PetscErrorCode MatMatSolve_SuperLU(Mat A,Mat B,Mat X)
{
Mat_SuperLU *lu = (Mat_SuperLU*)A->spptr;
PetscBool flg;
PetscErrorCode ierr;
PetscFunctionBegin;
ierr = PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,PETSC_NULL);CHKERRQ(ierr);
if (!flg) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
ierr = PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,PETSC_NULL);CHKERRQ(ierr);
if (!flg) SETERRQ(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix"); lu->options.Trans = TRANS;
SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatMatSolve_SuperLU() is not implemented yet");
PetscFunctionReturn(0);
}
static PetscErrorCode PCBDDCMatTransposeMatSolve_SeqDense(Mat A,Mat B,Mat X)
{
Mat_SeqDense *mat = (Mat_SeqDense*)A->data;
PetscErrorCode ierr;
const PetscScalar *b;
PetscScalar *x;
PetscInt n;
PetscBLASInt nrhs,info,m;
PetscBool flg;
PetscFunctionBegin;
ierr = PetscBLASIntCast(A->rmap->n,&m);CHKERRQ(ierr);
ierr = PetscObjectTypeCompareAny((PetscObject)B,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr);
if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
ierr = PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr);
if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");
ierr = MatGetSize(B,NULL,&n);CHKERRQ(ierr);
ierr = PetscBLASIntCast(n,&nrhs);CHKERRQ(ierr);
ierr = MatDenseGetArrayRead(B,&b);CHKERRQ(ierr);
ierr = MatDenseGetArray(X,&x);CHKERRQ(ierr);
ierr = PetscMemcpy(x,b,m*nrhs*sizeof(PetscScalar));CHKERRQ(ierr);
ierr = MatDenseRestoreArrayRead(B,&b);CHKERRQ(ierr);
if (A->factortype == MAT_FACTOR_LU) {
#if defined(PETSC_MISSING_LAPACK_GETRS)
SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"GETRS - Lapack routine is unavailable.");
#else
PetscStackCallBLAS("LAPACKgetrs",LAPACKgetrs_("T",&m,&nrhs,mat->v,&mat->lda,mat->pivots,x,&m,&info));
if (info) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"GETRS - Bad solve");
#endif
} else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only LU factor supported");
ierr = MatDenseRestoreArray(X,&x);CHKERRQ(ierr);
ierr = PetscLogFlops(nrhs*(2.0*m*m - m));CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*
EPSComputeVectors_XD - Compute eigenvectors from the vectors
provided by the eigensolver. This version is intended for solvers
that provide Schur vectors from the QZ decomposition. Given the partial
Schur decomposition OP*V=V*T, the following steps are performed:
1) compute eigenvectors of (S,T): S*Z=T*Z*D
2) compute eigenvectors of OP: X=V*Z
*/
PetscErrorCode EPSComputeVectors_XD(EPS eps)
{
PetscErrorCode ierr;
Mat X;
PetscBool symm;
PetscFunctionBegin;
ierr = PetscObjectTypeCompareAny((PetscObject)eps->ds,&symm,DSHEP,"");CHKERRQ(ierr);
if (symm) PetscFunctionReturn(0);
ierr = DSVectors(eps->ds,DS_MAT_X,NULL,NULL);CHKERRQ(ierr);
ierr = DSNormalize(eps->ds,DS_MAT_X,-1);CHKERRQ(ierr);
/* V <- V * X */
ierr = DSGetMat(eps->ds,DS_MAT_X,&X);CHKERRQ(ierr);
ierr = BVSetActiveColumns(eps->V,0,eps->nconv);CHKERRQ(ierr);
ierr = BVMultInPlace(eps->V,X,0,eps->nconv);CHKERRQ(ierr);
ierr = DSRestoreMat(eps->ds,DS_MAT_X,&X);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
/*
PEPBuildDiagonalScaling - compute two diagonal matrices to be applied for balancing
in polynomial eigenproblems.
