/*
EPS routines related to options that can be set via the command-line
or procedurally.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
SLEPc - Scalable Library for Eigenvalue Problem Computations
Copyright (c) 2002-2010, Universidad Politecnica de Valencia, Spain
This file is part of SLEPc.
SLEPc is free software: you can redistribute it and/or modify it under the
terms of version 3 of the GNU Lesser General Public License as published by
the Free Software Foundation.
SLEPc is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for
more details.
You should have received a copy of the GNU Lesser General Public License
along with SLEPc. If not, see <http://www.gnu.org/licenses/>.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
*/
#include <private/epsimpl.h> /*I "slepceps.h" I*/
#undef __FUNCT__
#define __FUNCT__ "EPSSetFromOptions"
/*@
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,val;
PetscReal r,nrma,nrmb;
PetscScalar s;
PetscInt i,j,k;
const char *bal_list[4] = {"none","oneside","twoside","user"};
PetscViewerASCIIMonitor monviewer;
SlepcConvMonitor ctx;
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
if (!EPSRegisterAllCalled) { ierr = EPSRegisterAll(PETSC_NULL);CHKERRQ(ierr); }
ierr = PetscOptionsBegin(((PetscObject)eps)->comm,((PetscObject)eps)->prefix,"Eigenproblem Solver (EPS) Options","EPS");CHKERRQ(ierr);
ierr = PetscOptionsList("-eps_type","Eigenproblem 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);
}
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);}
/*
Set the type if it was never set.
*/
if (!((PetscObject)eps)->type_name) {
ierr = EPSSetType(eps,EPSKRYLOVSCHUR);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);}
if (!eps->balance) eps->balance = EPS_BALANCE_NONE;
ierr = PetscOptionsEList("-eps_balance","Balancing method","EPSSetBalance",bal_list,4,bal_list[eps->balance-EPS_BALANCE_NONE],&i,&flg);CHKERRQ(ierr);
if (flg) { eps->balance = (EPSBalance)(i+EPS_BALANCE_NONE); }
r = j = PETSC_IGNORE;
ierr = PetscOptionsInt("-eps_balance_its","Number of iterations in balancing","EPSSetBalance",eps->balance_its,&j,PETSC_NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-eps_balance_cutoff","Cutoff value in balancing","EPSSetBalance",eps->balance_cutoff,&r,PETSC_NULL);CHKERRQ(ierr);
ierr = EPSSetBalance(eps,(EPSBalance)PETSC_IGNORE,j,r);CHKERRQ(ierr);
r = i = PETSC_IGNORE;
ierr = PetscOptionsInt("-eps_max_it","Maximum number of iterations","EPSSetTolerances",eps->max_it,&i,PETSC_NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-eps_tol","Tolerance","EPSSetTolerances",eps->tol,&r,PETSC_NULL);CHKERRQ(ierr);
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 = PetscOptionsBoolGroupEnd("-eps_conv_abs","Absolute error convergence test","EPSSetConvergenceTest",&flg);CHKERRQ(ierr);
if (flg) {ierr = EPSSetConvergenceTest(eps,EPS_CONV_ABS);CHKERRQ(ierr);}
i = j = k = PETSC_IGNORE;
ierr = PetscOptionsInt("-eps_nev","Number of eigenvalues to compute","EPSSetDimensions",eps->nev,&i,PETSC_NULL);CHKERRQ(ierr);
ierr = PetscOptionsInt("-eps_ncv","Number of basis vectors","EPSSetDimensions",eps->ncv,&j,PETSC_NULL);CHKERRQ(ierr);
ierr = PetscOptionsInt("-eps_mpd","Maximum dimension of projected problem","EPSSetDimensions",eps->mpd,&k,PETSC_NULL);CHKERRQ(ierr);
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,PETSC_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 = PetscViewerASCIIMonitorCreate(((PetscObject)eps)->comm,monfilename,((PetscObject)eps)->tablevel,&monviewer);CHKERRQ(ierr);
ierr = EPSMonitorSet(eps,EPSMonitorFirst,monviewer,(PetscErrorCode (*)(void**))PetscViewerASCIIMonitorDestroy);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(struct _n_SlepcConvMonitor,&ctx);CHKERRQ(ierr);
ierr = PetscViewerASCIIMonitorCreate(((PetscObject)eps)->comm,monfilename,((PetscObject)eps)->tablevel,&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 = PetscViewerASCIIMonitorCreate(((PetscObject)eps)->comm,monfilename,((PetscObject)eps)->tablevel,&monviewer);CHKERRQ(ierr);
ierr = EPSMonitorSet(eps,EPSMonitorAll,monviewer,(PetscErrorCode (*)(void**))PetscViewerASCIIMonitorDestroy);CHKERRQ(ierr);
ierr = EPSSetTrackAll(eps,PETSC_TRUE);CHKERRQ(ierr);
}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-eps_monitor_draw","Monitor first unconverged approximate eigenvalue and error estimate graphically","EPSMonitorSet",flg,&flg,PETSC_NULL);CHKERRQ(ierr);
if (flg) {
