Actual source code: pepdefault.c
slepc-3.21.1 2024-04-26
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      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-, 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:    Simple default routines for common PEP operations
     12: */

     14: #include <slepc/private/pepimpl.h>

     16: /*@
     17:    PEPSetWorkVecs - Sets a number of work vectors into a PEP object.

     19:    Collective

     21:    Input Parameters:
     22: +  pep - polynomial eigensolver context
     23: -  nw  - number of work vectors to allocate

     25:    Developer Notes:
     26:    This is SLEPC_EXTERN because it may be required by user plugin PEP
     27:    implementations.

     29:    Level: developer

     31: .seealso: PEPSetUp()
     32: @*/
     33: PetscErrorCode PEPSetWorkVecs(PEP pep,PetscInt nw)
     34: {
     35:   Vec            t;

     37:   PetscFunctionBegin;
     40:   PetscCheck(nw>0,PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_OUTOFRANGE,"nw must be > 0: nw = %" PetscInt_FMT,nw);
     41:   if (pep->nwork < nw) {
     42:     PetscCall(VecDestroyVecs(pep->nwork,&pep->work));
     43:     pep->nwork = nw;
     44:     PetscCall(BVGetColumn(pep->V,0,&t));
     45:     PetscCall(VecDuplicateVecs(t,nw,&pep->work));
     46:     PetscCall(BVRestoreColumn(pep->V,0,&t));
     47:   }
     48:   PetscFunctionReturn(PETSC_SUCCESS);
     49: }

     51: /*
     52:   PEPConvergedRelative - Checks convergence relative to the eigenvalue.
     53: */
     54: PetscErrorCode PEPConvergedRelative(PEP pep,PetscScalar eigr,PetscScalar eigi,PetscReal res,PetscReal *errest,void *ctx)
     55: {
     56:   PetscReal w;

     58:   PetscFunctionBegin;
     59:   w = SlepcAbsEigenvalue(eigr,eigi);
     60:   *errest = res/w;
     61:   PetscFunctionReturn(PETSC_SUCCESS);
     62: }

     64: /*
     65:   PEPConvergedNorm - Checks convergence relative to the matrix norms.
     66: */
     67: PetscErrorCode PEPConvergedNorm(PEP pep,PetscScalar eigr,PetscScalar eigi,PetscReal res,PetscReal *errest,void *ctx)
     68: {
     69:   PetscReal      w=0.0,t;
     70:   PetscInt       j;
     71:   PetscBool      flg;

     73:   PetscFunctionBegin;
     74:   /* initialization of matrix norms */
     75:   if (!pep->nrma[pep->nmat-1]) {
     76:     for (j=0;j<pep->nmat;j++) {
     77:       PetscCall(MatHasOperation(pep->A[j],MATOP_NORM,&flg));
     78:       PetscCheck(flg,PetscObjectComm((PetscObject)pep),PETSC_ERR_ARG_WRONG,"The convergence test related to the matrix norms requires a matrix norm operation");
     79:       PetscCall(MatNorm(pep->A[j],NORM_INFINITY,&pep->nrma[j]));
     80:     }
     81:   }
     82:   t = SlepcAbsEigenvalue(eigr,eigi);
     83:   for (j=pep->nmat-1;j>=0;j--) {
     84:     w = w*t+pep->nrma[j];
     85:   }
     86:   *errest = res/w;
     87:   PetscFunctionReturn(PETSC_SUCCESS);
     88: }

     90: /*
     91:   PEPSetWhichEigenpairs_Default - Sets the default value for which,
     92:   depending on the ST.
     93:  */
     94: PetscErrorCode PEPSetWhichEigenpairs_Default(PEP pep)
     95: {
     96:   PetscBool      target;

     98:   PetscFunctionBegin;
     99:   PetscCall(PetscObjectTypeCompare((PetscObject)pep->st,STSINVERT,&target));
    100:   if (target) pep->which = PEP_TARGET_MAGNITUDE;
    101:   else pep->which = PEP_LARGEST_MAGNITUDE;
    102:   PetscFunctionReturn(PETSC_SUCCESS);
    103: }

    105: /*
    106:   PEPConvergedAbsolute - Checks convergence absolutely.
    107: */
    108: PetscErrorCode PEPConvergedAbsolute(PEP pep,PetscScalar eigr,PetscScalar eigi,PetscReal res,PetscReal *errest,void *ctx)
    109: {
    110:   PetscFunctionBegin;
    111:   *errest = res;
    112:   PetscFunctionReturn(PETSC_SUCCESS);
    113: }

    115: /*@C
    116:    PEPStoppingBasic - Default routine to determine whether the outer eigensolver
    117:    iteration must be stopped.

