
ierr = EPSSetDimensions(eps,i,j,k);CHKERRQ(ierr);
}
/* -----------------------------------------------------------------------*/
/*
Cancels all monitors hardwired into code before call to EPSSetFromOptions()
*/
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-eps_monitor_cancel","Remove any hardwired monitor routines","EPSMonitorCancel",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
ierr = EPSMonitorCancel(eps);CHKERRQ(ierr);
}
/*
Prints approximate eigenvalues and error estimates at each iteration
*/
ierr = PetscOptionsString("-eps_monitor","Monitor first unconverged approximate eigenvalue and error estimate","EPSMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)eps),monfilename,&monviewer);CHKERRQ(ierr);
ierr = EPSMonitorSet(eps,EPSMonitorFirst,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
}
ierr = PetscOptionsString("-eps_monitor_conv","Monitor approximate eigenvalues and error estimates as they converge","EPSMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscNew(&ctx);CHKERRQ(ierr);
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)eps),monfilename,&ctx->viewer);CHKERRQ(ierr);
ierr = EPSMonitorSet(eps,EPSMonitorConverged,ctx,(PetscErrorCode (*)(void**))SlepcConvMonitorDestroy);CHKERRQ(ierr);
}
ierr = PetscOptionsString("-eps_monitor_all","Monitor approximate eigenvalues and error estimates","EPSMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)eps),monfilename,&monviewer);CHKERRQ(ierr);
ierr = EPSMonitorSet(eps,EPSMonitorAll,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
ierr = EPSSetTrackAll(eps,PETSC_TRUE);CHKERRQ(ierr);
}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-eps_monitor_lg","Monitor first unconverged approximate eigenvalue and error estimate graphically","EPSMonitorSet",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
ierr = EPSMonitorSet(eps,EPSMonitorLG,NULL,NULL);CHKERRQ(ierr);
}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-eps_monitor_lg_all","Monitor error estimates graphically","EPSMonitorSet",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
ierr = EPSMonitorSet(eps,EPSMonitorLGAll,NULL,NULL);CHKERRQ(ierr);
ierr = EPSSetTrackAll(eps,PETSC_TRUE);CHKERRQ(ierr);
}
/* -----------------------------------------------------------------------*/
ierr = PetscOptionsBoolGroupBegin("-eps_largest_magnitude","compute largest eigenvalues in magnitude","EPSSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetWhichEigenpairs(eps,EPS_LARGEST_MAGNITUDE);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_smallest_magnitude","compute smallest eigenvalues in magnitude","EPSSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetWhichEigenpairs(eps,EPS_SMALLEST_MAGNITUDE);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_largest_real","compute largest real parts","EPSSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetWhichEigenpairs(eps,EPS_LARGEST_REAL);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_smallest_real","compute smallest real parts","EPSSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetWhichEigenpairs(eps,EPS_SMALLEST_REAL);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_largest_imaginary","compute largest imaginary parts","EPSSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetWhichEigenpairs(eps,EPS_LARGEST_IMAGINARY);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_smallest_imaginary","compute smallest imaginary parts","EPSSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetWhichEigenpairs(eps,EPS_SMALLEST_IMAGINARY);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_target_magnitude","compute nearest eigenvalues to target","EPSSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetWhichEigenpairs(eps,EPS_TARGET_MAGNITUDE);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_target_real","compute eigenvalues with real parts close to target","EPSSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetWhichEigenpairs(eps,EPS_TARGET_REAL);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-eps_target_imaginary","compute eigenvalues with imaginary parts close to target","EPSSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetWhichEigenpairs(eps,EPS_TARGET_IMAGINARY);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroupEnd("-eps_all","compute all eigenvalues in an interval","EPSSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = EPSSetWhichEigenpairs(eps,EPS_ALL);CHKERRQ(ierr); }
ierr = PetscOptionsScalar("-eps_target","Value of the target","EPSSetTarget",eps->target,&s,&flg);CHKERRQ(ierr);
if (flg) {
if (eps->which!=EPS_TARGET_REAL && eps->which!=EPS_TARGET_IMAGINARY) {
ierr = EPSSetWhichEigenpairs(eps,EPS_TARGET_MAGNITUDE);CHKERRQ(ierr);
}
ierr = EPSSetTarget(eps,s);CHKERRQ(ierr);
}
k = 2;
ierr = PetscOptionsRealArray("-eps_interval","Computational interval (two real values separated with a comma without spaces)","EPSSetInterval",array,&k,&flg);CHKERRQ(ierr);
if (flg) {
if (k<2) SETERRQ(PetscObjectComm((PetscObject)eps),PETSC_ERR_ARG_SIZ,"Must pass two values in -eps_interval (comma-separated without spaces)");
ierr = EPSSetWhichEigenpairs(eps,EPS_ALL);CHKERRQ(ierr);
ierr = EPSSetInterval(eps,array[0],array[1]);CHKERRQ(ierr);
}
ierr = PetscOptionsBool("-eps_true_residual","Compute true residuals explicitly","EPSSetTrueResidual",eps->trueres,&eps->trueres,NULL);CHKERRQ(ierr);
ierr = PetscOptionsName("-eps_view","Print detailed information on solver used","EPSView",0);CHKERRQ(ierr);
ierr = PetscOptionsName("-eps_plot_eigs","Make a plot of the computed eigenvalues","EPSSolve",0);CHKERRQ(ierr);
if (eps->ops->setfromoptions) {
ierr = (*eps->ops->setfromoptions)(eps);CHKERRQ(ierr);
}
ierr = PetscObjectProcessOptionsHandlers((PetscObject)eps);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
if (!eps->V) { ierr = EPSGetBV(eps,&eps->V);CHKERRQ(ierr); }
ierr = BVSetFromOptions(eps->V);CHKERRQ(ierr);
if (!eps->rg) { ierr = EPSGetRG(eps,&eps->rg);CHKERRQ(ierr); }
ierr = RGSetFromOptions(eps->rg);CHKERRQ(ierr);
if (!eps->ds) { ierr = EPSGetDS(eps,&eps->ds);CHKERRQ(ierr); }
ierr = DSSetFromOptions(eps->ds);CHKERRQ(ierr);
ierr = STSetFromOptions(eps->st);CHKERRQ(ierr);
ierr = PetscRandomSetFromOptions(eps->rand);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
KSPSetFromOptions - Sets KSP options from the options database.
This routine must be called before KSPSetUp() if the user is to be
allowed to set the Krylov type.
Collective on KSP
Input Parameters:
. ksp - the Krylov space context
Options Database Keys:
+ -ksp_max_it - maximum number of linear iterations
. -ksp_rtol rtol - relative tolerance used in default determination of convergence, i.e.
if residual norm decreases by this factor than convergence is declared
. -ksp_atol abstol - absolute tolerance used in default convergence test, i.e. if residual
norm is less than this then convergence is declared
. -ksp_divtol tol - if residual norm increases by this factor than divergence is declared
. -ksp_converged_use_initial_residual_norm - see KSPConvergedDefaultSetUIRNorm()
. -ksp_converged_use_min_initial_residual_norm - see KSPConvergedDefaultSetUMIRNorm()
. -ksp_norm_type - none - skip norms used in convergence tests (useful only when not using
convergence test (say you always want to run with 5 iterations) to
save on communication overhead
preconditioned - default for left preconditioning
unpreconditioned - see KSPSetNormType()
natural - see KSPSetNormType()
. -ksp_check_norm_iteration it - do not compute residual norm until iteration number it (does compute at 0th iteration)
works only for PCBCGS, PCIBCGS and and PCCG
. -ksp_lag_norm - compute the norm of the residual for the ith iteration on the i+1 iteration; this means that one can use
the norm of the residual for convergence test WITHOUT an extra MPI_Allreduce() limiting global synchronizations.
This will require 1 more iteration of the solver than usual.