ierr = EPSMonitorSet(eps,EPSMonitorLG,PETSC_NULL,PETSC_NULL);CHKERRQ(ierr);
}
flg = PETSC_FALSE;
ierr = PetscOptionsBool("-eps_monitor_draw_all","Monitor error estimates graphically","EPSMonitorSet",flg,&flg,PETSC_NULL);CHKERRQ(ierr);
if (flg) {
ierr = EPSMonitorSet(eps,EPSMonitorLGAll,PETSC_NULL,PETSC_NULL);CHKERRQ(ierr);
ierr = EPSSetTrackAll(eps,PETSC_TRUE);CHKERRQ(ierr);
}
/* -----------------------------------------------------------------------*/
ierr = PetscOptionsScalar("-eps_target","Value of the target","EPSSetTarget",eps->target,&s,&flg);CHKERRQ(ierr);
if (flg) {
ierr = EPSSetWhichEigenpairs(eps,EPS_TARGET_MAGNITUDE);CHKERRQ(ierr);
ierr = EPSSetTarget(eps,s);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 = PetscOptionsBoolGroupEnd("-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 = PetscOptionsBool("-eps_left_vectors","Compute left eigenvectors also","EPSSetLeftVectorsWanted",eps->leftvecs,&val,&flg);CHKERRQ(ierr);
if (flg) {
ierr = EPSSetLeftVectorsWanted(eps,val);CHKERRQ(ierr);
}
ierr = PetscOptionsBool("-eps_true_residual","Compute true residuals explicitly","EPSSetTrueResidual",eps->trueres,&val,&flg);CHKERRQ(ierr);
if (flg) {
ierr = EPSSetTrueResidual(eps,val);CHKERRQ(ierr);
}
nrma = nrmb = PETSC_IGNORE;
ierr = PetscOptionsReal("-eps_norm_a","Norm of matrix A","EPSSetMatrixNorms",eps->nrma,&nrma,PETSC_NULL);CHKERRQ(ierr);
ierr = PetscOptionsReal("-eps_norm_b","Norm of matrix B","EPSSetMatrixNorms",eps->nrmb,&nrmb,PETSC_NULL);CHKERRQ(ierr);
ierr = EPSSetMatrixNorms(eps,nrma,nrmb,eps->adaptive);CHKERRQ(ierr);
ierr = PetscOptionsBool("-eps_norms_adaptive","Update the value of matrix norms adaptively","EPSSetMatrixNorms",eps->adaptive,&val,&flg);CHKERRQ(ierr);
if (flg) {
ierr = EPSSetMatrixNorms(eps,PETSC_IGNORE,PETSC_IGNORE,val);CHKERRQ(ierr);
}
ierr = PetscOptionsName("-eps_view","Print detailed information on solver used","EPSView",0);CHKERRQ(ierr);
ierr = PetscOptionsName("-eps_view_binary","Save the matrices associated to the eigenproblem","EPSSetFromOptions",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 = PetscOptionsEnd();CHKERRQ(ierr);
ierr = PetscRandomSetFromOptions(eps->rand);CHKERRQ(ierr);
if (!eps->ip) { ierr = EPSGetIP(eps,&eps->ip);CHKERRQ(ierr); }
ierr = IPSetFromOptions(eps->ip);CHKERRQ(ierr);
ierr = STSetFromOptions(eps->OP);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetTolerances"
/*@
EPSGetTolerances - Gets the tolerance and maximum iteration count used
by the EPS convergence tests.
Not Collective
Input Parameter:
. eps - the eigensolver context
Output Parameters:
+ tol - the convergence tolerance
- maxits - maximum number of iterations
Notes:
The user can specify PETSC_NULL for any parameter that is not needed.
Level: intermediate
.seealso: EPSSetTolerances()
@*/
PetscErrorCode EPSGetTolerances(EPS eps,PetscReal *tol,PetscInt *maxits)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
if (tol) *tol = eps->tol;
if (maxits) *maxits = eps->max_it;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetTolerances"
/*@
EPSSetTolerances - Sets the tolerance and maximum iteration count used
by the EPS convergence tests.
Logically Collective on EPS
Input Parameters:
+ eps - the eigensolver context
. tol - the convergence tolerance
- maxits - maximum number of iterations to use
Options Database Keys:
+ -eps_tol <tol> - Sets the convergence tolerance
- -eps_max_it <maxits> - Sets the maximum number of iterations allowed
Notes:
Use PETSC_IGNORE for an argument that need not be changed.
Use PETSC_DECIDE for maxits to assign a reasonably good value, which is
dependent on the solution method.
Level: intermediate
.seealso: EPSGetTolerances()
@*/
PetscErrorCode EPSSetTolerances(EPS eps,PetscReal tol,PetscInt maxits)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveReal(eps,tol,2);
PetscValidLogicalCollectiveInt(eps,maxits,3);
if (tol != PETSC_IGNORE) {
if (tol == PETSC_DEFAULT) {
eps->tol = 1e-7;
} else {
if (tol < 0.0) SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of tol. Must be > 0");
eps->tol = tol;
}
}
if (maxits != PETSC_IGNORE) {
if (maxits == PETSC_DEFAULT || maxits == PETSC_DECIDE) {
eps->max_it = 0;
eps->setupcalled = 0;
} else {
if (maxits < 0) SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of maxits. Must be > 0");
eps->max_it = maxits;
}
}
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetDimensions"
/*@
EPSGetDimensions - Gets the number of eigenvalues to compute
and the dimension of the subspace.