    119:    Collective

    121:    Input Parameters:
    122: +  pep    - eigensolver context obtained from PEPCreate()
    123: .  its    - current number of iterations
    124: .  max_it - maximum number of iterations
    125: .  nconv  - number of currently converged eigenpairs
    126: .  nev    - number of requested eigenpairs
    127: -  ctx    - context (not used here)

    129:    Output Parameter:
    130: .  reason - result of the stopping test

    132:    Notes:
    133:    A positive value of reason indicates that the iteration has finished successfully
    134:    (converged), and a negative value indicates an error condition (diverged). If
    135:    the iteration needs to be continued, reason must be set to PEP_CONVERGED_ITERATING
    136:    (zero).

    138:    PEPStoppingBasic() will stop if all requested eigenvalues are converged, or if
    139:    the maximum number of iterations has been reached.

    141:    Use PEPSetStoppingTest() to provide your own test instead of using this one.

    143:    Level: advanced

    145: .seealso: PEPSetStoppingTest(), PEPConvergedReason, PEPGetConvergedReason()
    146: @*/
    147: PetscErrorCode PEPStoppingBasic(PEP pep,PetscInt its,PetscInt max_it,PetscInt nconv,PetscInt nev,PEPConvergedReason *reason,void *ctx)
    148: {
    149:   PetscFunctionBegin;
    150:   *reason = PEP_CONVERGED_ITERATING;
    151:   if (nconv >= nev) {
    152:     PetscCall(PetscInfo(pep,"Polynomial eigensolver finished successfully: %" PetscInt_FMT " eigenpairs converged at iteration %" PetscInt_FMT "\n",nconv,its));
    153:     *reason = PEP_CONVERGED_TOL;
    154:   } else if (its >= max_it) {
    155:     *reason = PEP_DIVERGED_ITS;
    156:     PetscCall(PetscInfo(pep,"Polynomial eigensolver iteration reached maximum number of iterations (%" PetscInt_FMT ")\n",its));
    157:   }
    158:   PetscFunctionReturn(PETSC_SUCCESS);
    159: }

    161: PetscErrorCode PEPBackTransform_Default(PEP pep)
    162: {
    163:   PetscFunctionBegin;
    164:   PetscCall(STBackTransform(pep->st,pep->nconv,pep->eigr,pep->eigi));
    165:   PetscFunctionReturn(PETSC_SUCCESS);
    166: }

    168: PetscErrorCode PEPComputeVectors_Default(PEP pep)
    169: {
    170:   PetscInt       i;
    171:   Vec            v;

    173:   PetscFunctionBegin;
    174:   PetscCall(PEPExtractVectors(pep));

    176:   /* Fix eigenvectors if balancing was used */
    177:   if ((pep->scale==PEP_SCALE_DIAGONAL || pep->scale==PEP_SCALE_BOTH) && pep->Dr && (pep->refine!=PEP_REFINE_MULTIPLE)) {
    178:     for (i=0;i<pep->nconv;i++) {
    179:       PetscCall(BVGetColumn(pep->V,i,&v));
    180:       PetscCall(VecPointwiseMult(v,v,pep->Dr));
    181:       PetscCall(BVRestoreColumn(pep->V,i,&v));
    182:     }
    183:   }

    185:   /* normalization */
    186:   PetscCall(BVNormalize(pep->V,pep->eigi));
    187:   PetscFunctionReturn(PETSC_SUCCESS);
    188: }

    190: /*
    191:   PEPBuildDiagonalScaling - compute two diagonal matrices to be applied for balancing
    192:   in polynomial eigenproblems.
    193: */
    194: PetscErrorCode PEPBuildDiagonalScaling(PEP pep)
    195: {
    196:   PetscInt       it,i,j,k,nmat,nr,e,nz,lst,lend,nc=0,*cols,emax,emin,emaxl,eminl;
    197:   const PetscInt *cidx,*ridx;
    198:   Mat            M,*T,A;
    199:   PetscMPIInt    n;
    200:   PetscBool      cont=PETSC_TRUE,flg=PETSC_FALSE;
    201:   PetscScalar    *array,*Dr,*Dl,t;
    202:   PetscReal      l2,d,*rsum,*aux,*csum,w=1.0;
    203:   MatStructure   str;
    204:   MatInfo        info;