. -ksp_fischer_guess <model,size> - uses the Fischer initial guess generator for repeated linear solves
. -ksp_constant_null_space - assume the operator (matrix) has the constant vector in its null space
. -ksp_test_null_space - tests the null space set with KSPSetNullSpace() to see if it truly is a null space
. -ksp_knoll - compute initial guess by applying the preconditioner to the right hand side
. -ksp_monitor_cancel - cancel all previous convergene monitor routines set
. -ksp_monitor <optional filename> - print residual norm at each iteration
. -ksp_monitor_lg_residualnorm - plot residual norm at each iteration
. -ksp_monitor_solution - plot solution at each iteration
- -ksp_monitor_singular_value - monitor extremem singular values at each iteration
Notes:
To see all options, run your program with the -help option
or consult Users-Manual: ch_ksp
Level: beginner
.keywords: KSP, set, from, options, database
.seealso: KSPSetUseFischerGuess()
@*/
PetscErrorCode KSPSetFromOptions(KSP ksp)
{
PetscErrorCode ierr;
PetscInt indx;
const char *convtests[] = {"default","skip"};
char type[256], monfilename[PETSC_MAX_PATH_LEN];
PetscViewer monviewer;
PetscBool flg,flag,reuse;
PetscInt model[2]={0,0},nmax;
KSPNormType normtype;
PCSide pcside;
void *ctx;
PetscFunctionBegin;
PetscValidHeaderSpecific(ksp,KSP_CLASSID,1);
if (!ksp->pc) {ierr = KSPGetPC(ksp,&ksp->pc);CHKERRQ(ierr);}
ierr = PCSetFromOptions(ksp->pc);CHKERRQ(ierr);
if (!KSPRegisterAllCalled) {ierr = KSPRegisterAll();CHKERRQ(ierr);}
ierr = PetscObjectOptionsBegin((PetscObject)ksp);CHKERRQ(ierr);
ierr = PetscOptionsFList("-ksp_type","Krylov method","KSPSetType",KSPList,(char*)(((PetscObject)ksp)->type_name ? ((PetscObject)ksp)->type_name : KSPGMRES),type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetType(ksp,type);CHKERRQ(ierr);
}
/*
Set the type if it was never set.
*/
if (!((PetscObject)ksp)->type_name) {
ierr = KSPSetType(ksp,KSPGMRES);CHKERRQ(ierr);
}
ierr = PetscOptionsInt("-ksp_max_it","Maximum number of iterations","KSPSetTolerances",ksp->max_it,&ksp->max_it,NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-ksp_rtol","Relative decrease in residual norm","KSPSetTolerances",ksp->rtol,&ksp->rtol,NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-ksp_atol","Absolute value of residual norm","KSPSetTolerances",ksp->abstol,&ksp->abstol,NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-ksp_divtol","Residual norm increase cause divergence","KSPSetTolerances",ksp->divtol,&ksp->divtol,NULL);CHKERRQ(ierr);
flag = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_converged_use_initial_residual_norm","Use initial residual residual norm for computing relative convergence","KSPConvergedDefaultSetUIRNorm",flag,&flag,NULL);CHKERRQ(ierr);
if (flag) {ierr = KSPConvergedDefaultSetUIRNorm(ksp);CHKERRQ(ierr);}
flag = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_converged_use_min_initial_residual_norm","Use minimum of initial residual norm and b for computing relative convergence","KSPConvergedDefaultSetUMIRNorm",flag,&flag,NULL);CHKERRQ(ierr);
if (flag) {ierr = KSPConvergedDefaultSetUMIRNorm(ksp);CHKERRQ(ierr);}
ierr = KSPGetInitialGuessNonzero(ksp,&flag);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_initial_guess_nonzero","Use the contents of the solution vector for initial guess","KSPSetInitialNonzero",flag,&flag,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetInitialGuessNonzero(ksp,flag);CHKERRQ(ierr);
}
ierr = PCGetReusePreconditioner(ksp->pc,&reuse);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_reuse_preconditioner","Use initial preconditioner and don't ever compute a new one ","KSPReusePreconditioner",reuse,&reuse,NULL);CHKERRQ(ierr);
ierr = KSPSetReusePreconditioner(ksp,reuse);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_knoll","Use preconditioner applied to b for initial guess","KSPSetInitialGuessKnoll",ksp->guess_knoll,&ksp->guess_knoll,NULL);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_error_if_not_converged","Generate error if solver does not converge","KSPSetErrorIfNotConverged",ksp->errorifnotconverged,&ksp->errorifnotconverged,NULL);CHKERRQ(ierr);
nmax = 2;
ierr = PetscOptionsIntArray("-ksp_fischer_guess","Use Paul Fischer's algorithm for initial guess","KSPSetUseFischerGuess",model,&nmax,&flag);CHKERRQ(ierr);
if (flag) {
if (nmax != 2) SETERRQ(PetscObjectComm((PetscObject)ksp),PETSC_ERR_ARG_OUTOFRANGE,"Must pass in model,size as arguments");
ierr = KSPSetUseFischerGuess(ksp,model[0],model[1]);CHKERRQ(ierr);
}
ierr = PetscOptionsEList("-ksp_convergence_test","Convergence test","KSPSetConvergenceTest",convtests,2,"default",&indx,&flg);CHKERRQ(ierr);
if (flg) {
switch (indx) {
case 0:
ierr = KSPConvergedDefaultCreate(&ctx);CHKERRQ(ierr);
ierr = KSPSetConvergenceTest(ksp,KSPConvergedDefault,ctx,KSPConvergedDefaultDestroy);CHKERRQ(ierr);
break;
case 1: ierr = KSPSetConvergenceTest(ksp,KSPConvergedSkip,NULL,NULL);CHKERRQ(ierr); break;
}
}
ierr = KSPSetUpNorms_Private(ksp,&normtype,&pcside);CHKERRQ(ierr);
ierr = PetscOptionsEnum("-ksp_norm_type","KSP Norm type","KSPSetNormType",KSPNormTypes,(PetscEnum)normtype,(PetscEnum*)&normtype,&flg);CHKERRQ(ierr);
if (flg) { ierr = KSPSetNormType(ksp,normtype);CHKERRQ(ierr); }
ierr = PetscOptionsInt("-ksp_check_norm_iteration","First iteration to compute residual norm","KSPSetCheckNormIteration",ksp->chknorm,&ksp->chknorm,NULL);CHKERRQ(ierr);
flag = ksp->lagnorm;
ierr = PetscOptionsBool("-ksp_lag_norm","Lag the calculation of the residual norm","KSPSetLagNorm",flag,&flag,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetLagNorm(ksp,flag);CHKERRQ(ierr);
}
ierr = KSPGetDiagonalScale(ksp,&flag);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_diagonal_scale","Diagonal scale matrix before building preconditioner","KSPSetDiagonalScale",flag,&flag,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetDiagonalScale(ksp,flag);CHKERRQ(ierr);
}
ierr = KSPGetDiagonalScaleFix(ksp,&flag);CHKERRQ(ierr);
ierr = PetscOptionsBool("-ksp_diagonal_scale_fix","Fix diagonally scaled matrix after solve","KSPSetDiagonalScaleFix",flag,&flag,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetDiagonalScaleFix(ksp,flag);CHKERRQ(ierr);
}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_constant_null_space","Add constant null space to Krylov solver","KSPSetNullSpace",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
MatNullSpace nsp;
ierr = MatNullSpaceCreate(PetscObjectComm((PetscObject)ksp),PETSC_TRUE,0,0,&nsp);CHKERRQ(ierr);
ierr = KSPSetNullSpace(ksp,nsp);CHKERRQ(ierr);
ierr = MatNullSpaceDestroy(&nsp);CHKERRQ(ierr);
}
/* option is actually checked in KSPSetUp(), just here so goes into help message */
if (ksp->nullsp) {
ierr = PetscOptionsName("-ksp_test_null_space","Is provided null space correct","None",&flg);CHKERRQ(ierr);
}
/*
Prints reason for convergence or divergence of each linear solve
*/
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_converged_reason","Print reason for converged or diverged","KSPSolve",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) ksp->printreason = PETSC_TRUE;
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_monitor_cancel","Remove any hardwired monitor routines","KSPMonitorCancel",flg,&flg,NULL);CHKERRQ(ierr);
/* -----------------------------------------------------------------------*/
/*
Cancels all monitors hardwired into code before call to KSPSetFromOptions()
*/
if (flg) {
ierr = KSPMonitorCancel(ksp);CHKERRQ(ierr);
}
/*
Prints preconditioned residual norm at each iteration
*/