Not Collective
Input Parameter:
. eps - the eigensolver context
Output Parameters:
+ nev - number of eigenvalues to compute
. ncv - the maximum dimension of the subspace to be used by the solver
- mpd - the maximum dimension allowed for the projected problem
Notes:
The user can specify PETSC_NULL for any parameter that is not needed.
Level: intermediate
.seealso: EPSSetDimensions()
@*/
PetscErrorCode EPSGetDimensions(EPS eps,PetscInt *nev,PetscInt *ncv,PetscInt *mpd)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
if (nev) *nev = eps->nev;
if (ncv) *ncv = eps->ncv;
if (mpd) *mpd = eps->mpd;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetDimensions"
/*@
EPSSetDimensions - Sets the number of eigenvalues to compute
and the dimension of the subspace.
Logically Collective on EPS
Input Parameters:
+ eps - the eigensolver context
. nev - number of eigenvalues to compute
. ncv - the maximum dimension of the subspace to be used by the solver
- mpd - the maximum dimension allowed for the projected problem
Options Database Keys:
+ -eps_nev <nev> - Sets the number of eigenvalues
. -eps_ncv <ncv> - Sets the dimension of the subspace
- -eps_mpd <mpd> - Sets the maximum projected dimension
Notes:
Use PETSC_IGNORE to retain the previous value of any parameter.
Use PETSC_DECIDE for ncv and mpd to assign a reasonably good value, which is
dependent on the solution method.
The parameters ncv and mpd are intimately related, so that the user is advised
to set one of them at most. Normal usage is the following:
(a) In cases where nev is small, the user sets ncv (a reasonable default is 2*nev).
(b) In cases where nev is large, the user sets mpd.
The value of ncv should always be between nev and (nev+mpd), typically
ncv=nev+mpd. If nev is not too large, mpd=nev is a reasonable choice, otherwise
a smaller value should be used.
Level: intermediate
.seealso: EPSGetDimensions()
@*/
PetscErrorCode EPSSetDimensions(EPS eps,PetscInt nev,PetscInt ncv,PetscInt mpd)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveInt(eps,nev,2);
PetscValidLogicalCollectiveInt(eps,ncv,3);
PetscValidLogicalCollectiveInt(eps,mpd,4);
if (nev != PETSC_IGNORE) {
if (nev<1) SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of nev. Must be > 0");
eps->nev = nev;
eps->setupcalled = 0;
}
if (ncv != PETSC_IGNORE) {
if (ncv == PETSC_DECIDE || ncv == PETSC_DEFAULT) {
eps->ncv = 0;
} else {
if (ncv<1) SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of ncv. Must be > 0");
eps->ncv = ncv;
}
eps->setupcalled = 0;
}
if (mpd != PETSC_IGNORE) {
if (mpd == PETSC_DECIDE || mpd == PETSC_DEFAULT) {
eps->mpd = 0;
} else {
if (mpd<1) SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of mpd. Must be > 0");
eps->mpd = mpd;
}
}
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetWhichEigenpairs"
/*@
EPSSetWhichEigenpairs - Specifies which portion of the spectrum is
to be sought.
Logically Collective on EPS
Input Parameters:
+ eps - eigensolver context obtained from EPSCreate()
- which - the portion of the spectrum to be sought
Possible values:
The parameter 'which' can have one of these values
+ EPS_LARGEST_MAGNITUDE - largest eigenvalues in magnitude (default)
. EPS_SMALLEST_MAGNITUDE - smallest eigenvalues in magnitude
. EPS_LARGEST_REAL - largest real parts
. EPS_SMALLEST_REAL - smallest real parts
. EPS_LARGEST_IMAGINARY - largest imaginary parts
. EPS_SMALLEST_IMAGINARY - smallest imaginary parts
. EPS_TARGET_MAGNITUDE - eigenvalues closest to the target (in magnitude)
. EPS_TARGET_REAL - eigenvalues with real part closest to target
. EPS_TARGET_IMAGINARY - eigenvalues with imaginary part closest to target
- EPS_WHICH_USER - user defined ordering set with EPSSetEigenvalueComparison()
Options Database Keys:
+ -eps_largest_magnitude - Sets largest eigenvalues in magnitude
. -eps_smallest_magnitude - Sets smallest eigenvalues in magnitude
. -eps_largest_real - Sets largest real parts
. -eps_smallest_real - Sets smallest real parts
. -eps_largest_imaginary - Sets largest imaginary parts
. -eps_smallest_imaginary - Sets smallest imaginary parts
. -eps_target_magnitude - Sets eigenvalues closest to target
. -eps_target_real - Sets real parts closest to target
- -eps_target_imaginary - Sets imaginary parts closest to target
Notes:
Not all eigensolvers implemented in EPS account for all the possible values
stated above. Also, some values make sense only for certain types of
problems. If SLEPc is compiled for real numbers EPS_LARGEST_IMAGINARY
and EPS_SMALLEST_IMAGINARY use the absolute value of the imaginary part
for eigenvalue selection.
The target is a scalar value provided with EPSSetTarget().
The criterion EPS_TARGET_IMAGINARY is available only in case PETSc and
SLEPc have been built with complex scalars.