    206:   PetscFunctionBegin;
    207:   l2 = 2*PetscLogReal(2.0);
    208:   nmat = pep->nmat;
    209:   PetscCall(PetscMPIIntCast(pep->n,&n));
    210:   PetscCall(STGetMatStructure(pep->st,&str));
    211:   PetscCall(PetscMalloc1(nmat,&T));
    212:   for (k=0;k<nmat;k++) PetscCall(STGetMatrixTransformed(pep->st,k,&T[k]));
    213:   /* Form local auxiliary matrix M */
    214:   PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)T[0],&cont,MATMPIAIJ,MATSEQAIJ,""));
    215:   PetscCheck(cont,PetscObjectComm((PetscObject)T[0]),PETSC_ERR_SUP,"Only for MPIAIJ or SEQAIJ matrix types");
    216:   PetscCall(PetscObjectBaseTypeCompare((PetscObject)T[0],MATMPIAIJ,&cont));
    217:   if (cont) {
    218:     PetscCall(MatMPIAIJGetLocalMat(T[0],MAT_INITIAL_MATRIX,&M));
    219:     flg = PETSC_TRUE;
    220:   } else PetscCall(MatDuplicate(T[0],MAT_COPY_VALUES,&M));
    221:   PetscCall(MatGetInfo(M,MAT_LOCAL,&info));
    222:   nz = (PetscInt)info.nz_used;
    223:   PetscCall(MatSeqAIJGetArray(M,&array));
    224:   for (i=0;i<nz;i++) {
    225:     t = PetscAbsScalar(array[i]);
    226:     array[i] = t*t;
    227:   }
    228:   PetscCall(MatSeqAIJRestoreArray(M,&array));
    229:   for (k=1;k<nmat;k++) {
    230:     if (flg) PetscCall(MatMPIAIJGetLocalMat(T[k],MAT_INITIAL_MATRIX,&A));
    231:     else {
    232:       if (str==SAME_NONZERO_PATTERN) PetscCall(MatCopy(T[k],A,SAME_NONZERO_PATTERN));
    233:       else PetscCall(MatDuplicate(T[k],MAT_COPY_VALUES,&A));
    234:     }
    235:     PetscCall(MatGetInfo(A,MAT_LOCAL,&info));
    236:     nz = (PetscInt)info.nz_used;
    237:     PetscCall(MatSeqAIJGetArray(A,&array));
    238:     for (i=0;i<nz;i++) {
    239:       t = PetscAbsScalar(array[i]);
    240:       array[i] = t*t;
    241:     }
    242:     PetscCall(MatSeqAIJRestoreArray(A,&array));
    243:     w *= pep->slambda*pep->slambda*pep->sfactor;
    244:     PetscCall(MatAXPY(M,w,A,str));
    245:     if (flg || str!=SAME_NONZERO_PATTERN || k==nmat-2) PetscCall(MatDestroy(&A));
    246:   }
    247:   PetscCall(MatGetRowIJ(M,0,PETSC_FALSE,PETSC_FALSE,&nr,&ridx,&cidx,&cont));
    248:   PetscCheck(cont,PetscObjectComm((PetscObject)T[0]),PETSC_ERR_SUP,"It is not possible to compute scaling diagonals for these PEP matrices");
    249:   PetscCall(MatGetInfo(M,MAT_LOCAL,&info));
    250:   nz = (PetscInt)info.nz_used;
    251:   PetscCall(VecGetOwnershipRange(pep->Dl,&lst,&lend));
    252:   PetscCall(PetscMalloc4(nr,&rsum,pep->n,&csum,pep->n,&aux,PetscMin(pep->n-lend+lst,nz),&cols));
    253:   PetscCall(VecSet(pep->Dr,1.0));
    254:   PetscCall(VecSet(pep->Dl,1.0));
    255:   PetscCall(VecGetArray(pep->Dl,&Dl));
    256:   PetscCall(VecGetArray(pep->Dr,&Dr));
    257:   PetscCall(MatSeqAIJGetArray(M,&array));
    258:   PetscCall(PetscArrayzero(aux,pep->n));
    259:   for (j=0;j<nz;j++) {
    260:     /* Search non-zero columns outsize lst-lend */