*/
PetscErrorCode PEPBuildDiagonalScaling(PEP pep)
{
PetscErrorCode ierr;
PetscInt it,i,j,k,nmat,nr,e,nz,lst,lend,nc=0,*cols,emax,emin,emaxl,eminl;
const PetscInt *cidx,*ridx;
Mat M,*T,A;
PetscMPIInt n;
PetscBool cont=PETSC_TRUE,flg=PETSC_FALSE;
PetscScalar *array,*Dr,*Dl,t;
PetscReal l2,d,*rsum,*aux,*csum,w=1.0;
MatStructure str;
MatInfo info;
PetscFunctionBegin;
l2 = 2*PetscLogReal(2.0);
nmat = pep->nmat;
ierr = PetscMPIIntCast(pep->n,&n);
ierr = STGetMatStructure(pep->st,&str);CHKERRQ(ierr);
ierr = PetscMalloc1(nmat,&T);CHKERRQ(ierr);
for (k=0;k<nmat;k++) {
ierr = STGetTOperators(pep->st,k,&T[k]);CHKERRQ(ierr);
}
/* Form local auxiliar matrix M */
ierr = PetscObjectTypeCompareAny((PetscObject)T[0],&cont,MATMPIAIJ,MATSEQAIJ);CHKERRQ(ierr);
if (!cont) SETERRQ(PetscObjectComm((PetscObject)T[0]),PETSC_ERR_SUP,"Only for MPIAIJ or SEQAIJ matrix types");
ierr = PetscObjectTypeCompare((PetscObject)T[0],MATMPIAIJ,&cont);CHKERRQ(ierr);
if (cont) {
ierr = MatMPIAIJGetLocalMat(T[0],MAT_INITIAL_MATRIX,&M);CHKERRQ(ierr);
flg = PETSC_TRUE;
} else {
ierr = MatDuplicate(T[0],MAT_COPY_VALUES,&M);CHKERRQ(ierr);
}
ierr = MatGetInfo(M,MAT_LOCAL,&info);CHKERRQ(ierr);
nz = info.nz_used;
ierr = MatSeqAIJGetArray(M,&array);CHKERRQ(ierr);
for (i=0;i<nz;i++) {
t = PetscAbsScalar(array[i]);
array[i] = t*t;
}
ierr = MatSeqAIJRestoreArray(M,&array);CHKERRQ(ierr);
for (k=1;k<nmat;k++) {
if (flg) {
ierr = MatMPIAIJGetLocalMat(T[k],MAT_INITIAL_MATRIX,&A);CHKERRQ(ierr);
} else {
if (str==SAME_NONZERO_PATTERN) {
ierr = MatCopy(T[k],A,SAME_NONZERO_PATTERN);CHKERRQ(ierr);
} else {
ierr = MatDuplicate(T[k],MAT_COPY_VALUES,&A);CHKERRQ(ierr);
}
}
ierr = MatGetInfo(A,MAT_LOCAL,&info);CHKERRQ(ierr);
nz = info.nz_used;
ierr = MatSeqAIJGetArray(A,&array);CHKERRQ(ierr);
for (i=0;i<nz;i++) {
t = PetscAbsScalar(array[i]);
array[i] = t*t;
}
ierr = MatSeqAIJRestoreArray(A,&array);CHKERRQ(ierr);
w *= pep->slambda*pep->slambda*pep->sfactor;
ierr = MatAXPY(M,w,A,str);CHKERRQ(ierr);
if (flg || str!=SAME_NONZERO_PATTERN || k==nmat-2) {
ierr = MatDestroy(&A);CHKERRQ(ierr);
}
}
ierr = MatGetRowIJ(M,0,PETSC_FALSE,PETSC_FALSE,&nr,&ridx,&cidx,&cont);CHKERRQ(ierr);
if (!cont) SETERRQ(PetscObjectComm((PetscObject)T[0]), PETSC_ERR_SUP,"It is not possible to compute scaling diagonals for these PEP matrices");
ierr = MatGetInfo(M,MAT_LOCAL,&info);CHKERRQ(ierr);
nz = info.nz_used;
ierr = VecGetOwnershipRange(pep->Dl,&lst,&lend);CHKERRQ(ierr);
ierr = PetscMalloc4(nr,&rsum,pep->n,&csum,pep->n,&aux,PetscMin(pep->n-lend+lst,nz),&cols);CHKERRQ(ierr);
ierr = VecSet(pep->Dr,1.0);CHKERRQ(ierr);
ierr = VecSet(pep->Dl,1.0);CHKERRQ(ierr);
ierr = VecGetArray(pep->Dl,&Dl);CHKERRQ(ierr);
ierr = VecGetArray(pep->Dr,&Dr);CHKERRQ(ierr);
