Actual source code: stsles.c

slepc-3.8.3 2018-04-03
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  1: /*
  2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
  4:    Copyright (c) 2002-2017, Universitat Politecnica de Valencia, Spain

  6:    This file is part of SLEPc.
  7:    SLEPc is distributed under a 2-clause BSD license (see LICENSE).
  8:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  9: */
 10: /*
 11:    ST interface routines related to the KSP object associated with it
 12: */

 14: #include <slepc/private/stimpl.h>            /*I "slepcst.h" I*/

 16: /*
 17:    This is used to set a default type for the KSP and PC objects.
 18:    It is called at STSetFromOptions (before KSPSetFromOptions)
 19:    and also at STSetUp (in case STSetFromOptions was not called).
 20: */
 21: PetscErrorCode STSetDefaultKSP(ST st)
 22: {

 28:   if (!st->ksp) { STGetKSP(st,&st->ksp); }
 29:   if (st->ops->setdefaultksp) { (*st->ops->setdefaultksp)(st); }
 30:   return(0);
 31: }

 33: /*
 34:    This is done by all ST types except PRECOND.
 35:    The default is an LU direct solver, or GMRES+Jacobi if matmode=shell.
 36: */
 37: PetscErrorCode STSetDefaultKSP_Default(ST st)
 38: {
 40:   PC             pc;
 41:   PCType         pctype;
 42:   KSPType        ksptype;

 45:   KSPGetPC(st->ksp,&pc);
 46:   KSPGetType(st->ksp,&ksptype);
 47:   PCGetType(pc,&pctype);
 48:   if (!pctype && !ksptype) {
 49:     if (st->shift_matrix == ST_MATMODE_SHELL) {
 50:       KSPSetType(st->ksp,KSPGMRES);
 51:       PCSetType(pc,PCJACOBI);
 52:     } else {
 53:       KSPSetType(st->ksp,KSPPREONLY);
 54:       PCSetType(pc,PCLU);
 55:     }
 56:   }
 57:   KSPSetErrorIfNotConverged(st->ksp,PETSC_TRUE);
 58:   return(0);
 59: }

 61: /*@
 62:    STMatMult - Computes the matrix-vector product y = T[k] x, where T[k] is
 63:    the k-th matrix of the spectral transformation.

 65:    Collective on ST

 67:    Input Parameters:
 68: +  st - the spectral transformation context
 69: .  k  - index of matrix to use
 70: -  x  - the vector to be multiplied

 72:    Output Parameter:
 73: .  y - the result

 75:    Level: developer

 77: .seealso: STMatMultTranspose()
 78: @*/
 79: PetscErrorCode STMatMult(ST st,PetscInt k,Vec x,Vec y)
 80: {

 88:   STCheckMatrices(st,1);
 89:   if (k<0 || k>=PetscMax(2,st->nmat)) SETERRQ1(PetscObjectComm((PetscObject)st),PETSC_ERR_ARG_OUTOFRANGE,"k must be between 0 and %D",st->nmat);
 90:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)st),PETSC_ERR_ARG_IDN,"x and y must be different vectors");
 91:   VecLocked(y,3);

 93:   if (st->state!=ST_STATE_SETUP) { STSetUp(st); }
 94:   VecLockPush(x);
 95:   PetscLogEventBegin(ST_MatMult,st,x,y,0);
 96:   if (!st->T[k]) {
 97:     /* T[k]=NULL means identity matrix */
 98:     VecCopy(x,y);
 99:   } else {
100:     MatMult(st->T[k],x,y);
101:   }
102:   PetscLogEventEnd(ST_MatMult,st,x,y,0);
103:   VecLockPop(x);
104:   return(0);
105: }

107: /*@
108:    STMatMultTranspose - Computes the matrix-vector product y = T[k]' x, where T[k] is
109:    the k-th matrix of the spectral transformation.

111:    Collective on ST

113:    Input Parameters:
114: +  st - the spectral transformation context
115: .  k  - index of matrix to use
116: -  x  - the vector to be multiplied

118:    Output Parameter:
119: .  y - the result

121:    Level: developer

123: .seealso: STMatMult()
124: @*/
125: PetscErrorCode STMatMultTranspose(ST st,PetscInt k,Vec x,Vec y)
126: {

134:   STCheckMatrices(st,1);
135:   if (k<0 || k>=PetscMax(2,st->nmat)) SETERRQ1(PetscObjectComm((PetscObject)st),PETSC_ERR_ARG_OUTOFRANGE,"k must be between 0 and %D",st->nmat);
136:   if (x == y) SETERRQ(PetscObjectComm((PetscObject)st),PETSC_ERR_ARG_IDN,"x and y must be different vectors");
137:   VecLocked(y,3);

139:   if (st->state!=ST_STATE_SETUP) { STSetUp(st); }
140:   VecLockPush(x);
141:   PetscLogEventBegin(ST_MatMultTranspose,st,x,y,0);
142:   if (!st->T[k]) {
143:     /* T[k]=NULL means identity matrix */
144:     VecCopy(x,y);
145:   } else {
146:     MatMultTranspose(st->T[k],x,y);
147:   }
148:   PetscLogEventEnd(ST_MatMultTranspose,st,x,y,0);
149:   VecLockPop(x);
150:   return(0);
151: }

153: /*@
154:    STMatSolve - Solves P x = b, where P is the preconditioner matrix of
155:    the spectral transformation, using a KSP object stored internally.