ierr = PetscOptionsString("-ksp_monitor","Monitor preconditioned residual norm","KSPMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)ksp),monfilename,&monviewer);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorDefault,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
}
/*
Prints preconditioned residual norm at each iteration
*/
ierr = PetscOptionsString("-ksp_monitor_range","Monitor percent of residual entries more than 10 percent of max","KSPMonitorRange","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)ksp),monfilename,&monviewer);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorRange,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
}
ierr = PetscObjectTypeCompare((PetscObject)ksp->pc,PCKSP,&flg);CHKERRQ(ierr);
ierr = PetscObjectTypeCompare((PetscObject)ksp->pc,PCBJACOBI,&flag);CHKERRQ(ierr);
if (flg || flag) {
/* A hack for using dynamic tolerance in preconditioner */
ierr = PetscOptionsString("-sub_ksp_dynamic_tolerance","Use dynamic tolerance for PC if PC is a KSP","KSPMonitorDynamicTolerance","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
KSPDynTolCtx *scale = NULL;
PetscReal defaultv = 1.0;
ierr = PetscMalloc1(1,&scale);CHKERRQ(ierr);
scale->bnrm = -1.0;
scale->coef = defaultv;
ierr = PetscOptionsReal("-sub_ksp_dynamic_tolerance_param","Parameter of dynamic tolerance for inner PCKSP","KSPMonitorDynamicToleranceParam",defaultv,&(scale->coef),&flg);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorDynamicTolerance,scale,KSPMonitorDynamicToleranceDestroy);CHKERRQ(ierr);
}
}
/*
Plots the vector solution
*/
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_monitor_solution","Monitor solution graphically","KSPMonitorSet",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
ierr = KSPMonitorSet(ksp,KSPMonitorSolution,NULL,NULL);CHKERRQ(ierr);
}
/*
Prints preconditioned and true residual norm at each iteration
*/
ierr = PetscOptionsString("-ksp_monitor_true_residual","Monitor true residual norm","KSPMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)ksp),monfilename,&monviewer);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorTrueResidualNorm,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
}
/*
Prints with max norm at each iteration
*/
ierr = PetscOptionsString("-ksp_monitor_max","Monitor true residual max norm","KSPMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)ksp),monfilename,&monviewer);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorTrueResidualMaxNorm,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
}
/*
Prints extreme eigenvalue estimates at each iteration
*/
ierr = PetscOptionsString("-ksp_monitor_singular_value","Monitor singular values","KSPMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = KSPSetComputeSingularValues(ksp,PETSC_TRUE);CHKERRQ(ierr);
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)ksp),monfilename,&monviewer);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorSingularValue,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
}
/*
Prints preconditioned residual norm with fewer digits
*/
ierr = PetscOptionsString("-ksp_monitor_short","Monitor preconditioned residual norm with fewer digits","KSPMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)ksp),monfilename,&monviewer);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorDefaultShort,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
}
/*
Calls Python function
*/
ierr = PetscOptionsString("-ksp_monitor_python","Use Python function","KSPMonitorSet",0,monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {ierr = PetscPythonMonitorSet((PetscObject)ksp,monfilename);CHKERRQ(ierr);}
/*
Graphically plots preconditioned residual norm
*/
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_monitor_lg_residualnorm","Monitor graphically preconditioned residual norm","KSPMonitorSet",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
PetscDrawLG ctx;
ierr = KSPMonitorLGResidualNormCreate(0,0,PETSC_DECIDE,PETSC_DECIDE,300,300,&ctx);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorLGResidualNorm,ctx,(PetscErrorCode (*)(void**))KSPMonitorLGResidualNormDestroy);CHKERRQ(ierr);
}
/*
Graphically plots preconditioned and true residual norm
*/
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_monitor_lg_true_residualnorm","Monitor graphically true residual norm","KSPMonitorSet",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
PetscDrawLG ctx;
ierr = KSPMonitorLGTrueResidualNormCreate(PetscObjectComm((PetscObject)ksp),0,0,PETSC_DECIDE,PETSC_DECIDE,300,300,&ctx);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorLGTrueResidualNorm,ctx,(PetscErrorCode (*)(void**))KSPMonitorLGTrueResidualNormDestroy);CHKERRQ(ierr);
}
/*
Graphically plots preconditioned residual norm and range of residual element values
*/
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_monitor_lg_range","Monitor graphically range of preconditioned residual norm","KSPMonitorSet",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
PetscViewer ctx;
ierr = PetscViewerDrawOpen(PetscObjectComm((PetscObject)ksp),0,0,PETSC_DECIDE,PETSC_DECIDE,300,300,&ctx);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorLGRange,ctx,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
}
#if defined(PETSC_HAVE_SAWS)
/*
Publish convergence information using AMS
*/
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_monitor_saws","Publish KSP progress using SAWs","KSPMonitorSet",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
void *ctx;
ierr = KSPMonitorSAWsCreate(ksp,&ctx);CHKERRQ(ierr);
ierr = KSPMonitorSet(ksp,KSPMonitorSAWs,ctx,KSPMonitorSAWsDestroy);CHKERRQ(ierr);
ierr = KSPSetComputeSingularValues(ksp,PETSC_TRUE);CHKERRQ(ierr);
}
#endif
/* -----------------------------------------------------------------------*/
ierr = KSPSetUpNorms_Private(ksp,&normtype,&pcside);CHKERRQ(ierr);
ierr = PetscOptionsEnum("-ksp_pc_side","KSP preconditioner side","KSPSetPCSide",PCSides,(PetscEnum)pcside,(PetscEnum*)&pcside,&flg);CHKERRQ(ierr);
if (flg) {ierr = KSPSetPCSide(ksp,pcside);CHKERRQ(ierr);}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_compute_singularvalues","Compute singular values of preconditioned operator","KSPSetComputeSingularValues",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) { ierr = KSPSetComputeSingularValues(ksp,PETSC_TRUE);CHKERRQ(ierr); }
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_compute_eigenvalues","Compute eigenvalues of preconditioned operator","KSPSetComputeSingularValues",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) { ierr = KSPSetComputeSingularValues(ksp,PETSC_TRUE);CHKERRQ(ierr); }
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_plot_eigenvalues","Scatter plot extreme eigenvalues","KSPSetComputeSingularValues",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) { ierr = KSPSetComputeSingularValues(ksp,PETSC_TRUE);CHKERRQ(ierr); }
#if defined(PETSC_HAVE_SAWS)
{
PetscBool set;
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-ksp_saws_block","Block for SAWs at end of KSPSolve","PetscObjectSAWsBlock",((PetscObject)ksp)->amspublishblock,&flg,&set);CHKERRQ(ierr);
if (set) {
ierr = PetscObjectSAWsSetBlock((PetscObject)ksp,flg);CHKERRQ(ierr);
}
}
#endif
if (ksp->ops->setfromoptions) {
ierr = (*ksp->ops->setfromoptions)(ksp);CHKERRQ(ierr);
}
/* process any options handlers added with PetscObjectAddOptionsHandler() */
ierr = PetscObjectProcessOptionsHandlers((PetscObject)ksp);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
MFNSetFromOptions - Sets MFN options from the options database.