Level: intermediate
.seealso: EPSGetWhichEigenpairs(), EPSSetTarget(), EPSSetEigenvalueComparison(), EPSSortEigenvalues(), EPSWhich
@*/
PetscErrorCode EPSSetWhichEigenpairs(EPS eps,EPSWhich which)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveEnum(eps,which,2);
if (which!=PETSC_IGNORE) {
if (which==PETSC_DECIDE || which==PETSC_DEFAULT) eps->which = (EPSWhich)0;
else switch (which) {
case EPS_LARGEST_MAGNITUDE:
case EPS_SMALLEST_MAGNITUDE:
case EPS_LARGEST_REAL:
case EPS_SMALLEST_REAL:
case EPS_LARGEST_IMAGINARY:
case EPS_SMALLEST_IMAGINARY:
case EPS_TARGET_MAGNITUDE:
case EPS_TARGET_REAL:
#if defined(PETSC_USE_COMPLEX)
case EPS_TARGET_IMAGINARY:
#endif
case EPS_WHICH_USER:
if (eps->which != which) {
eps->setupcalled = 0;
eps->which = which;
}
break;
default:
SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Invalid 'which' value");
}
}
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetWhichEigenpairs"
/*@C
EPSGetWhichEigenpairs - Returns which portion of the spectrum is to be
sought.
Not Collective
Input Parameter:
. eps - eigensolver context obtained from EPSCreate()
Output Parameter:
. which - the portion of the spectrum to be sought
Notes:
See EPSSetWhichEigenpairs() for possible values of 'which'.
Level: intermediate
.seealso: EPSSetWhichEigenpairs(), EPSWhich
@*/
PetscErrorCode EPSGetWhichEigenpairs(EPS eps,EPSWhich *which)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidPointer(which,2);
*which = eps->which;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetLeftVectorsWanted"
/*@
EPSSetLeftVectorsWanted - Specifies which eigenvectors are required.
Logically Collective on EPS
Input Parameters:
+ eps - the eigensolver context
- leftvecs - whether left eigenvectors are required or not
Options Database Keys:
. -eps_left_vectors <boolean> - Sets/resets the boolean flag 'leftvecs'
Notes:
If the user sets leftvecs=PETSC_TRUE then the solver uses a variant of
the algorithm that computes both right and left eigenvectors. This is
usually much more costly. This option is not available in all solvers.
Level: intermediate
.seealso: EPSGetLeftVectorsWanted(), EPSGetEigenvectorLeft()
@*/
PetscErrorCode EPSSetLeftVectorsWanted(EPS eps,PetscBool leftvecs)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveBool(eps,leftvecs,2);
if (eps->leftvecs != leftvecs) {
eps->leftvecs = leftvecs;
eps->setupcalled = 0;
}
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetLeftVectorsWanted"
/*@
EPSGetLeftVectorsWanted - Returns the flag indicating whether left
eigenvectors are required or not.
Not Collective
Input Parameter:
. eps - the eigensolver context
Output Parameter:
. leftvecs - the returned flag
Level: intermediate
.seealso: EPSSetLeftVectorsWanted(), EPSWhich
@*/
PetscErrorCode EPSGetLeftVectorsWanted(EPS eps,PetscBool *leftvecs)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidPointer(leftvecs,2);
*leftvecs = eps->leftvecs;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetMatrixNorms"
/*@
EPSSetMatrixNorms - Gives the reference values of the matrix norms
and specifies whether these values should be improved adaptively.
Logically Collective on EPS
Input Parameters:
+ eps - the eigensolver context
. nrma - a reference value for the norm of matrix A
. nrmb - a reference value for the norm of matrix B
- adaptive - whether matrix norms are improved adaptively
Options Database Keys:
+ -eps_norm_a <nrma> - norm of A
. -eps_norm_b <nrma> - norm of B
- -eps_norms_adaptive <boolean> - Sets/resets the boolean flag 'adaptive'
Notes:
If the user sets adaptive=PETSC_FALSE then the solver uses the values
of nrma and nrmb for the matrix norms, and these values do not change
throughout the iteration.
If the user sets adaptive=PETSC_TRUE then the solver tries to adaptively
improve the supplied values, with the numerical information generated
during the iteration. This option is not available in all solvers.
If a passed value is PETSC_DEFAULT, the corresponding norm will be set to 1.
If a passed value is PETSC_DETERMINE, the corresponding norm will be computed
as the NORM_INFINITY with MatNorm().
Level: intermediate
.seealso: EPSGetMatrixNorms()
@*/
PetscErrorCode EPSSetMatrixNorms(EPS eps,PetscReal nrma,PetscReal nrmb,PetscBool adaptive)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveReal(eps,nrma,2);
PetscValidLogicalCollectiveReal(eps,nrmb,3);
PetscValidLogicalCollectiveBool(eps,adaptive,4);
if (nrma != PETSC_IGNORE) {
if (nrma == PETSC_DEFAULT) eps->nrma = 1.0;
else if (nrma == PETSC_DETERMINE) {
eps->nrma = nrma;
eps->setupcalled = 0;
} else {
if (nrma < 0.0) SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of nrma. Must be > 0");
eps->nrma = nrma;
}
}
if (nrmb != PETSC_IGNORE) {
if (!eps->isgeneralized) SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_WRONG,"Norm of B only allowed in generalized problems");
if (nrmb == PETSC_DEFAULT) eps->nrmb = 1.0;
else if (nrmb == PETSC_DETERMINE) {
eps->nrmb = nrmb;
eps->setupcalled = 0;
} else {
if (nrmb < 0.0) SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Illegal value of nrmb. Must be > 0");
eps->nrmb = nrmb;
}
}
if (eps->adaptive != adaptive) {
eps->adaptive = adaptive;
eps->setupcalled = 0;
if (adaptive) SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_SUP,"Sorry, adaptive norms are not implemented in this release.");
}
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetMatrixNorms"
/*@
EPSGetMatrixNorms - Returns the value of the matrix norms (either set
by the user or estimated by the solver) and the flag indicating whether
the norms are being adaptively improved.
