Actual source code: stsles.c

slepc-3.16.1 2021-11-17
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  1: /*
  2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
  4:    Copyright (c) 2002-2021, 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>

 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->Pmat || st->matmode == ST_MATMODE_SHELL) {
 50:       KSPSetType(st->ksp,KSPGMRES);
 51:       PCSetType(pc,PCJACOBI);
 52:     } else {
 53:       KSPSetType(st->ksp,KSPPREONLY);
 54:       PCSetType(pc,st->asymm?PCCHOLESKY: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:    Neighbor-wise 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:   VecSetErrorIfLocked(y,3);

 93:   if (st->state!=ST_STATE_SETUP) { STSetUp(st); }
 94:   VecLockReadPush(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:   VecLockReadPop(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:    Neighbor-wise 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:   VecSetErrorIfLocked(y,3);

139:   if (st->state!=ST_STATE_SETUP) { STSetUp(st); }
140:   VecLockReadPush(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:   VecLockReadPop(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: {

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

182:   if (st->state!=ST_STATE_SETUP) { STSetUp(st); }
183:   VecLockReadPush(b);
184:   PetscLogEventBegin(ST_MatSolve,st,b,x,0);
185:   if (!st->P) {
186:     /* P=NULL means identity matrix */
187:     VecCopy(b,x);
188:   } else {
189:     KSPSolve(st->ksp,b,x);
190:   }
191:   PetscLogEventEnd(ST_MatSolve,st,b,x,0);
192:   VecLockReadPop(b);
193:   return(0);
194: }

196: /*@
197:    STMatMatSolve - Solves P X = B, where P is the preconditioner matrix of
198:    the spectral transformation, using a KSP object stored internally.

200:    Collective on st

202:    Input Parameters:
203: +  st - the spectral transformation context
204: -  B  - right hand side vectors

206:    Output Parameter:
207: .  X - computed solutions

209:    Level: developer

211: .seealso: STMatSolve()
212: @*/
213: PetscErrorCode STMatMatSolve(ST st,Mat B,Mat X)
214: {

221:   STCheckMatrices(st,1);

223:   if (st->state!=ST_STATE_SETUP) { STSetUp(st); }
224:   PetscLogEventBegin(ST_MatSolve,st,B,X,0);
225:   if (!st->P) {
226:     /* P=NULL means identity matrix */
227:     MatCopy(B,X,SAME_NONZERO_PATTERN);
228:   } else {
229:     KSPMatSolve(st->ksp,B,X);
230:   }
231:   PetscLogEventEnd(ST_MatSolve,st,B,X,0);
232:   return(0);
233: }

235: /*@
236:    STMatSolveTranspose - Solves P' x = b, where P is the preconditioner matrix of
237:    the spectral transformation, using a KSP object stored internally.

239:    Collective on st

241:    Input Parameters:
242: +  st - the spectral transformation context
243: -  b  - right hand side vector

245:    Output Parameter:
246: .  x - computed solution

248:    Level: developer

250: .seealso: STMatSolve()
251: @*/
252: PetscErrorCode STMatSolveTranspose(ST st,Vec b,Vec x)
253: {

260:   STCheckMatrices(st,1);
261:   if (x == b) SETERRQ(PetscObjectComm((PetscObject)st),PETSC_ERR_ARG_IDN,"x and b must be different vectors");
262:   VecSetErrorIfLocked(x,3);

264:   if (st->state!=ST_STATE_SETUP) { STSetUp(st); }
265:   VecLockReadPush(b);
266:   PetscLogEventBegin(ST_MatSolveTranspose,st,b,x,0);
267:   if (!st->P) {
268:     /* P=NULL means identity matrix */
269:     VecCopy(b,x);
270:   } else {
271:     KSPSolveTranspose(st->ksp,b,x);
272:   }
273:   PetscLogEventEnd(ST_MatSolveTranspose,st,b,x,0);
274:   VecLockReadPop(b);
275:   return(0);
276: }

278: PetscErrorCode STCheckFactorPackage(ST st)
279: {
281:   PC             pc;
282:   PetscMPIInt    size;
283:   PetscBool      flg;
284:   MatSolverType  stype;

287:   MPI_Comm_size(PetscObjectComm((PetscObject)st),&size);
288:   if (size==1) return(0);
289:   KSPGetPC(st->ksp,&pc);
290:   PCFactorGetMatSolverType(pc,&stype);
291:   if (stype) {   /* currently selected PC is a factorization */
292:     PetscStrcmp(stype,MATSOLVERPETSC,&flg);
293:     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");
294:   }
295:   return(0);
296: }

298: /*@
299:    STSetKSP - Sets the KSP object associated with the spectral
300:    transformation.

302:    Collective on st

304:    Input Parameters:
305: +  st   - the spectral transformation context
306: -  ksp  - the linear system context

308:    Level: advanced
309: @*/
310: PetscErrorCode STSetKSP(ST st,KSP ksp)
311: {

318:   STCheckNotSeized(st,1);
319:   PetscObjectReference((PetscObject)ksp);
320:   KSPDestroy(&st->ksp);
321:   st->ksp = ksp;
322:   PetscLogObjectParent((PetscObject)st,(PetscObject)st->ksp);
323:   return(0);
324: }

326: /*@
327:    STGetKSP - Gets the KSP object associated with the spectral
328:    transformation.

330:    Not Collective

332:    Input Parameter:
333: .  st - the spectral transformation context

335:    Output Parameter:
336: .  ksp  - the linear system context

338:    Level: intermediate
339: @*/
340: PetscErrorCode STGetKSP(ST st,KSP* ksp)
341: {

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

360: PetscErrorCode STCheckNullSpace_Default(ST st,BV V)
361: {
363:   PetscInt       nc,i,c;
364:   PetscReal      norm;
365:   Vec            *T,w,vi;
366:   Mat            A;
367:   PC             pc;
368:   MatNullSpace   nullsp;

371:   BVGetNumConstraints(V,&nc);
372:   PetscMalloc1(nc,&T);
373:   if (!st->ksp) { STGetKSP(st,&st->ksp); }
374:   KSPGetPC(st->ksp,&pc);
375:   PCGetOperators(pc,&A,NULL);
376:   MatCreateVecs(A,NULL,&w);
377:   c = 0;
378:   for (i=0;i<nc;i++) {
379:     BVGetColumn(V,-nc+i,&vi);
380:     MatMult(A,vi,w);
381:     VecNorm(w,NORM_2,&norm);
382:     if (norm < 10.0*PETSC_SQRT_MACHINE_EPSILON) {
383:       PetscInfo2(st,"Vector %D included in the nullspace of OP, norm=%g\n",i,(double)norm);
384:       BVCreateVec(V,T+c);
385:       VecCopy(vi,T[c]);
386:       VecNormalize(T[c],NULL);
387:       c++;
388:     } else {
389:       PetscInfo2(st,"Vector %D discarded as possible nullspace of OP, norm=%g\n",i,(double)norm);
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 STSetUp() first");

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