This routine must be called before MFNSetUp() if the user is to be
allowed to set the solver type.
Collective on MFN
Input Parameters:
. mfn - the matrix function context
Notes:
To see all options, run your program with the -help option.
Level: beginner
@*/
PetscErrorCode MFNSetFromOptions(MFN mfn)
{
PetscErrorCode ierr;
char type[256],monfilename[PETSC_MAX_PATH_LEN];
PetscBool flg,flg1,flg2;
PetscReal r;
PetscInt i;
PetscViewer monviewer;
PetscFunctionBegin;
PetscValidHeaderSpecific(mfn,MFN_CLASSID,1);
if (!MFNRegisterAllCalled) { ierr = MFNRegisterAll();CHKERRQ(ierr); }
ierr = PetscObjectOptionsBegin((PetscObject)mfn);CHKERRQ(ierr);
ierr = PetscOptionsFList("-mfn_type","Matrix Function method","MFNSetType",MFNList,(char*)(((PetscObject)mfn)->type_name?((PetscObject)mfn)->type_name:MFNKRYLOV),type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = MFNSetType(mfn,type);CHKERRQ(ierr);
}
/*
Set the type if it was never set.
*/
if (!((PetscObject)mfn)->type_name) {
ierr = MFNSetType(mfn,MFNKRYLOV);CHKERRQ(ierr);
}
ierr = PetscOptionsBoolGroupBegin("-mfn_exp","matrix exponential","MFNSetFunction",&flg);CHKERRQ(ierr);
if (flg) {
ierr = MFNSetFunction(mfn,SLEPC_FUNCTION_EXP);CHKERRQ(ierr);
}
ierr = PetscOptionsScalar("-mfn_scale","Scale factor","MFNSetScaleFactor",mfn->sfactor,&mfn->sfactor,NULL);CHKERRQ(ierr);
i = mfn->max_it;
ierr = PetscOptionsInt("-mfn_max_it","Maximum number of iterations","MFNSetTolerances",mfn->max_it,&i,&flg1);CHKERRQ(ierr);
r = mfn->tol;
ierr = PetscOptionsReal("-mfn_tol","Tolerance","MFNSetTolerances",mfn->tol==PETSC_DEFAULT?SLEPC_DEFAULT_TOL:mfn->tol,&r,&flg2);CHKERRQ(ierr);
if (flg1 || flg2) {
ierr = MFNSetTolerances(mfn,r,i);CHKERRQ(ierr);
}
ierr = PetscOptionsInt("-mfn_ncv","Number of basis vectors","MFNSetDimensions",mfn->ncv,&i,&flg);CHKERRQ(ierr);
if (flg) {
ierr = MFNSetDimensions(mfn,i);CHKERRQ(ierr);
}
ierr = PetscOptionsBool("-mfn_error_if_not_converged","Generate error if solver does not converge","MFNSetErrorIfNotConverged",mfn->errorifnotconverged,&mfn->errorifnotconverged,NULL);CHKERRQ(ierr);
/* -----------------------------------------------------------------------*/
/*
Cancels all monitors hardwired into code before call to MFNSetFromOptions()
*/
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-mfn_monitor_cancel","Remove any hardwired monitor routines","MFNMonitorCancel",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
ierr = MFNMonitorCancel(mfn);CHKERRQ(ierr);
}
/*
Prints error estimate at each iteration
*/
ierr = PetscOptionsString("-mfn_monitor","Monitor error estimate","MFNMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)mfn),monfilename,&monviewer);CHKERRQ(ierr);
ierr = MFNMonitorSet(mfn,MFNMonitorDefault,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-mfn_monitor_lg","Monitor error estimate graphically","MFNMonitorSet",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
ierr = MFNMonitorSet(mfn,MFNMonitorLG,NULL,NULL);CHKERRQ(ierr);
}
/* -----------------------------------------------------------------------*/
ierr = PetscOptionsName("-mfn_view","Print detailed information on solver used","MFNView",0);CHKERRQ(ierr);
if (mfn->ops->setfromoptions) {
ierr = (*mfn->ops->setfromoptions)(mfn);CHKERRQ(ierr);
}
ierr = PetscObjectProcessOptionsHandlers((PetscObject)mfn);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
if (!mfn->V) { ierr = MFNGetBV(mfn,&mfn->V);CHKERRQ(ierr); }
ierr = BVSetFromOptions(mfn->V);CHKERRQ(ierr);
if (!mfn->ds) { ierr = MFNGetDS(mfn,&mfn->ds);CHKERRQ(ierr); }
ierr = DSSetFromOptions(mfn->ds);CHKERRQ(ierr);
ierr = PetscRandomSetFromOptions(mfn->rand);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
PetscDrawSetFromOptions - Sets the graphics type from the options database.
Defaults to a PETSc X windows graphics.
Collective on PetscDraw
Input Parameter:
. draw - the graphics context
Options Database Keys:
+ -nox - do not use X graphics (ignore graphics calls, but run program correctly)
. -nox_warning - when X windows support is not installed this prevents the warning message from being printed
. -draw_pause <pause amount> -- -1 indicates wait for mouse input, -2 indicates pause when window is to be destroyed
. -draw_save [optional filename] - (X windows only) saves each image before it is cleared to a file
. -draw_save_final_image [optional filename] - (X windows only) saves the final image displayed in a window
. -draw_save_movie - converts image files to a movie at the end of the run. See PetscDrawSetSave()
. -draw_save_on_flush - saves an image on each flush in addition to each clear
- -draw_save_single_file - saves each new image in the same file, normally each new image is saved in a new file with filename_%d
Level: intermediate
Notes:
Must be called after PetscDrawCreate() before the PetscDraw is used.