Not Collective
Input Parameter:
. eps - the eigensolver context
Output Parameters:
+ nrma - the norm of matrix A
. nrmb - the norm of matrix B
- adaptive - whether matrix norms are improved adaptively
Level: intermediate
.seealso: EPSSetMatrixNorms()
@*/
PetscErrorCode EPSGetMatrixNorms(EPS eps,PetscReal *nrma,PetscReal *nrmb,PetscBool *adaptive)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
if (nrma) *nrma = eps->nrma;
if (nrmb) *nrmb = eps->nrmb;
if (adaptive) *adaptive = eps->adaptive;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetEigenvalueComparison"
/*@C
EPSSetEigenvalueComparison - Specifies the eigenvalue comparison function
when EPSSetWhichEigenpairs() is set to EPS_WHICH_USER.
Logically Collective on EPS
Input Parameters:
+ eps - eigensolver context obtained from EPSCreate()
. func - a pointer to the comparison function
- ctx - a context pointer (the last parameter to the comparison function)
Calling Sequence of func:
$ func(EPS eps,PetscScalar ar,PetscScalar ai,PetscScalar br,PetscScalar bi,PetscInt *res,void *ctx)
+ eps - eigensolver context obtained from EPSCreate()
. ar - real part of the 1st eigenvalue
. ai - imaginary part of the 1st eigenvalue
. br - real part of the 2nd eigenvalue
. bi - imaginary part of the 2nd eigenvalue
. res - result of comparison
- ctx - optional context, as set by EPSSetEigenvalueComparison()
Note:
The returning parameter 'res' can be:
+ negative - if the 1st eigenvalue is preferred to the 2st one
. zero - if both eigenvalues are equally preferred
- positive - if the 2st eigenvalue is preferred to the 1st one
Level: advanced
.seealso: EPSSetWhichEigenpairs(), EPSSortEigenvalues(), EPSWhich
@*/
PetscErrorCode EPSSetEigenvalueComparison(EPS eps,PetscErrorCode (*func)(EPS,PetscScalar,PetscScalar,PetscScalar,PetscScalar,PetscInt*,void*),void* ctx)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
eps->which_func = func;
eps->which_ctx = ctx;
eps->which = EPS_WHICH_USER;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetConvergenceTestFunction"
/*@C
EPSSetConvergenceTestFunction - Sets a function to compute the error estimate
used in the convergence test.
Logically Collective on EPS
Input Parameters:
+ eps - eigensolver context obtained from EPSCreate()
. func - a pointer to the convergence test function
- ctx - a context pointer (the last parameter to the convergence test function)
Calling Sequence of func:
$ func(EPS eps,PetscScalar eigr,PetscScalar eigi,PetscReal res,PetscReal *errest,void *ctx)
+ eps - eigensolver context obtained from EPSCreate()
. eigr - real part of the eigenvalue
. eigi - imaginary part of the eigenvalue
. res - residual norm associated to the eigenpair
. errest - (output) computed error estimate
- ctx - optional context, as set by EPSSetConvergenceTest()
Note:
If the error estimate returned by the convergence test function is less than
the tolerance, then the eigenvalue is accepted as converged.
Level: advanced
.seealso: EPSSetConvergenceTest(),EPSSetTolerances()
@*/
extern PetscErrorCode EPSSetConvergenceTestFunction(EPS eps,PetscErrorCode (*func)(EPS,PetscScalar,PetscScalar,PetscReal,PetscReal*,void*),void* ctx)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
eps->conv_func = func;
eps->conv_ctx = ctx;
if (func == EPSEigRelativeConverged) eps->conv = EPS_CONV_EIG;
else if (func == EPSNormRelativeConverged) eps->conv = EPS_CONV_NORM;
else if (func == EPSAbsoluteConverged) eps->conv = EPS_CONV_ABS;
else eps->conv = EPS_CONV_USER;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetConvergenceTest"
/*@
EPSSetConvergenceTest - Specifies how to compute the error estimate
used in the convergence test.