157:    Collective on ST

159:    Input Parameters:
160: +  st - the spectral transformation context
161: -  b  - right hand side vector

163:    Output Parameter:
164: .  x - computed solution

166:    Level: developer

168: .seealso: STMatSolveTranspose()
169: @*/
170: PetscErrorCode STMatSolve(ST st,Vec b,Vec x)
171: {
173:   PetscInt       its;
174:   PetscBool      flg;

180:   STCheckMatrices(st,1);
181:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)st),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
182:   VecLocked(x,3);

184:   if (st->state!=ST_STATE_SETUP) { STSetUp(st); }
185:   VecLockPush(b);
186:   PetscLogEventBegin(ST_MatSolve,st,b,x,0);
187:   PetscObjectTypeCompareAny((PetscObject)st,&flg,STPRECOND,STSHELL,"");
188:   if (!flg && !st->P) {
189:     /* P=NULL means identity matrix */
190:     VecCopy(b,x);
191:     return(0);
192:   }
193:   if (!st->ksp) { STGetKSP(st,&st->ksp); }
194:   KSPSolve(st->ksp,b,x);
195:   KSPGetIterationNumber(st->ksp,&its);
196:   PetscInfo1(st,"Linear solve iterations=%D\n",its);
197:   PetscLogEventEnd(ST_MatSolve,st,b,x,0);
198:   VecLockPop(b);
199:   return(0);
200: }

202: /*@
203:    STMatSolveTranspose - Solves P' x = b, where P is the preconditioner matrix of
204:    the spectral transformation, using a KSP object stored internally.

206:    Collective on ST

208:    Input Parameters:
209: .  st - the spectral transformation context
210: .  b  - right hand side vector

212:    Output Parameter:
213: .  x - computed solution

215:    Level: developer

217: .seealso: STMatSolve()
218: @*/
219: PetscErrorCode STMatSolveTranspose(ST st,Vec b,Vec x)
220: {
222:   PetscInt       its;
223:   PetscBool      flg;

229:   STCheckMatrices(st,1);
230:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)st),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
231:   VecLocked(x,3);

233:   if (st->state!=ST_STATE_SETUP) { STSetUp(st); }
234:   VecLockPush(b);
235:   PetscLogEventBegin(ST_MatSolveTranspose,st,b,x,0);
236:   PetscObjectTypeCompareAny((PetscObject)st,&flg,STPRECOND,STSHELL,"");
237:   if (!flg && !st->P) {
238:     /* P=NULL means identity matrix */
239:     VecCopy(b,x);
240:     return(0);
241:   }
242:   if (!st->ksp) { STGetKSP(st,&st->ksp); }
243:   KSPSolveTranspose(st->ksp,b,x);
244:   KSPGetIterationNumber(st->ksp,&its);
245:   PetscInfo1(st,"Linear solve iterations=%D\n",its);
246:   PetscLogEventEnd(ST_MatSolveTranspose,st,b,x,0);
247:   VecLockPop(b);
248:   return(0);
249: }

251: /*
252:    STMatSetHermitian - Sets the Hermitian flag to the ST matrix.

254:    Input Parameters:
255: .  st - the spectral transformation context
256: .  M  - matrix
257: */
258: PetscErrorCode STMatSetHermitian(ST st,Mat M)
259: {
260: #if defined(PETSC_USE_COMPLEX)
262:   PetscBool      set,aherm,mherm;
263:   PetscInt       i;
264: #endif

267: #if defined(PETSC_USE_COMPLEX)
268:   if (PetscImaginaryPart(st->sigma)!=0.0) mherm = PETSC_FALSE;
269:   else {
270:     mherm = PETSC_TRUE;
271:     for (i=0;i<st->nmat;i++) {
272:       MatIsHermitianKnown(st->A[i],&set,&aherm);
273:       if (!set) aherm = PETSC_FALSE;
274:       if (!aherm) { mherm = PETSC_FALSE; break; }
275:       if (PetscRealPart(st->sigma)==0.0) break;
276:     }
277:   }
278:   MatSetOption(M,MAT_HERMITIAN,mherm);
279: #endif
280:   return(0);
281: }

283: PetscErrorCode STCheckFactorPackage(ST st)
284: {
285:   PetscErrorCode         ierr;
286:   PC                     pc;
287:   PetscMPIInt            size;
288:   PetscBool              flg;
289:   const MatSolverPackage stype;

292:   MPI_Comm_size(PetscObjectComm((PetscObject)st),&size);
293:   if (size==1) return(0);
294:   KSPGetPC(st->ksp,&pc);
295:   PCFactorGetMatSolverPackage(pc,&stype);
296:   if (stype) {   /* currently selected PC is a factorization */
297:     PetscStrcmp(stype,MATSOLVERPETSC,&flg);
298:     if (flg) SETERRQ(PetscObjectComm((PetscObject)st),PETSC_ERR_SUP,"You chose to solve linear systems with a factorization, but in parallel runs you need to select an external package; see the users guide for details");
299:   }
300:   return(0);
301: }

303: /*@
304:    STSetKSP - Sets the KSP object associated with the spectral
305:    transformation.