    261:     if (aux[cidx[j]]==0 && (cidx[j]<lst || lend<=cidx[j])) cols[nc++] = cidx[j];
    262:     /* Local column sums */
    263:     aux[cidx[j]] += PetscAbsScalar(array[j]);
    264:   }
    265:   for (it=0;it<pep->sits && cont;it++) {
    266:     emaxl = 0; eminl = 0;
    267:     /* Column sum  */
    268:     if (it>0) { /* it=0 has been already done*/
    269:       PetscCall(MatSeqAIJGetArray(M,&array));
    270:       PetscCall(PetscArrayzero(aux,pep->n));
    271:       for (j=0;j<nz;j++) aux[cidx[j]] += PetscAbsScalar(array[j]);
    272:       PetscCall(MatSeqAIJRestoreArray(M,&array));
    273:     }
    274:     PetscCall(MPIU_Allreduce(aux,csum,n,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)pep->Dr)));
    275:     /* Update Dr */
    276:     for (j=lst;j<lend;j++) {
    277:       d = PetscLogReal(csum[j])/l2;
    278:       e = -(PetscInt)((d < 0)?(d-0.5):(d+0.5));
    279:       d = PetscPowReal(2.0,e);
    280:       Dr[j-lst] *= d;
    281:       aux[j] = d*d;
    282:       emaxl = PetscMax(emaxl,e);
    283:       eminl = PetscMin(eminl,e);
    284:     }
    285:     for (j=0;j<nc;j++) {
    286:       d = PetscLogReal(csum[cols[j]])/l2;
    287:       e = -(PetscInt)((d < 0)?(d-0.5):(d+0.5));
    288:       d = PetscPowReal(2.0,e);
    289:       aux[cols[j]] = d*d;
    290:       emaxl = PetscMax(emaxl,e);
    291:       eminl = PetscMin(eminl,e);
    292:     }
    293:     /* Scale M */
    294:     PetscCall(MatSeqAIJGetArray(M,&array));
    295:     for (j=0;j<nz;j++) {
    296:       array[j] *= aux[cidx[j]];
    297:     }
    298:     PetscCall(MatSeqAIJRestoreArray(M,&array));
    299:     /* Row sum */
    300:     PetscCall(PetscArrayzero(rsum,nr));
    301:     PetscCall(MatSeqAIJGetArray(M,&array));
    302:     for (i=0;i<nr;i++) {
    303:       for (j=ridx[i];j<ridx[i+1];j++) rsum[i] += PetscAbsScalar(array[j]);
    304:       /* Update Dl */
    305:       d = PetscLogReal(rsum[i])/l2;
    306:       e = -(PetscInt)((d < 0)?(d-0.5):(d+0.5));
    307:       d = PetscPowReal(2.0,e);
    308:       Dl[i] *= d;
    309:       /* Scale M */
    310:       for (j=ridx[i];j<ridx[i+1];j++) array[j] *= d*d;
    311:       emaxl = PetscMax(emaxl,e);
    312:       eminl = PetscMin(eminl,e);
    313:     }
    314:     PetscCall(MatSeqAIJRestoreArray(M,&array));
    315:     /* Compute global max and min */
    316:     PetscCall(MPIU_Allreduce(&emaxl,&emax,1,MPIU_INT,MPI_MAX,PetscObjectComm((PetscObject)pep->Dl)));
    317:     PetscCall(MPIU_Allreduce(&eminl,&emin,1,MPIU_INT,MPI_MIN,PetscObjectComm((PetscObject)pep->Dl)));
    318:     if (emax<=emin+2) cont = PETSC_FALSE;
    319:   }
    320:   PetscCall(VecRestoreArray(pep->Dr,&Dr));
    321:   PetscCall(VecRestoreArray(pep->Dl,&Dl));
    322:   /* Free memory*/
    323:   PetscCall(MatDestroy(&M));
    324:   PetscCall(PetscFree4(rsum,csum,aux,cols));
    325:   PetscCall(PetscFree(T));
    326:   PetscFunctionReturn(PETSC_SUCCESS);
    327: }