Logically Collective on EPS
Input Parameters:
+ eps - eigensolver context obtained from EPSCreate()
- conv - the type of convergence test
Options Database Keys:
+ -eps_conv_abs - Sets the absolute convergence test
. -eps_conv_eig - Sets the convergence test relative to the eigenvalue
- -eps_conv_norm - Sets the convergence test relative to the matrix norms
Note:
The parameter 'conv' can have one of these values
+ EPS_CONV_ABS - absolute error ||r||
. EPS_CONV_EIG - error relative to the eigenvalue l, ||r||/|l|
. EPS_CONV_NORM - error relative to the matrix norms, ||r||/(||A||+|l|*||B||)
- EPS_CONV_USER - function set by EPSSetConvergenceTestFunction()
Level: intermediate
.seealso: EPSGetConvergenceTest(), EPSSetConvergenceTestFunction(), EPSConv
@*/
PetscErrorCode EPSSetConvergenceTest(EPS eps,EPSConv conv)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveEnum(eps,conv,2);
switch(conv) {
case EPS_CONV_EIG: eps->conv_func = EPSEigRelativeConverged; break;
case EPS_CONV_NORM: eps->conv_func = EPSNormRelativeConverged; break;
case EPS_CONV_ABS: eps->conv_func = EPSAbsoluteConverged; break;
case EPS_CONV_USER: break;
default:
SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Invalid 'conv' value");
}
eps->conv = conv;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetConvergenceTest"
/*@
EPSGetConvergenceTest - Gets the method used to compute the error estimate
used in the convergence test.
Not Collective
Input Parameters:
. eps - eigensolver context obtained from EPSCreate()
Output Parameters:
. conv - the type of convergence test
Level: intermediate
.seealso: EPSSetConvergenceTest(), EPSSetConvergenceTestFunction(), EPSConv
@*/
PetscErrorCode EPSGetConvergenceTest(EPS eps,EPSConv *conv)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidPointer(conv,2);
*conv = eps->conv;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetProblemType"
/*@
EPSSetProblemType - Specifies the type of the eigenvalue problem.
Logically Collective on EPS
Input Parameters:
+ eps - the eigensolver context
- type - a known type of eigenvalue problem
Options Database Keys:
+ -eps_hermitian - Hermitian eigenvalue problem
. -eps_gen_hermitian - generalized Hermitian eigenvalue problem
. -eps_non_hermitian - non-Hermitian eigenvalue problem
. -eps_gen_non_hermitian - generalized non-Hermitian eigenvalue problem
- -eps_pos_gen_non_hermitian - generalized non-Hermitian eigenvalue problem
with positive semi-definite B
Notes:
Allowed values for the problem type are: Hermitian (EPS_HEP), non-Hermitian
(EPS_NHEP), generalized Hermitian (EPS_GHEP), generalized non-Hermitian
(EPS_GNHEP), generalized non-Hermitian with positive semi-definite B
(EPS_PGNHEP), and generalized Hermitian-indefinite (EPS_GHIEP).
This function must be used to instruct SLEPc to exploit symmetry. If no
problem type is specified, by default a non-Hermitian problem is assumed
(either standard or generalized). If the user knows that the problem is
Hermitian (i.e. A=A^H) or generalized Hermitian (i.e. A=A^H, B=B^H, and
B positive definite) then it is recommended to set the problem type so
that eigensolver can exploit these properties.
Level: beginner
.seealso: EPSSetOperators(), EPSSetType(), EPSGetProblemType(), EPSProblemType
@*/
PetscErrorCode EPSSetProblemType(EPS eps,EPSProblemType type)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveEnum(eps,type,2);
switch (type) {
case EPS_HEP:
eps->isgeneralized = PETSC_FALSE;
eps->ishermitian = PETSC_TRUE;
eps->ispositive = PETSC_FALSE;
break;
case EPS_NHEP:
eps->isgeneralized = PETSC_FALSE;
eps->ishermitian = PETSC_FALSE;
eps->ispositive = PETSC_FALSE;
break;
case EPS_GHEP:
eps->isgeneralized = PETSC_TRUE;
eps->ishermitian = PETSC_TRUE;
eps->ispositive = PETSC_TRUE;
break;
case EPS_GNHEP:
eps->isgeneralized = PETSC_TRUE;
eps->ishermitian = PETSC_FALSE;
eps->ispositive = PETSC_FALSE;
break;
case EPS_PGNHEP:
eps->isgeneralized = PETSC_TRUE;
eps->ishermitian = PETSC_FALSE;
eps->ispositive = PETSC_TRUE;
break;
case EPS_GHIEP:
eps->isgeneralized = PETSC_TRUE;
eps->ishermitian = PETSC_TRUE;
eps->ispositive = PETSC_FALSE;
break;
default:
SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_WRONG,"Unknown eigenvalue problem type");
}
eps->problem_type = type;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetProblemType"
/*@C
EPSGetProblemType - Gets the problem type from the EPS object.
Not Collective
Input Parameter:
. eps - the eigensolver context
Output Parameter:
. type - name of EPS problem type
Level: intermediate
.seealso: EPSSetProblemType(), EPSProblemType
@*/
PetscErrorCode EPSGetProblemType(EPS eps,EPSProblemType *type)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidPointer(type,2);
*type = eps->problem_type;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetExtraction"
/*@
EPSSetExtraction - Specifies the type of extraction technique to be employed
by the eigensolver.
Logically Collective on EPS
Input Parameters:
+ eps - the eigensolver context
- extr - a known type of extraction
Options Database Keys:
+ -eps_ritz - Rayleigh-Ritz extraction
. -eps_harmonic - harmonic Ritz extraction
. -eps_harmonic_relative - harmonic Ritz extraction relative to the eigenvalue
. -eps_harmonic_right - harmonic Ritz extraction for rightmost eigenvalues
. -eps_harmonic_largest - harmonic Ritz extraction for largest magnitude
(without target)
. -eps_refined - refined Ritz extraction
- -eps_refined_harmonic - refined harmonic Ritz extraction
Notes:
Not all eigensolvers support all types of extraction. See the SLEPc
Users Manual for details.