307:    Collective on ST

309:    Input Parameters:
310: +  st   - the spectral transformation context
311: -  ksp  - the linear system context

313:    Level: advanced
314: @*/
315: PetscErrorCode STSetKSP(ST st,KSP ksp)
316: {

323:   PetscObjectReference((PetscObject)ksp);
324:   KSPDestroy(&st->ksp);
325:   st->ksp = ksp;
326:   PetscLogObjectParent((PetscObject)st,(PetscObject)st->ksp);
327:   return(0);
328: }

330: /*@
331:    STGetKSP - Gets the KSP object associated with the spectral
332:    transformation.

334:    Not Collective

336:    Input Parameter:
337: .  st - the spectral transformation context

339:    Output Parameter:
340: .  ksp  - the linear system context

342:    Level: intermediate
343: @*/
344: PetscErrorCode STGetKSP(ST st,KSP* ksp)
345: {

351:   if (!st->ksp) {
352:     KSPCreate(PetscObjectComm((PetscObject)st),&st->ksp);
353:     KSPSetOptionsPrefix(st->ksp,((PetscObject)st)->prefix);
354:     KSPAppendOptionsPrefix(st->ksp,"st_");
355:     PetscObjectIncrementTabLevel((PetscObject)st->ksp,(PetscObject)st,1);
356:     PetscLogObjectParent((PetscObject)st,(PetscObject)st->ksp);
357:     KSPSetTolerances(st->ksp,SLEPC_DEFAULT_TOL,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT);
358:   }
359:   *ksp = st->ksp;
360:   return(0);
361: }

363: PetscErrorCode STCheckNullSpace_Default(ST st,BV V)
364: {
366:   PetscInt       nc,i,c;
367:   PetscReal      norm;
368:   Vec            *T,w,vi;
369:   Mat            A;
370:   PC             pc;
371:   MatNullSpace   nullsp;

374:   BVGetNumConstraints(V,&nc);
375:   PetscMalloc1(nc,&T);
376:   if (!st->ksp) { STGetKSP(st,&st->ksp); }
377:   KSPGetPC(st->ksp,&pc);
378:   PCGetOperators(pc,&A,NULL);
379:   MatCreateVecs(A,NULL,&w);
380:   c = 0;
381:   for (i=0;i<nc;i++) {
382:     BVGetColumn(V,-nc+i,&vi);
383:     MatMult(A,vi,w);
384:     VecNorm(w,NORM_2,&norm);
385:     if (norm < 1e-8) {
386:       PetscInfo2(st,"Vector %D norm=%g\n",i,(double)norm);
387:       BVCreateVec(V,T+c);
388:       VecCopy(vi,T[c]);
389:       c++;
390:     }
391:     BVRestoreColumn(V,-nc+i,&vi);
392:   }
393:   VecDestroy(&w);
394:   if (c>0) {
395:     MatNullSpaceCreate(PetscObjectComm((PetscObject)st),PETSC_FALSE,c,T,&nullsp);
396:     MatSetNullSpace(A,nullsp);
397:     MatNullSpaceDestroy(&nullsp);
398:     VecDestroyVecs(c,&T);
399:   } else {
400:     PetscFree(T);
401:   }
402:   return(0);
403: }

405: /*@
406:    STCheckNullSpace - Given a basis vectors object, this function tests each
407:    of its constraint vectors to be a nullspace vector of the coefficient
408:    matrix of the associated KSP object. All these nullspace vectors are passed
409:    to the KSP object.

411:    Collective on ST

413:    Input Parameters:
414: +  st - the spectral transformation context
415: -  V  - basis vectors to be checked

417:    Note:
418:    This function allows to handle singular pencils and to solve some problems
419:    in which the nullspace is important (see the users guide for details).

421:    Level: developer

423: .seealso: EPSSetDeflationSpace()
424: @*/
425: PetscErrorCode STCheckNullSpace(ST st,BV V)
426: {
428:   PetscInt       nc;

435:   if (!st->state) SETERRQ(PetscObjectComm((PetscObject)st),PETSC_ERR_ARG_WRONGSTATE,"Must call STSolve() first");

437:   BVGetNumConstraints(V,&nc);
438:   if (nc && st->ops->checknullspace) {
439:     (*st->ops->checknullspace)(st,V);
440:   }
441:   return(0);
442: }