ierr = MatSeqAIJGetArray(M,&array);CHKERRQ(ierr);
ierr = PetscMemzero(aux,pep->n*sizeof(PetscReal));CHKERRQ(ierr);
for (j=0;j<nz;j++) {
/* Search non-zero columns outsize lst-lend */
if (aux[cidx[j]]==0 && (cidx[j]<lst || lend<=cidx[j])) cols[nc++] = cidx[j];
/* Local column sums */
aux[cidx[j]] += PetscAbsScalar(array[j]);
}
for (it=0;it<pep->sits && cont;it++) {
emaxl = 0; eminl = 0;
/* Column sum */
if (it>0) { /* it=0 has been already done*/
ierr = MatSeqAIJGetArray(M,&array);CHKERRQ(ierr);
ierr = PetscMemzero(aux,pep->n*sizeof(PetscReal));CHKERRQ(ierr);
for (j=0;j<nz;j++) aux[cidx[j]] += PetscAbsScalar(array[j]);
ierr = MatSeqAIJRestoreArray(M,&array);CHKERRQ(ierr);
}
ierr = MPI_Allreduce(aux,csum,n,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)pep->Dr));
/* Update Dr */
for (j=lst;j<lend;j++) {
d = PetscLogReal(csum[j])/l2;
e = -(PetscInt)((d < 0)?(d-0.5):(d+0.5));
d = PetscPowReal(2.0,e);
Dr[j-lst] *= d;
aux[j] = d*d;
emaxl = PetscMax(emaxl,e);
eminl = PetscMin(eminl,e);
}
for (j=0;j<nc;j++) {
d = PetscLogReal(csum[cols[j]])/l2;
e = -(PetscInt)((d < 0)?(d-0.5):(d+0.5));
d = PetscPowReal(2.0,e);
aux[cols[j]] = d*d;
emaxl = PetscMax(emaxl,e);
eminl = PetscMin(eminl,e);
}
/* Scale M */
ierr = MatSeqAIJGetArray(M,&array);CHKERRQ(ierr);
for (j=0;j<nz;j++) {
array[j] *= aux[cidx[j]];
}
ierr = MatSeqAIJRestoreArray(M,&array);CHKERRQ(ierr);
/* Row sum */
ierr = PetscMemzero(rsum,nr*sizeof(PetscReal));CHKERRQ(ierr);
ierr = MatSeqAIJGetArray(M,&array);CHKERRQ(ierr);
for (i=0;i<nr;i++) {
for (j=ridx[i];j<ridx[i+1];j++) rsum[i] += PetscAbsScalar(array[j]);
/* Update Dl */
d = PetscLogReal(rsum[i])/l2;
e = -(PetscInt)((d < 0)?(d-0.5):(d+0.5));
d = PetscPowReal(2.0,e);
Dl[i] *= d;
/* Scale M */
for (j=ridx[i];j<ridx[i+1];j++) array[j] *= d*d;
emaxl = PetscMax(emaxl,e);
eminl = PetscMin(eminl,e);
}
ierr = MatSeqAIJRestoreArray(M,&array);CHKERRQ(ierr);
/* Compute global max and min */
ierr = MPI_Allreduce(&emaxl,&emax,1,MPIU_INT,MPIU_MAX,PetscObjectComm((PetscObject)pep->Dl));
ierr = MPI_Allreduce(&eminl,&emin,1,MPIU_INT,MPIU_MIN,PetscObjectComm((PetscObject)pep->Dl));
if (emax<=emin+2) cont = PETSC_FALSE;
}
ierr = VecRestoreArray(pep->Dr,&Dr);CHKERRQ(ierr);
ierr = VecRestoreArray(pep->Dl,&Dl);CHKERRQ(ierr);
/* Free memory*/
ierr = MatDestroy(&M);CHKERRQ(ierr);
ierr = PetscFree4(rsum,csum,aux,cols);CHKERRQ(ierr);
ierr = PetscFree(T);CHKERRQ(ierr);
PetscFunctionReturn(0);
}
PetscErrorCode MatMatSolve_SuperLU_DIST(Mat A,Mat B_mpi,Mat X)
{
Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->spptr;
PetscErrorCode ierr;
PetscMPIInt size;
PetscInt M=A->rmap->N,m=A->rmap->n,nrhs;
SuperLUStat_t stat;
double berr[1];
PetscScalar *bptr;