Concepts: drawing^setting options
Concepts: graphics^setting options
.seealso: PetscDrawCreate(), PetscDrawSetType(), PetscDrawSetSave(), PetscDrawSetSaveFinalImage()
@*/
PetscErrorCode PetscDrawSetFromOptions(PetscDraw draw)
{
PetscErrorCode ierr;
PetscBool flg,nox;
char vtype[256];
const char *def;
PetscReal dpause;
#if !defined(PETSC_USE_WINDOWS_GRAPHICS) && !defined(PETSC_HAVE_X)
PetscBool warn;
#endif
PetscFunctionBegin;
PetscValidHeaderSpecific(draw,PETSC_DRAW_CLASSID,1);
if (!PetscDrawList) {
ierr = PetscDrawRegisterAll();CHKERRQ(ierr);
}
if (((PetscObject)draw)->type_name) def = ((PetscObject)draw)->type_name;
else {
ierr = PetscOptionsHasName(NULL,"-nox",&nox);CHKERRQ(ierr);
def = PETSC_DRAW_NULL;
#if defined(PETSC_USE_WINDOWS_GRAPHICS)
if (!nox) def = PETSC_DRAW_WIN32;
#elif defined(PETSC_HAVE_X)
if (!nox) def = PETSC_DRAW_X;
#elif defined(PETSC_HAVE_GLUT)
if (!nox) def = PETSC_DRAW_GLUT;
#elif defined(PETSC_HAVE_OPENGLES)
if (!nox) def = PETSC_DRAW_OPENGLES;
#else
ierr = PetscOptionsHasName(NULL,"-nox_warning",&warn);CHKERRQ(ierr);
if (!nox && !warn) (*PetscErrorPrintf)("PETSc installed without X windows, Microsoft Graphics, OpenGL ES, or GLUT/OpenGL on this machine\nproceeding without graphics\n");
#endif
}
ierr = PetscObjectOptionsBegin((PetscObject)draw);CHKERRQ(ierr);
ierr = PetscOptionsFList("-draw_type","Type of graphical output","PetscDrawSetType",PetscDrawList,def,vtype,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscDrawSetType(draw,vtype);CHKERRQ(ierr);
} else if (!((PetscObject)draw)->type_name) {
ierr = PetscDrawSetType(draw,def);CHKERRQ(ierr);
}
ierr = PetscOptionsName("-nox","Run without graphics","None",&nox);CHKERRQ(ierr);
#if defined(PETSC_HAVE_X)
{
char filename[PETSC_MAX_PATH_LEN];
PetscBool save,movie = PETSC_FALSE;
ierr = PetscOptionsBool("-draw_save_movie","Make a movie from the images saved (X Windows only)","PetscDrawSetSave",movie,&movie,NULL);CHKERRQ(ierr);
ierr = PetscOptionsBool("-draw_save_single_file","Each new image replaces previous image in file","PetscDrawSetSave",draw->savesinglefile,&draw->savesinglefile,NULL);CHKERRQ(ierr);
ierr = PetscOptionsString("-draw_save","Save graphics to file (X Windows only)","PetscDrawSetSave",filename,filename,PETSC_MAX_PATH_LEN,&save);CHKERRQ(ierr);
if (save) {
ierr = PetscDrawSetSave(draw,filename,movie);CHKERRQ(ierr);
}
ierr = PetscOptionsString("-draw_save_final_image","Save graphics to file (X Windows only)","PetscDrawSetSaveFinalImage",filename,filename,PETSC_MAX_PATH_LEN,&save);CHKERRQ(ierr);
if (save) {
ierr = PetscDrawSetSaveFinalImage(draw,filename);CHKERRQ(ierr);
}
ierr = PetscOptionsBool("-draw_save_on_flush","Save graphics to file (X Windows only) on each flush","PetscDrawSetSave",draw->saveonflush,&draw->saveonflush,NULL);CHKERRQ(ierr);
}
#endif
ierr = PetscOptionsGetReal(NULL,"-draw_pause",&dpause,&flg);CHKERRQ(ierr);
if (flg) draw->pause = dpause;
/* process any options handlers added with PetscObjectAddOptionsHandler() */
ierr = PetscObjectProcessOptionsHandlers((PetscObject)draw);CHKERRQ(ierr);
ierr = PetscDrawViewFromOptions(draw,NULL,"-draw_view");CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
PetscFunctionReturn(0);
}
PetscErrorCode PCSetUp_ML(PC pc)
{
PetscErrorCode ierr;
PetscMPIInt size;
FineGridCtx *PetscMLdata;
ML *ml_object;
ML_Aggregate *agg_object;
ML_Operator *mlmat;
PetscInt nlocal_allcols,Nlevels,mllevel,level,level1,m,fine_level,bs;
Mat A,Aloc;
GridCtx *gridctx;
PC_MG *mg = (PC_MG*)pc->data;
PC_ML *pc_ml = (PC_ML*)mg->innerctx;
PetscBool isSeq, isMPI;
KSP smoother;
PC subpc;
PetscInt mesh_level, old_mesh_level;
PetscFunctionBegin;
A = pc->pmat;
ierr = MPI_Comm_size(((PetscObject)A)->comm,&size);CHKERRQ(ierr);
if (pc->setupcalled) {
if (pc->flag == SAME_NONZERO_PATTERN && pc_ml->reuse_interpolation) {
/*
Reuse interpolaton instead of recomputing aggregates and updating the whole hierarchy. This is less expensive for
multiple solves in which the matrix is not changing too quickly.
*/
ml_object = pc_ml->ml_object;
gridctx = pc_ml->gridctx;
Nlevels = pc_ml->Nlevels;
fine_level = Nlevels - 1;
gridctx[fine_level].A = A;
ierr = PetscObjectTypeCompare((PetscObject) A, MATSEQAIJ, &isSeq);CHKERRQ(ierr);
ierr = PetscObjectTypeCompare((PetscObject) A, MATMPIAIJ, &isMPI);CHKERRQ(ierr);
if (isMPI){
ierr = MatConvert_MPIAIJ_ML(A,PETSC_NULL,MAT_INITIAL_MATRIX,&Aloc);CHKERRQ(ierr);
} else if (isSeq) {
Aloc = A;
ierr = PetscObjectReference((PetscObject)Aloc);CHKERRQ(ierr);
} else SETERRQ1(((PetscObject)pc)->comm,PETSC_ERR_ARG_WRONG, "Matrix type '%s' cannot be used with ML. ML can only handle AIJ matrices.",((PetscObject)A)->type_name);
ierr = MatGetSize(Aloc,&m,&nlocal_allcols);CHKERRQ(ierr);
PetscMLdata = pc_ml->PetscMLdata;
ierr = MatDestroy(&PetscMLdata->Aloc);CHKERRQ(ierr);
PetscMLdata->A = A;
PetscMLdata->Aloc = Aloc;
ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata);
ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec);
mesh_level = ml_object->ML_finest_level;
while (ml_object->SingleLevel[mesh_level].Rmat->to) {
old_mesh_level = mesh_level;
mesh_level = ml_object->SingleLevel[mesh_level].Rmat->to->levelnum;
/* clean and regenerate A */
mlmat = &(ml_object->Amat[mesh_level]);
ML_Operator_Clean(mlmat);
ML_Operator_Init(mlmat,ml_object->comm);
ML_Gen_AmatrixRAP(ml_object, old_mesh_level, mesh_level);
}
level = fine_level - 1;
if (size == 1) { /* convert ML P, R and A into seqaij format */
for (mllevel=1; mllevel<Nlevels; mllevel++){
mlmat = &(ml_object->Amat[mllevel]);
ierr = MatWrapML_SeqAIJ(mlmat,MAT_REUSE_MATRIX,&gridctx[level].A);CHKERRQ(ierr);
level--;
}
} else { /* convert ML P and R into shell format, ML A into mpiaij format */
for (mllevel=1; mllevel<Nlevels; mllevel++){
mlmat = &(ml_object->Amat[mllevel]);
ierr = MatWrapML_MPIAIJ(mlmat,MAT_REUSE_MATRIX,&gridctx[level].A);CHKERRQ(ierr);
level--;
}
}
for (level=0; level<fine_level; level++) {
if (level > 0){
ierr = PCMGSetResidual(pc,level,PCMGDefaultResidual,gridctx[level].A);CHKERRQ(ierr);
}
ierr = KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A,SAME_NONZERO_PATTERN);CHKERRQ(ierr);
}
ierr = PCMGSetResidual(pc,fine_level,PCMGDefaultResidual,gridctx[fine_level].A);CHKERRQ(ierr);