By default, a standard Rayleigh-Ritz extraction is used. Other extractions
may be useful when computing interior eigenvalues.
Harmonic-type extractions are used in combination with a 'target'.
Level: beginner
.seealso: EPSSetTarget(), EPSGetExtraction(), EPSExtraction
@*/
PetscErrorCode EPSSetExtraction(EPS eps,EPSExtraction extr)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveEnum(eps,extr,2);
eps->extraction = extr;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetExtraction"
/*@C
EPSGetExtraction - Gets the extraction type used by the EPS object.
Not Collective
Input Parameter:
. eps - the eigensolver context
Output Parameter:
. extr - name of extraction type
Level: intermediate
.seealso: EPSSetExtraction(), EPSExtraction
@*/
PetscErrorCode EPSGetExtraction(EPS eps,EPSExtraction *extr)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidPointer(extr,2);
*extr = eps->extraction;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetBalance"
/*@
EPSSetBalance - Specifies the balancing technique to be employed by the
eigensolver, and some parameters associated to it.
Logically Collective on EPS
Input Parameters:
+ eps - the eigensolver context
. bal - the balancing method, one of EPS_BALANCE_NONE, EPS_BALANCE_ONESIDE,
EPS_BALANCE_TWOSIDE, or EPS_BALANCE_USER
. its - number of iterations of the balancing algorithm
- cutoff - cutoff value
Options Database Keys:
+ -eps_balance <method> - the balancing method, where <method> is one of
'none', 'oneside', 'twoside', or 'user'
. -eps_balance_its <its> - number of iterations
- -eps_balance_cutoff <cutoff> - cutoff value
Notes:
When balancing is enabled, the solver works implicitly with matrix DAD^-1,
where D is an appropriate diagonal matrix. This improves the accuracy of
the computed results in some cases. See the SLEPc Users Manual for details.
Balancing makes sense only for non-Hermitian problems when the required
precision is high (i.e. a small tolerance such as 1e-15).
By default, balancing is disabled. The two-sided method is much more
effective than the one-sided counterpart, but it requires the system
matrices to have the MatMultTranspose operation defined.
The parameter 'its' is the number of iterations performed by the method. The
cutoff value is used only in the two-side variant. Use PETSC_IGNORE for an
argument that need not be changed. Use PETSC_DECIDE to assign a reasonably
good value.
User-defined balancing is allowed provided that the corresponding matrix
is set via STSetBalanceMatrix.
Level: intermediate
.seealso: EPSGetBalance(), EPSBalance, STSetBalanceMatrix()
@*/
PetscErrorCode EPSSetBalance(EPS eps,EPSBalance bal,PetscInt its,PetscReal cutoff)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveEnum(eps,bal,2);
PetscValidLogicalCollectiveInt(eps,its,3);
PetscValidLogicalCollectiveReal(eps,cutoff,4);
if (bal!=PETSC_IGNORE) {
if (bal==PETSC_DECIDE || bal==PETSC_DEFAULT) eps->balance = EPS_BALANCE_TWOSIDE;
else switch (bal) {
case EPS_BALANCE_NONE:
case EPS_BALANCE_ONESIDE:
case EPS_BALANCE_TWOSIDE:
case EPS_BALANCE_USER:
eps->balance = bal;
break;
default:
SETERRQ(((PetscObject)eps)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Invalid value of argument 'bal'");
}
}
if (its!=PETSC_IGNORE) {
if (its==PETSC_DECIDE || its==PETSC_DEFAULT) eps->balance_its = 5;
else eps->balance_its = its;
}
if (cutoff!=PETSC_IGNORE) {
if (cutoff==PETSC_DECIDE || cutoff==PETSC_DEFAULT) eps->balance_cutoff = 1e-8;
else eps->balance_cutoff = cutoff;
}
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetBalance"
/*@
EPSGetBalance - Gets the balancing type used by the EPS object, and the associated
parameters.
Not Collective
Input Parameter:
. eps - the eigensolver context
Output Parameters:
+ bal - the balancing method
. its - number of iterations of the balancing algorithm
- cutoff - cutoff value
Level: intermediate
Note:
The user can specify PETSC_NULL for any parameter that is not needed.
.seealso: EPSSetBalance(), EPSBalance
@*/
PetscErrorCode EPSGetBalance(EPS eps,EPSBalance *bal,PetscInt *its,PetscReal *cutoff)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
if (bal) *bal = eps->balance;
if (its) *its = eps->balance_its;
if (cutoff) *cutoff = eps->balance_cutoff;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetTrueResidual"
/*@
EPSSetTrueResidual - Specifies if the solver must compute the true residual
explicitly or not.
Logically Collective on EPS
Input Parameters:
+ eps - the eigensolver context
- trueres - whether true residuals are required or not
Options Database Keys:
. -eps_true_residual <boolean> - Sets/resets the boolean flag 'trueres'
Notes:
If the user sets trueres=PETSC_TRUE then the solver explicitly computes
the true residual for each eigenpair approximation, and uses it for
convergence testing. Computing the residual is usually an expensive
operation. Some solvers (e.g., Krylov solvers) can avoid this computation
by using a cheap estimate of the residual norm, but this may sometimes
give inaccurate results (especially if a spectral transform is being
used). On the contrary, preconditioned eigensolvers (e.g., Davidson solvers)
do rely on computing the true residual, so this option is irrelevant for them.