int info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
PetscBool flg;
PetscFunctionBegin;
if (lu->options.Fact != FACTORED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"SuperLU_DIST options.Fact mush equal FACTORED");
ierr = PetscObjectTypeCompareAny((PetscObject)B_mpi,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr);
if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
ierr = PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr);
if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");
ierr = MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);CHKERRQ(ierr);
if (size > 1 && lu->MatInputMode == GLOBAL) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatInputMode=GLOBAL for nproc %d>1 is not supported",size);
/* size==1 or distributed mat input */
if (lu->options.SolveInitialized && !lu->matmatsolve_iscalled) {
/* communication pattern of SOLVEstruct is unlikely created for matmatsolve,
thus destroy it and create a new SOLVEstruct.
Otherwise it may result in memory corruption or incorrect solution
See src/mat/examples/tests/ex125.c */
#if defined(PETSC_USE_COMPLEX)
PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
#else
PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
#endif
lu->options.SolveInitialized = NO;
}
ierr = MatCopy(B_mpi,X,SAME_NONZERO_PATTERN);CHKERRQ(ierr);
ierr = MatGetSize(B_mpi,NULL,&nrhs);CHKERRQ(ierr);
PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat)); /* Initialize the statistics variables. */
ierr = MatDenseGetArray(X,&bptr);CHKERRQ(ierr);
if (lu->MatInputMode == GLOBAL) { /* size == 1 */
#if defined(PETSC_USE_COMPLEX)
PetscStackCall("SuperLU_DIST:pzgssvx_ABglobal",pzgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,(doublecomplex*)bptr, M, nrhs,&lu->grid, &lu->LUstruct, berr, &stat, &info));
#else
PetscStackCall("SuperLU_DIST:pdgssvx_ABglobal",pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,bptr, M, nrhs, &lu->grid, &lu->LUstruct, berr, &stat, &info));
#endif
} else { /* distributed mat input */
#if defined(PETSC_USE_COMPLEX)
PetscStackCall("SuperLU_DIST:pzgssvx",pzgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,(doublecomplex*)bptr,m,nrhs,&lu->grid, &lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
#else
PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
#endif
}
if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);
ierr = MatDenseRestoreArray(X,&bptr);CHKERRQ(ierr);
if (lu->options.PrintStat) PStatPrint(&lu->options, &stat, &lu->grid); /* Print the statistics. */
PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
lu->matsolve_iscalled = PETSC_FALSE;
lu->matmatsolve_iscalled = PETSC_TRUE;
PetscFunctionReturn(0);
}
/*@C
EPSView - Prints the EPS data structure.