ierr = KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A,SAME_NONZERO_PATTERN);CHKERRQ(ierr);
ierr = PCSetUp_MG(pc);CHKERRQ(ierr);
PetscFunctionReturn(0);
} else {
/* since ML can change the size of vectors/matrices at any level we must destroy everything */
ierr = PCReset_ML(pc);CHKERRQ(ierr);
ierr = PCReset_MG(pc);CHKERRQ(ierr);
}
}
/* setup special features of PCML */
/*--------------------------------*/
/* covert A to Aloc to be used by ML at fine grid */
pc_ml->size = size;
ierr = PetscObjectTypeCompare((PetscObject) A, MATSEQAIJ, &isSeq);CHKERRQ(ierr);
ierr = PetscObjectTypeCompare((PetscObject) A, MATMPIAIJ, &isMPI);CHKERRQ(ierr);
if (isMPI){
ierr = MatConvert_MPIAIJ_ML(A,PETSC_NULL,MAT_INITIAL_MATRIX,&Aloc);CHKERRQ(ierr);
} else if (isSeq) {
Aloc = A;
ierr = PetscObjectReference((PetscObject)Aloc);CHKERRQ(ierr);
} else SETERRQ1(((PetscObject)pc)->comm,PETSC_ERR_ARG_WRONG, "Matrix type '%s' cannot be used with ML. ML can only handle AIJ matrices.",((PetscObject)A)->type_name);
/* create and initialize struct 'PetscMLdata' */
ierr = PetscNewLog(pc,FineGridCtx,&PetscMLdata);CHKERRQ(ierr);
pc_ml->PetscMLdata = PetscMLdata;
ierr = PetscMalloc((Aloc->cmap->n+1)*sizeof(PetscScalar),&PetscMLdata->pwork);CHKERRQ(ierr);
ierr = VecCreate(PETSC_COMM_SELF,&PetscMLdata->x);CHKERRQ(ierr);
ierr = VecSetSizes(PetscMLdata->x,Aloc->cmap->n,Aloc->cmap->n);CHKERRQ(ierr);
ierr = VecSetType(PetscMLdata->x,VECSEQ);CHKERRQ(ierr);
ierr = VecCreate(PETSC_COMM_SELF,&PetscMLdata->y);CHKERRQ(ierr);
ierr = VecSetSizes(PetscMLdata->y,A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr);
ierr = VecSetType(PetscMLdata->y,VECSEQ);CHKERRQ(ierr);
PetscMLdata->A = A;
PetscMLdata->Aloc = Aloc;
/* create ML discretization matrix at fine grid */
/* ML requires input of fine-grid matrix. It determines nlevels. */
ierr = MatGetSize(Aloc,&m,&nlocal_allcols);CHKERRQ(ierr);
ierr = MatGetBlockSize(A,&bs);CHKERRQ(ierr);
ML_Create(&ml_object,pc_ml->MaxNlevels);
ML_Comm_Set_UsrComm(ml_object->comm,((PetscObject)A)->comm);
pc_ml->ml_object = ml_object;
ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata);
ML_Set_Amatrix_Getrow(ml_object,0,PetscML_getrow,PetscML_comm,nlocal_allcols);
ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec);
ML_Set_Symmetrize(ml_object,pc_ml->Symmetrize ? ML_YES : ML_NO);
/* aggregation */
ML_Aggregate_Create(&agg_object);
pc_ml->agg_object = agg_object;
{
MatNullSpace mnull;
ierr = MatGetNearNullSpace(A,&mnull);CHKERRQ(ierr);
if (pc_ml->nulltype == PCML_NULLSPACE_AUTO) {
if (mnull) pc_ml->nulltype = PCML_NULLSPACE_USER;
else if (bs > 1) pc_ml->nulltype = PCML_NULLSPACE_BLOCK;
else pc_ml->nulltype = PCML_NULLSPACE_SCALAR;
}
switch (pc_ml->nulltype) {
case PCML_NULLSPACE_USER: {
PetscScalar *nullvec;
const PetscScalar *v;
PetscBool has_const;
PetscInt i,j,mlocal,nvec,M;
const Vec *vecs;
if (!mnull) SETERRQ(((PetscObject)pc)->comm,PETSC_ERR_USER,"Must provide explicit null space using MatSetNearNullSpace() to use user-specified null space");
ierr = MatGetSize(A,&M,PETSC_NULL);CHKERRQ(ierr);
ierr = MatGetLocalSize(Aloc,&mlocal,PETSC_NULL);CHKERRQ(ierr);
ierr = MatNullSpaceGetVecs(mnull,&has_const,&nvec,&vecs);CHKERRQ(ierr);
ierr = PetscMalloc((nvec+!!has_const)*mlocal*sizeof *nullvec,&nullvec);CHKERRQ(ierr);
if (has_const) for (i=0; i<mlocal; i++) nullvec[i] = 1.0/M;
for (i=0; i<nvec; i++) {
ierr = VecGetArrayRead(vecs[i],&v);CHKERRQ(ierr);
for (j=0; j<mlocal; j++) nullvec[(i+!!has_const)*mlocal + j] = v[j];
ierr = VecRestoreArrayRead(vecs[i],&v);CHKERRQ(ierr);
}
ierr = ML_Aggregate_Set_NullSpace(agg_object,bs,nvec+!!has_const,nullvec,mlocal);CHKERRQ(ierr);
ierr = PetscFree(nullvec);CHKERRQ(ierr);
} break;
case PCML_NULLSPACE_BLOCK:
ierr = ML_Aggregate_Set_NullSpace(agg_object,bs,bs,0,0);CHKERRQ(ierr);
break;
case PCML_NULLSPACE_SCALAR:
break;
default: SETERRQ(((PetscObject)pc)->comm,PETSC_ERR_SUP,"Unknown null space type");
}
}
ML_Aggregate_Set_MaxCoarseSize(agg_object,pc_ml->MaxCoarseSize);
/* set options */
switch (pc_ml->CoarsenScheme) {
case 1:
ML_Aggregate_Set_CoarsenScheme_Coupled(agg_object);break;
case 2:
ML_Aggregate_Set_CoarsenScheme_MIS(agg_object);break;
case 3:
ML_Aggregate_Set_CoarsenScheme_METIS(agg_object);break;
}
ML_Aggregate_Set_Threshold(agg_object,pc_ml->Threshold);
ML_Aggregate_Set_DampingFactor(agg_object,pc_ml->DampingFactor);
if (pc_ml->SpectralNormScheme_Anorm){
ML_Set_SpectralNormScheme_Anorm(ml_object);
}
agg_object->keep_agg_information = (int)pc_ml->KeepAggInfo;
agg_object->keep_P_tentative = (int)pc_ml->Reusable;
agg_object->block_scaled_SA = (int)pc_ml->BlockScaling;
agg_object->minimizing_energy = (int)pc_ml->EnergyMinimization;
agg_object->minimizing_energy_droptol = (double)pc_ml->EnergyMinimizationDropTol;
agg_object->cheap_minimizing_energy = (int)pc_ml->EnergyMinimizationCheap;
if (pc_ml->OldHierarchy) {
Nlevels = ML_Gen_MGHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object);
} else {
Nlevels = ML_Gen_MultiLevelHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object);
}
if (Nlevels<=0) SETERRQ1(((PetscObject)pc)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Nlevels %d must > 0",Nlevels);
pc_ml->Nlevels = Nlevels;
fine_level = Nlevels - 1;
ierr = PCMGSetLevels(pc,Nlevels,PETSC_NULL);CHKERRQ(ierr);
/* set default smoothers */
for (level=1; level<=fine_level; level++){
if (size == 1){
ierr = PCMGGetSmoother(pc,level,&smoother);CHKERRQ(ierr);
ierr = KSPSetType(smoother,KSPRICHARDSON);CHKERRQ(ierr);
ierr = KSPGetPC(smoother,&subpc);CHKERRQ(ierr);
ierr = PCSetType(subpc,PCSOR);CHKERRQ(ierr);
} else {
ierr = PCMGGetSmoother(pc,level,&smoother);CHKERRQ(ierr);
ierr = KSPSetType(smoother,KSPRICHARDSON);CHKERRQ(ierr);
ierr = KSPGetPC(smoother,&subpc);CHKERRQ(ierr);
ierr = PCSetType(subpc,PCSOR);CHKERRQ(ierr);
}
}
ierr = PetscObjectOptionsBegin((PetscObject)pc);CHKERRQ(ierr);
ierr = PCSetFromOptions_MG(pc);CHKERRQ(ierr); /* should be called in PCSetFromOptions_ML(), but cannot be called prior to PCMGSetLevels() */
ierr = PetscOptionsEnd();CHKERRQ(ierr);
ierr = PetscMalloc(Nlevels*sizeof(GridCtx),&gridctx);CHKERRQ(ierr);
pc_ml->gridctx = gridctx;
/* wrap ML matrices by PETSc shell matrices at coarsened grids.