Level: intermediate
.seealso: EPSGetTrueResidual()
@*/
PetscErrorCode EPSSetTrueResidual(EPS eps,PetscBool trueres)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveBool(eps,trueres,2);
eps->trueres = trueres;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetTrueResidual"
/*@C
EPSGetTrueResidual - Returns the flag indicating whether true
residuals must be computed explicitly or not.
Not Collective
Input Parameter:
. eps - the eigensolver context
Output Parameter:
. trueres - the returned flag
Level: intermediate
.seealso: EPSSetTrueResidual()
@*/
PetscErrorCode EPSGetTrueResidual(EPS eps,PetscBool *trueres)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidPointer(trueres,2);
*trueres = eps->trueres;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetTrackAll"
/*@
EPSSetTrackAll - Specifies if the solver must compute the residual norm of all
approximate eigenpairs or not.
Logically Collective on EPS
Input Parameters:
+ eps - the eigensolver context
- trackall - whether to compute all residuals or not
Notes:
If the user sets trackall=PETSC_TRUE then the solver computes (or estimates)
the residual norm for each eigenpair approximation. Computing the residual is
usually an expensive operation and solvers commonly compute only the residual
associated to the first unconverged eigenpair.
The options '-eps_monitor_all' and '-eps_monitor_draw_all' automatically
activate this option.
Level: intermediate
.seealso: EPSGetTrackAll()
@*/
PetscErrorCode EPSSetTrackAll(EPS eps,PetscBool trackall)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidLogicalCollectiveBool(eps,trackall,2);
eps->trackall = trackall;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetTrackAll"
/*@
EPSGetTrackAll - Returns the flag indicating whether all residual norms must
be computed or not.
Not Collective
Input Parameter:
. eps - the eigensolver context
Output Parameter:
. trackall - the returned flag
Level: intermediate
.seealso: EPSSetTrackAll()
@*/
PetscErrorCode EPSGetTrackAll(EPS eps,PetscBool *trackall)
{
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidPointer(trackall,2);
*trackall = eps->trackall;
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSSetOptionsPrefix"
/*@C
EPSSetOptionsPrefix - Sets the prefix used for searching for all
EPS options in the database.
Logically Collective on EPS
Input Parameters:
+ eps - the eigensolver context
- prefix - the prefix string to prepend to all EPS option requests
Notes:
A hyphen (-) must NOT be given at the beginning of the prefix name.
The first character of all runtime options is AUTOMATICALLY the
hyphen.
For example, to distinguish between the runtime options for two
different EPS contexts, one could call
.vb
EPSSetOptionsPrefix(eps1,"eig1_")
EPSSetOptionsPrefix(eps2,"eig2_")
.ve
Level: advanced
.seealso: EPSAppendOptionsPrefix(), EPSGetOptionsPrefix()
@*/
PetscErrorCode EPSSetOptionsPrefix(EPS eps,const char *prefix)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
if (!eps->OP) { ierr = EPSGetST(eps,&eps->OP);CHKERRQ(ierr); }
ierr = STSetOptionsPrefix(eps->OP,prefix);CHKERRQ(ierr);
ierr = PetscObjectSetOptionsPrefix((PetscObject)eps,prefix);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSAppendOptionsPrefix"
/*@C
EPSAppendOptionsPrefix - Appends to the prefix used for searching for all
EPS options in the database.
Logically Collective on EPS
Input Parameters:
+ eps - the eigensolver context
- prefix - the prefix string to prepend to all EPS option requests
Notes:
A hyphen (-) must NOT be given at the beginning of the prefix name.
The first character of all runtime options is AUTOMATICALLY the hyphen.
Level: advanced
.seealso: EPSSetOptionsPrefix(), EPSGetOptionsPrefix()
@*/
PetscErrorCode EPSAppendOptionsPrefix(EPS eps,const char *prefix)
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
if (!eps->OP) { ierr = EPSGetST(eps,&eps->OP);CHKERRQ(ierr); }
ierr = STAppendOptionsPrefix(eps->OP,prefix);CHKERRQ(ierr);
ierr = PetscObjectAppendOptionsPrefix((PetscObject)eps,prefix);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
#undef __FUNCT__
#define __FUNCT__ "EPSGetOptionsPrefix"
/*@C
EPSGetOptionsPrefix - Gets the prefix used for searching for all
EPS options in the database.
Not Collective
Input Parameters:
. eps - the eigensolver context
Output Parameters:
. prefix - pointer to the prefix string used is returned
Notes: On the fortran side, the user should pass in a string 'prefix' of
sufficient length to hold the prefix.
Level: advanced
.seealso: EPSSetOptionsPrefix(), EPSAppendOptionsPrefix()
@*/
PetscErrorCode EPSGetOptionsPrefix(EPS eps,const char *prefix[])
{
PetscErrorCode ierr;
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
PetscValidPointer(prefix,2);
ierr = PetscObjectGetOptionsPrefix((PetscObject)eps,prefix);CHKERRQ(ierr);
PetscFunctionReturn(0);
}