Collective on EPS
Input Parameters:
+ eps - the eigenproblem solver context
- viewer - optional visualization context
Options Database Key:
. -eps_view - Calls EPSView() at end of EPSSolve()
Note:
The available visualization contexts include
+ PETSC_VIEWER_STDOUT_SELF - standard output (default)
- PETSC_VIEWER_STDOUT_WORLD - synchronized standard
output where only the first processor opens
the file. All other processors send their
data to the first processor to print.
The user can open an alternative visualization context with
PetscViewerASCIIOpen() - output to a specified file.
Level: beginner
.seealso: STView(), PetscViewerASCIIOpen()
@*/
PetscErrorCode EPSView(EPS eps,PetscViewer viewer)
{
PetscErrorCode ierr;
const char *type,*extr,*bal;
char str[50];
PetscBool isascii,ispower,isexternal,istrivial;
PetscFunctionBegin;
PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
if (!viewer) viewer = PETSC_VIEWER_STDOUT_(PetscObjectComm((PetscObject)eps));
PetscValidHeaderSpecific(viewer,PETSC_VIEWER_CLASSID,2);
PetscCheckSameComm(eps,1,viewer,2);
#if defined(PETSC_USE_COMPLEX)
#define HERM "hermitian"
#else
#define HERM "symmetric"
#endif
ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii);
CHKERRQ(ierr);
if (isascii) {
ierr = PetscObjectPrintClassNamePrefixType((PetscObject)eps,viewer);
CHKERRQ(ierr);
if (eps->ops->view) {
ierr = PetscViewerASCIIPushTab(viewer);
CHKERRQ(ierr);
ierr = (*eps->ops->view)(eps,viewer);
CHKERRQ(ierr);
ierr = PetscViewerASCIIPopTab(viewer);
CHKERRQ(ierr);
}
if (eps->problem_type) {
switch (eps->problem_type) {
case EPS_HEP:
type = HERM " eigenvalue problem";
break;
case EPS_GHEP:
type = "generalized " HERM " eigenvalue problem";
break;
case EPS_NHEP:
type = "non-" HERM " eigenvalue problem";
break;
case EPS_GNHEP:
type = "generalized non-" HERM " eigenvalue problem";
break;
case EPS_PGNHEP:
type = "generalized non-" HERM " eigenvalue problem with " HERM " positive definite B";
break;
case EPS_GHIEP:
type = "generalized " HERM "-indefinite eigenvalue problem";
break;
default:
SETERRQ(PetscObjectComm((PetscObject)eps),1,"Wrong value of eps->problem_type");
}
} else type = "not yet set";
ierr = PetscViewerASCIIPrintf(viewer," problem type: %s\n",type);
CHKERRQ(ierr);
if (eps->extraction) {
switch (eps->extraction) {
case EPS_RITZ:
extr = "Rayleigh-Ritz";
break;
case EPS_HARMONIC:
extr = "harmonic Ritz";
break;
case EPS_HARMONIC_RELATIVE:
extr = "relative harmonic Ritz";
break;
case EPS_HARMONIC_RIGHT:
extr = "right harmonic Ritz";
break;
case EPS_HARMONIC_LARGEST:
extr = "largest harmonic Ritz";
break;
case EPS_REFINED:
extr = "refined Ritz";
break;
case EPS_REFINED_HARMONIC:
extr = "refined harmonic Ritz";
break;
default:
SETERRQ(PetscObjectComm((PetscObject)eps),1,"Wrong value of eps->extraction");