Level 0 is the finest grid for ML, but coarsest for PETSc! */
gridctx[fine_level].A = A;
level = fine_level - 1;
if (size == 1){ /* convert ML P, R and A into seqaij format */
for (mllevel=1; mllevel<Nlevels; mllevel++){
mlmat = &(ml_object->Pmat[mllevel]);
ierr = MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].P);CHKERRQ(ierr);
mlmat = &(ml_object->Rmat[mllevel-1]);
ierr = MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].R);CHKERRQ(ierr);
mlmat = &(ml_object->Amat[mllevel]);
ierr = MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].A);CHKERRQ(ierr);
level--;
}
} else { /* convert ML P and R into shell format, ML A into mpiaij format */
for (mllevel=1; mllevel<Nlevels; mllevel++){
mlmat = &(ml_object->Pmat[mllevel]);
ierr = MatWrapML_SHELL(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].P);CHKERRQ(ierr);
mlmat = &(ml_object->Rmat[mllevel-1]);
ierr = MatWrapML_SHELL(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].R);CHKERRQ(ierr);
mlmat = &(ml_object->Amat[mllevel]);
ierr = MatWrapML_MPIAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].A);CHKERRQ(ierr);
level--;
}
}
/* create vectors and ksp at all levels */
for (level=0; level<fine_level; level++){
level1 = level + 1;
ierr = VecCreate(((PetscObject)gridctx[level].A)->comm,&gridctx[level].x);CHKERRQ(ierr);
ierr = VecSetSizes(gridctx[level].x,gridctx[level].A->cmap->n,PETSC_DECIDE);CHKERRQ(ierr);
ierr = VecSetType(gridctx[level].x,VECMPI);CHKERRQ(ierr);
ierr = PCMGSetX(pc,level,gridctx[level].x);CHKERRQ(ierr);
ierr = VecCreate(((PetscObject)gridctx[level].A)->comm,&gridctx[level].b);CHKERRQ(ierr);
ierr = VecSetSizes(gridctx[level].b,gridctx[level].A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr);
ierr = VecSetType(gridctx[level].b,VECMPI);CHKERRQ(ierr);
ierr = PCMGSetRhs(pc,level,gridctx[level].b);CHKERRQ(ierr);
ierr = VecCreate(((PetscObject)gridctx[level1].A)->comm,&gridctx[level1].r);CHKERRQ(ierr);
ierr = VecSetSizes(gridctx[level1].r,gridctx[level1].A->rmap->n,PETSC_DECIDE);CHKERRQ(ierr);
ierr = VecSetType(gridctx[level1].r,VECMPI);CHKERRQ(ierr);
ierr = PCMGSetR(pc,level1,gridctx[level1].r);CHKERRQ(ierr);
if (level == 0){
ierr = PCMGGetCoarseSolve(pc,&gridctx[level].ksp);CHKERRQ(ierr);
} else {
ierr = PCMGGetSmoother(pc,level,&gridctx[level].ksp);CHKERRQ(ierr);
}
}
ierr = PCMGGetSmoother(pc,fine_level,&gridctx[fine_level].ksp);CHKERRQ(ierr);
/* create coarse level and the interpolation between the levels */
for (level=0; level<fine_level; level++){
level1 = level + 1;
ierr = PCMGSetInterpolation(pc,level1,gridctx[level].P);CHKERRQ(ierr);
ierr = PCMGSetRestriction(pc,level1,gridctx[level].R);CHKERRQ(ierr);
if (level > 0){
ierr = PCMGSetResidual(pc,level,PCMGDefaultResidual,gridctx[level].A);CHKERRQ(ierr);
}
ierr = KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A,DIFFERENT_NONZERO_PATTERN);CHKERRQ(ierr);
}
ierr = PCMGSetResidual(pc,fine_level,PCMGDefaultResidual,gridctx[fine_level].A);CHKERRQ(ierr);
ierr = KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A,DIFFERENT_NONZERO_PATTERN);CHKERRQ(ierr);
/* setupcalled is set to 0 so that MG is setup from scratch */
pc->setupcalled = 0;
ierr = PCSetUp_MG(pc);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*@
NEPSetFromOptions - Sets NEP options from the options database.
This routine must be called before NEPSetUp() if the user is to be
allowed to set the solver type.
Collective on NEP
Input Parameters:
. nep - the nonlinear eigensolver context
Notes:
To see all options, run your program with the -help option.
Level: beginner
@*/
PetscErrorCode NEPSetFromOptions(NEP nep)
{
PetscErrorCode ierr;
char type[256],monfilename[PETSC_MAX_PATH_LEN];
PetscBool flg,flg1,flg2,flg3,flg4,flg5;
PetscReal r1,r2,r3;
PetscScalar s;
PetscInt i,j,k;
PetscViewer monviewer;
SlepcConvMonitor ctx;
PetscFunctionBegin;
PetscValidHeaderSpecific(nep,NEP_CLASSID,1);
if (!NEPRegisterAllCalled) { ierr = NEPRegisterAll();CHKERRQ(ierr); }
ierr = PetscObjectOptionsBegin((PetscObject)nep);CHKERRQ(ierr);
ierr = PetscOptionsFList("-nep_type","Nonlinear Eigenvalue Problem method","NEPSetType",NEPList,(char*)(((PetscObject)nep)->type_name?((PetscObject)nep)->type_name:NEPRII),type,256,&flg);CHKERRQ(ierr);
if (flg) {
ierr = NEPSetType(nep,type);CHKERRQ(ierr);
} else if (!((PetscObject)nep)->type_name) {
ierr = NEPSetType(nep,NEPRII);CHKERRQ(ierr);
}
ierr = PetscOptionsEnum("-nep_refine","Iterative refinement method","NEPSetRefine",NEPRefineTypes,(PetscEnum)nep->refine,(PetscEnum*)&nep->refine,NULL);CHKERRQ(ierr);
r1 = nep->reftol;
ierr = PetscOptionsReal("-nep_refine_tol","Tolerance for iterative refinement","NEPSetRefine",nep->reftol,&r1,&flg1);CHKERRQ(ierr);
j = nep->rits;
ierr = PetscOptionsInt("-nep_refine_its","Maximum number of iterations for iterative refinement","NEPSetRefine",nep->rits,&j,&flg2);CHKERRQ(ierr);
if (flg1 || flg2) {
ierr = NEPSetRefine(nep,nep->refine,r1,j);CHKERRQ(ierr);
}
i = nep->max_it? nep->max_it: PETSC_DEFAULT;
ierr = PetscOptionsInt("-nep_max_it","Maximum number of iterations","NEPSetTolerances",nep->max_it,&i,&flg1);CHKERRQ(ierr);
j = nep->max_funcs? nep->max_funcs: PETSC_DEFAULT;
ierr = PetscOptionsInt("-nep_max_funcs","Maximum number of function evaluations","NEPSetTolerances",nep->max_funcs,&j,&flg2);CHKERRQ(ierr);
r1 = nep->abstol;
ierr = PetscOptionsReal("-nep_atol","Absolute tolerance for residual norm","NEPSetTolerances",nep->abstol==PETSC_DEFAULT?SLEPC_DEFAULT_TOL:nep->abstol,&r1,&flg3);CHKERRQ(ierr);
r2 = nep->rtol;
ierr = PetscOptionsReal("-nep_rtol","Relative tolerance for residual norm","NEPSetTolerances",nep->rtol==PETSC_DEFAULT?SLEPC_DEFAULT_TOL:nep->rtol,&r2,&flg4);CHKERRQ(ierr);
r3 = nep->stol;
ierr = PetscOptionsReal("-nep_stol","Relative tolerance for step length","NEPSetTolerances",nep->stol==PETSC_DEFAULT?SLEPC_DEFAULT_TOL:nep->stol,&r3,&flg5);CHKERRQ(ierr);
if (flg1 || flg2 || flg3 || flg4 || flg5) {
ierr = NEPSetTolerances(nep,r1,r2,r3,i,j);CHKERRQ(ierr);
}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-nep_convergence_default","Default (relative error) convergence test","NEPSetConvergenceTest",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
ierr = NEPSetConvergenceTest(nep,NEPConvergedDefault,NULL,NULL);CHKERRQ(ierr);
}
i = nep->nev;
ierr = PetscOptionsInt("-nep_nev","Number of eigenvalues to compute","NEPSetDimensions",nep->nev,&i,&flg1);CHKERRQ(ierr);