    329: /*
    330:    PEPComputeScaleFactor - compute sfactor as described in [Betcke 2008].
    331: */
    332: PetscErrorCode PEPComputeScaleFactor(PEP pep)
    333: {
    334:   PetscBool      has0,has1,flg;
    335:   PetscReal      norm0,norm1;
    336:   Mat            T[2];
    337:   PEPBasis       basis;
    338:   PetscInt       i;

    340:   PetscFunctionBegin;
    341:   if (pep->scale==PEP_SCALE_NONE || pep->scale==PEP_SCALE_DIAGONAL) {  /* no scalar scaling */
    342:     pep->sfactor = 1.0;
    343:     pep->dsfactor = 1.0;
    344:     PetscFunctionReturn(PETSC_SUCCESS);
    345:   }
    346:   if (pep->sfactor_set) PetscFunctionReturn(PETSC_SUCCESS);  /* user provided value */
    347:   pep->sfactor = 1.0;
    348:   pep->dsfactor = 1.0;
    349:   PetscCall(PEPGetBasis(pep,&basis));
    350:   if (basis==PEP_BASIS_MONOMIAL) {
    351:     PetscCall(STGetTransform(pep->st,&flg));
    352:     if (flg) {
    353:       PetscCall(STGetMatrixTransformed(pep->st,0,&T[0]));
    354:       PetscCall(STGetMatrixTransformed(pep->st,pep->nmat-1,&T[1]));
    355:     } else {
    356:       T[0] = pep->A[0];
    357:       T[1] = pep->A[pep->nmat-1];
    358:     }
    359:     if (pep->nmat>2) {
    360:       PetscCall(MatHasOperation(T[0],MATOP_NORM,&has0));
    361:       PetscCall(MatHasOperation(T[1],MATOP_NORM,&has1));
    362:       if (has0 && has1) {
    363:         PetscCall(MatNorm(T[0],NORM_INFINITY,&norm0));
    364:         PetscCall(MatNorm(T[1],NORM_INFINITY,&norm1));
    365:         pep->sfactor = PetscPowReal(norm0/norm1,1.0/(pep->nmat-1));
    366:         pep->dsfactor = norm1;
    367:         for (i=pep->nmat-2;i>0;i--) {
    368:           PetscCall(STGetMatrixTransformed(pep->st,i,&T[1]));
    369:           PetscCall(MatHasOperation(T[1],MATOP_NORM,&has1));
    370:           if (has1) {
    371:             PetscCall(MatNorm(T[1],NORM_INFINITY,&norm1));
    372:             pep->dsfactor = pep->dsfactor*pep->sfactor+norm1;
    373:           } else break;
    374:         }
    375:         if (has1) {
    376:           pep->dsfactor = pep->dsfactor*pep->sfactor+norm0;
    377:           pep->dsfactor = pep->nmat/pep->dsfactor;
    378:         } else pep->dsfactor = 1.0;
    379:       }
    380:     }
    381:   }
    382:   PetscFunctionReturn(PETSC_SUCCESS);
    383: }

    385: /*
    386:    PEPBasisCoefficients - compute polynomial basis coefficients
    387: */
    388: PetscErrorCode PEPBasisCoefficients(PEP pep,PetscReal *pbc)
    389: {
    390:   PetscReal *ca,*cb,*cg;
    391:   PetscInt  k,nmat=pep->nmat;

    393:   PetscFunctionBegin;
    394:   ca = pbc;
    395:   cb = pbc+nmat;
    396:   cg = pbc+2*nmat;
    397:   switch (pep->basis) {
    398:   case PEP_BASIS_MONOMIAL:
    399:     for (k=0;k<nmat;k++) {
    400:       ca[k] = 1.0; cb[k] = 0.0; cg[k] = 0.0;
    401:     }
    402:     break;
    403:   case PEP_BASIS_CHEBYSHEV1:
    404:     ca[0] = 1.0; cb[0] = 0.0; cg[0] = 0.0;
    405:     for (k=1;k<nmat;k++) {
    406:       ca[k] = .5; cb[k] = 0.0; cg[k] = .5;
    407:     }
    408:     break;
    409:   case PEP_BASIS_CHEBYSHEV2:
    410:     ca[0] = .5; cb[0] = 0.0; cg[0] = 0.0;
    411:     for (k=1;k<nmat;k++) {
    412:       ca[k] = .5; cb[k] = 0.0; cg[k] = .5;
    413:     }
    414:     break;
    415:   case PEP_BASIS_LEGENDRE:
    416:     ca[0] = 1.0; cb[0] = 0.0; cg[0] = 0.0;
    417:     for (k=1;k<nmat;k++) {
    418:       ca[k] = k+1; cb[k] = -2*k; cg[k] = k;
    419:     }
    420:     break;
    421:   case PEP_BASIS_LAGUERRE:
    422:     ca[0] = -1.0; cb[0] = 0.0; cg[0] = 0.0;
    423:     for (k=1;k<nmat;k++) {
    424:       ca[k] = -(k+1); cb[k] = 2*k+1; cg[k] = -k;
    425:     }
    426:     break;
    427:   case PEP_BASIS_HERMITE:
    428:     ca[0] = .5; cb[0] = 0.0; cg[0] = 0.0;
    429:     for (k=1;k<nmat;k++) {
    430:       ca[k] = .5; cb[k] = 0.0; cg[k] = -k;
    431:     }
    432:     break;
    433:   }
    434:   PetscFunctionReturn(PETSC_SUCCESS);
    435: }