}
ierr = PetscViewerASCIIPrintf(viewer," extraction type: %s\n",extr);
CHKERRQ(ierr);
}
if (!eps->ishermitian && eps->balance!=EPS_BALANCE_NONE) {
switch (eps->balance) {
case EPS_BALANCE_ONESIDE:
bal = "one-sided Krylov";
break;
case EPS_BALANCE_TWOSIDE:
bal = "two-sided Krylov";
break;
case EPS_BALANCE_USER:
bal = "user-defined matrix";
break;
default:
SETERRQ(PetscObjectComm((PetscObject)eps),1,"Wrong value of eps->balance");
}
ierr = PetscViewerASCIIPrintf(viewer," balancing enabled: %s",bal);
CHKERRQ(ierr);
if (eps->balance==EPS_BALANCE_ONESIDE || eps->balance==EPS_BALANCE_TWOSIDE) {
ierr = PetscViewerASCIIPrintf(viewer,", with its=%D",eps->balance_its);
CHKERRQ(ierr);
}
if (eps->balance==EPS_BALANCE_TWOSIDE && eps->balance_cutoff!=0.0) {
ierr = PetscViewerASCIIPrintf(viewer," and cutoff=%g",(double)eps->balance_cutoff);
CHKERRQ(ierr);
}
ierr = PetscViewerASCIIPrintf(viewer,"\n");
CHKERRQ(ierr);
}
ierr = PetscViewerASCIIPrintf(viewer," selected portion of the spectrum: ");
CHKERRQ(ierr);
ierr = SlepcSNPrintfScalar(str,50,eps->target,PETSC_FALSE);
CHKERRQ(ierr);
if (!eps->which) {
ierr = PetscViewerASCIIPrintf(viewer,"not yet set\n");
CHKERRQ(ierr);
} else switch (eps->which) {
case EPS_WHICH_USER:
ierr = PetscViewerASCIIPrintf(viewer,"user defined\n");
CHKERRQ(ierr);
break;
case EPS_TARGET_MAGNITUDE:
ierr = PetscViewerASCIIPrintf(viewer,"closest to target: %s (in magnitude)\n",str);
CHKERRQ(ierr);
break;
case EPS_TARGET_REAL:
ierr = PetscViewerASCIIPrintf(viewer,"closest to target: %s (along the real axis)\n",str);
CHKERRQ(ierr);
break;
case EPS_TARGET_IMAGINARY:
ierr = PetscViewerASCIIPrintf(viewer,"closest to target: %s (along the imaginary axis)\n",str);
CHKERRQ(ierr);
break;
case EPS_LARGEST_MAGNITUDE:
ierr = PetscViewerASCIIPrintf(viewer,"largest eigenvalues in magnitude\n");
CHKERRQ(ierr);
break;
case EPS_SMALLEST_MAGNITUDE:
ierr = PetscViewerASCIIPrintf(viewer,"smallest eigenvalues in magnitude\n");
CHKERRQ(ierr);
break;
case EPS_LARGEST_REAL:
ierr = PetscViewerASCIIPrintf(viewer,"largest real parts\n");
CHKERRQ(ierr);
break;
case EPS_SMALLEST_REAL:
ierr = PetscViewerASCIIPrintf(viewer,"smallest real parts\n");
CHKERRQ(ierr);
break;
case EPS_LARGEST_IMAGINARY:
ierr = PetscViewerASCIIPrintf(viewer,"largest imaginary parts\n");
CHKERRQ(ierr);
break;
case EPS_SMALLEST_IMAGINARY:
ierr = PetscViewerASCIIPrintf(viewer,"smallest imaginary parts\n");
CHKERRQ(ierr);
break;
case EPS_ALL:
ierr = PetscViewerASCIIPrintf(viewer,"all eigenvalues in interval [%g,%g]\n",(double)eps->inta,(double)eps->intb);
CHKERRQ(ierr);
break;
default:
SETERRQ(PetscObjectComm((PetscObject)eps),1,"Wrong value of eps->which");
}
if (eps->trueres) {
ierr = PetscViewerASCIIPrintf(viewer," computing true residuals explicitly\n");