j = nep->ncv? nep->ncv: PETSC_DEFAULT;
ierr = PetscOptionsInt("-nep_ncv","Number of basis vectors","NEPSetDimensions",nep->ncv,&j,&flg2);CHKERRQ(ierr);
k = nep->mpd? nep->mpd: PETSC_DEFAULT;
ierr = PetscOptionsInt("-nep_mpd","Maximum dimension of projected problem","NEPSetDimensions",nep->mpd,&k,&flg3);CHKERRQ(ierr);
if (flg1 || flg2 || flg3) {
ierr = NEPSetDimensions(nep,i,j,k);CHKERRQ(ierr);
}
i = 0;
ierr = PetscOptionsInt("-nep_lag_preconditioner","Interval to rebuild preconditioner","NEPSetLagPreconditioner",nep->lag,&i,&flg);CHKERRQ(ierr);
if (flg) { ierr = NEPSetLagPreconditioner(nep,i);CHKERRQ(ierr); }
ierr = PetscOptionsBool("-nep_const_correction_tol","Constant correction tolerance for the linear solver","NEPSetConstCorrectionTol",nep->cctol,&nep->cctol,NULL);CHKERRQ(ierr);
ierr = PetscOptionsScalar("-nep_target","Value of the target","NEPSetTarget",nep->target,&s,&flg);CHKERRQ(ierr);
if (flg) {
ierr = NEPSetWhichEigenpairs(nep,NEP_TARGET_MAGNITUDE);CHKERRQ(ierr);
ierr = NEPSetTarget(nep,s);CHKERRQ(ierr);
}
/* -----------------------------------------------------------------------*/
/*
Cancels all monitors hardwired into code before call to NEPSetFromOptions()
*/
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-nep_monitor_cancel","Remove any hardwired monitor routines","NEPMonitorCancel",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
ierr = NEPMonitorCancel(nep);CHKERRQ(ierr);
}
/*
Prints approximate eigenvalues and error estimates at each iteration
*/
ierr = PetscOptionsString("-nep_monitor","Monitor first unconverged approximate eigenvalue and error estimate","NEPMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)nep),monfilename,&monviewer);CHKERRQ(ierr);
ierr = NEPMonitorSet(nep,NEPMonitorFirst,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
}
ierr = PetscOptionsString("-nep_monitor_conv","Monitor approximate eigenvalues and error estimates as they converge","NEPMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscNew(&ctx);CHKERRQ(ierr);
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)nep),monfilename,&ctx->viewer);CHKERRQ(ierr);
ierr = NEPMonitorSet(nep,NEPMonitorConverged,ctx,(PetscErrorCode (*)(void**))SlepcConvMonitorDestroy);CHKERRQ(ierr);
}
ierr = PetscOptionsString("-nep_monitor_all","Monitor approximate eigenvalues and error estimates","NEPMonitorSet","stdout",monfilename,PETSC_MAX_PATH_LEN,&flg);CHKERRQ(ierr);
if (flg) {
ierr = PetscViewerASCIIOpen(PetscObjectComm((PetscObject)nep),monfilename,&monviewer);CHKERRQ(ierr);
ierr = NEPMonitorSet(nep,NEPMonitorAll,monviewer,(PetscErrorCode (*)(void**))PetscViewerDestroy);CHKERRQ(ierr);
ierr = NEPSetTrackAll(nep,PETSC_TRUE);CHKERRQ(ierr);
}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-nep_monitor_lg","Monitor first unconverged approximate error estimate graphically","NEPMonitorSet",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
ierr = NEPMonitorSet(nep,NEPMonitorLG,NULL,NULL);CHKERRQ(ierr);
}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-nep_monitor_lg_all","Monitor error estimates graphically","NEPMonitorSet",flg,&flg,NULL);CHKERRQ(ierr);
if (flg) {
ierr = NEPMonitorSet(nep,NEPMonitorLGAll,NULL,NULL);CHKERRQ(ierr);
ierr = NEPSetTrackAll(nep,PETSC_TRUE);CHKERRQ(ierr);
}
/* -----------------------------------------------------------------------*/
ierr = PetscOptionsBoolGroupBegin("-nep_largest_magnitude","compute largest eigenvalues in magnitude","NEPSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = NEPSetWhichEigenpairs(nep,NEP_LARGEST_MAGNITUDE);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-nep_smallest_magnitude","compute smallest eigenvalues in magnitude","NEPSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = NEPSetWhichEigenpairs(nep,NEP_SMALLEST_MAGNITUDE);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-nep_largest_real","compute largest real parts","NEPSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = NEPSetWhichEigenpairs(nep,NEP_LARGEST_REAL);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-nep_smallest_real","compute smallest real parts","NEPSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = NEPSetWhichEigenpairs(nep,NEP_SMALLEST_REAL);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-nep_largest_imaginary","compute largest imaginary parts","NEPSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = NEPSetWhichEigenpairs(nep,NEP_LARGEST_IMAGINARY);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-nep_smallest_imaginary","compute smallest imaginary parts","NEPSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = NEPSetWhichEigenpairs(nep,NEP_SMALLEST_IMAGINARY);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-nep_target_magnitude","compute nearest eigenvalues to target","NEPSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = NEPSetWhichEigenpairs(nep,NEP_TARGET_MAGNITUDE);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroup("-nep_target_real","compute eigenvalues with real parts close to target","NEPSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = NEPSetWhichEigenpairs(nep,NEP_TARGET_REAL);CHKERRQ(ierr); }
ierr = PetscOptionsBoolGroupEnd("-nep_target_imaginary","compute eigenvalues with imaginary parts close to target","NEPSetWhichEigenpairs",&flg);CHKERRQ(ierr);
if (flg) { ierr = NEPSetWhichEigenpairs(nep,NEP_TARGET_IMAGINARY);CHKERRQ(ierr); }
ierr = PetscOptionsName("-nep_view","Print detailed information on solver used","NEPView",0);CHKERRQ(ierr);
ierr = PetscOptionsName("-nep_plot_eigs","Make a plot of the computed eigenvalues","NEPSolve",0);CHKERRQ(ierr);
if (nep->ops->setfromoptions) {
ierr = (*nep->ops->setfromoptions)(nep);CHKERRQ(ierr);
}
ierr = PetscObjectProcessOptionsHandlers((PetscObject)nep);CHKERRQ(ierr);
ierr = PetscOptionsEnd();CHKERRQ(ierr);
if (!nep->V) { ierr = NEPGetBV(nep,&nep->V);CHKERRQ(ierr); }
ierr = BVSetFromOptions(nep->V);CHKERRQ(ierr);
if (!nep->rg) { ierr = NEPGetRG(nep,&nep->rg);CHKERRQ(ierr); }
ierr = RGSetFromOptions(nep->rg);CHKERRQ(ierr);
if (!nep->ds) { ierr = NEPGetDS(nep,&nep->ds);CHKERRQ(ierr); }
ierr = DSSetFromOptions(nep->ds);CHKERRQ(ierr);
if (!nep->ksp) { ierr = NEPGetKSP(nep,&nep->ksp);CHKERRQ(ierr); }
ierr = KSPSetOperators(nep->ksp,nep->function,nep->function_pre);CHKERRQ(ierr);
ierr = KSPSetFromOptions(nep->ksp);CHKERRQ(ierr);
ierr = PetscRandomSetFromOptions(nep->rand);CHKERRQ(ierr);
PetscFunctionReturn(0);
}