Date: 2021-02-01T14:41:50+00:00

Actual source code: stsles.c
slepc-3.14.2 2021-02-01
Report Typos and Errors

      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-2020, 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->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 < 1e-8) {
    383:       PetscInfo2(st,"Vector %D norm=%g\n",i,(double)norm);
    384:       BVCreateVec(V,T+c);
    385:       VecCopy(vi,T[c]);
    386:       VecNormalize(T[c],NULL);
    387:       c++;
    388:     }
    389:     BVRestoreColumn(V,-nc+i,&vi);
    390:   }
    391:   VecDestroy(&w);
    392:   if (c>0) {
    393:     MatNullSpaceCreate(PetscObjectComm((PetscObject)st),PETSC_FALSE,c,T,&nullsp);
    394:     MatSetNullSpace(A,nullsp);
    395:     MatNullSpaceDestroy(&nullsp);
    396:     VecDestroyVecs(c,&T);
    397:   } else {
    398:     PetscFree(T);
    399:   }
    400:   return(0);
    401: }

    403: /*@
    404:    STCheckNullSpace - Given a basis vectors object, this function tests each
    405:    of its constraint vectors to be a nullspace vector of the coefficient
    406:    matrix of the associated KSP object. All these nullspace vectors are passed
    407:    to the KSP object.

    409:    Collective on st

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

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

    419:    Level: developer

    421: .seealso: EPSSetDeflationSpace()
    422: @*/
    423: PetscErrorCode STCheckNullSpace(ST st,BV V)
    424: {
    426:   PetscInt       nc;

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

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