CHKERRQ(ierr);
}
if (eps->trackall) {
ierr = PetscViewerASCIIPrintf(viewer," computing all residuals (for tracking convergence)\n");
CHKERRQ(ierr);
}
ierr = PetscViewerASCIIPrintf(viewer," number of eigenvalues (nev): %D\n",eps->nev);
CHKERRQ(ierr);
ierr = PetscViewerASCIIPrintf(viewer," number of column vectors (ncv): %D\n",eps->ncv);
CHKERRQ(ierr);
ierr = PetscViewerASCIIPrintf(viewer," maximum dimension of projected problem (mpd): %D\n",eps->mpd);
CHKERRQ(ierr);
ierr = PetscViewerASCIIPrintf(viewer," maximum number of iterations: %D\n",eps->max_it);
CHKERRQ(ierr);
ierr = PetscViewerASCIIPrintf(viewer," tolerance: %g\n",(double)eps->tol);
CHKERRQ(ierr);
ierr = PetscViewerASCIIPrintf(viewer," convergence test: ");
CHKERRQ(ierr);
switch (eps->conv) {
case EPS_CONV_ABS:
ierr = PetscViewerASCIIPrintf(viewer,"absolute\n");
CHKERRQ(ierr);
break;
case EPS_CONV_EIG:
ierr = PetscViewerASCIIPrintf(viewer,"relative to the eigenvalue\n");
CHKERRQ(ierr);
break;
case EPS_CONV_NORM:
ierr = PetscViewerASCIIPrintf(viewer,"relative to the eigenvalue and matrix norms\n");
CHKERRQ(ierr);
ierr = PetscViewerASCIIPrintf(viewer," computed matrix norms: norm(A)=%g",(double)eps->nrma);
CHKERRQ(ierr);
if (eps->isgeneralized) {
ierr = PetscViewerASCIIPrintf(viewer,", norm(B)=%g",(double)eps->nrmb);
CHKERRQ(ierr);
}
ierr = PetscViewerASCIIPrintf(viewer,"\n");
CHKERRQ(ierr);
break;
case EPS_CONV_USER:
ierr = PetscViewerASCIIPrintf(viewer,"user-defined\n");
CHKERRQ(ierr);
break;
}
if (eps->nini) {
ierr = PetscViewerASCIIPrintf(viewer," dimension of user-provided initial space: %D\n",PetscAbs(eps->nini));
CHKERRQ(ierr);
}
if (eps->nds) {
ierr = PetscViewerASCIIPrintf(viewer," dimension of user-provided deflation space: %D\n",PetscAbs(eps->nds));
CHKERRQ(ierr);
}
} else {
if (eps->ops->view) {
ierr = (*eps->ops->view)(eps,viewer);
CHKERRQ(ierr);
}
}
ierr = PetscObjectTypeCompareAny((PetscObject)eps,&isexternal,EPSARPACK,EPSBLZPACK,EPSTRLAN,EPSBLOPEX,EPSPRIMME,"");
CHKERRQ(ierr);
if (!isexternal) {
ierr = PetscViewerPushFormat(viewer,PETSC_VIEWER_ASCII_INFO);
CHKERRQ(ierr);
if (!eps->V) {
ierr = EPSGetBV(eps,&eps->V);
CHKERRQ(ierr);
}
ierr = BVView(eps->V,viewer);
CHKERRQ(ierr);
if (!eps->rg) {
ierr = EPSGetRG(eps,&eps->rg);
CHKERRQ(ierr);
}
ierr = RGIsTrivial(eps->rg,&istrivial);
CHKERRQ(ierr);
if (!istrivial) {
ierr = RGView(eps->rg,viewer);
CHKERRQ(ierr);
}
ierr = PetscObjectTypeCompare((PetscObject)eps,EPSPOWER,&ispower);
CHKERRQ(ierr);
if (!ispower) {
if (!eps->ds) {
ierr = EPSGetDS(eps,&eps->ds);
CHKERRQ(ierr);
}
ierr = DSView(eps->ds,viewer);
CHKERRQ(ierr);
}
ierr = PetscViewerPopFormat(viewer);
CHKERRQ(ierr);
}
if (!eps->st) {
ierr = EPSGetST(eps,&eps->st);
CHKERRQ(ierr);
}
ierr = STView(eps->st,viewer);
CHKERRQ(ierr);
PetscFunctionReturn(0);
}
