Date: 2021-02-01T14:42:11+00:00

Actual source code: pepkrylov.c
slepc-3.14.2 2021-02-01
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      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:    Common subroutines for all Krylov-type PEP solvers
     12: */

     14: #include <slepc/private/pepimpl.h>
     15: #include <slepcblaslapack.h>
     16: #include "pepkrylov.h"

     18: PetscErrorCode PEPExtractVectors_TOAR(PEP pep)
     19: {
     21:   PetscInt       i,j,nq,deg=pep->nmat-1,lds,idxcpy=0,ldds,k,ld;
     22:   PetscScalar    *X,*er,*ei,*SS,*vals,*ivals,sone=1.0,szero=0.0,*yi,*yr,*tr,*ti,alpha,t,*S,*pS0;
     23:   PetscBLASInt   k_,nq_,lds_,one=1,ldds_;
     24:   PetscBool      flg;
     25:   PetscReal      norm,max,factor=1.0;
     26:   Vec            xr,xi,w[4];
     27:   PEP_TOAR       *ctx = (PEP_TOAR*)pep->data;
     28:   Mat            S0,MS;

     31:   BVTensorGetFactors(ctx->V,NULL,&MS);
     32:   MatDenseGetArray(MS,&S);
     33:   BVGetSizes(pep->V,NULL,NULL,&ld);
     34:   BVGetActiveColumns(pep->V,NULL,&nq);
     35:   k = pep->nconv;
     36:   if (k==0) return(0);
     37:   lds = deg*ld;
     38:   DSGetLeadingDimension(pep->ds,&ldds);
     39:   PetscCalloc5(k,&er,k,&ei,nq*k,&SS,pep->nmat,&vals,pep->nmat,&ivals);
     40:   STGetTransform(pep->st,&flg);
     41:   if (flg) factor = pep->sfactor;
     42:   for (i=0;i<k;i++) {
     43:     er[i] = factor*pep->eigr[i];
     44:     ei[i] = factor*pep->eigi[i];
     45:   }
     46:   STBackTransform(pep->st,k,er,ei);

     48:   DSVectors(pep->ds,DS_MAT_X,NULL,NULL);
     49:   DSGetArray(pep->ds,DS_MAT_X,&X);

     51:   PetscBLASIntCast(k,&k_);
     52:   PetscBLASIntCast(nq,&nq_);
     53:   PetscBLASIntCast(lds,&lds_);
     54:   PetscBLASIntCast(ldds,&ldds_);

     56:   if (pep->extract==PEP_EXTRACT_NONE || pep->refine==PEP_REFINE_MULTIPLE) {
     57:     PetscStackCallBLAS("BLASgemm",BLASgemm_("N","N",&nq_,&k_,&k_,&sone,S,&lds_,X,&ldds_,&szero,SS,&nq_));
     58:   } else {
     59:     switch (pep->extract) {
     60:     case PEP_EXTRACT_NONE:
     61:       break;
     62:     case PEP_EXTRACT_NORM:
     63:       for (i=0;i<k;i++) {
     64:         PEPEvaluateBasis(pep,er[i],ei[i],vals,ivals);
     65:         max = 1.0;
     66:         for (j=1;j<deg;j++) {
     67:           norm = SlepcAbsEigenvalue(vals[j],ivals[j]);
     68:           if (max < norm) { max = norm; idxcpy = j; }
     69:         }
     70:         PetscStackCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&sone,S+idxcpy*ld,&lds_,X+i*ldds,&one,&szero,SS+i*nq,&one));
     71: #if !defined(PETSC_USE_COMPLEX)
     72:         if (PetscRealPart(ei[i])!=0.0) {
     73:           i++;
     74:           PetscStackCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&sone,S+idxcpy*ld,&lds_,X+i*ldds,&one,&szero,SS+i*nq,&one));
     75:         }
     76: #endif
     77:       }
     78:       break;
     79:     case PEP_EXTRACT_RESIDUAL:
     80:       VecDuplicate(pep->work[0],&xr);
     81:       VecDuplicate(pep->work[0],&w[0]);
     82:       VecDuplicate(pep->work[0],&w[1]);
     83: #if !defined(PETSC_USE_COMPLEX)
     84:       VecDuplicate(pep->work[0],&w[2]);
     85:       VecDuplicate(pep->work[0],&w[3]);
     86:       VecDuplicate(pep->work[0],&xi);
     87: #else
     88:       xi = NULL;
     89: #endif
     90:       for (i=0;i<k;i++) {
     91:         max = 0.0;
     92:         for (j=0;j<deg;j++) {
     93:           PetscStackCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&sone,S+j*ld,&lds_,X+i*ldds,&one,&szero,SS+i*nq,&one));
     94:           BVMultVec(pep->V,1.0,0.0,xr,SS+i*nq);
     95: #if !defined(PETSC_USE_COMPLEX)
     96:           PetscStackCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&sone,S+j*ld,&lds_,X+(i+1)*ldds,&one,&szero,SS+i*nq,&one));
     97:           BVMultVec(pep->V,1.0,0.0,xi,SS+i*nq);
     98: #endif
     99:           PEPComputeResidualNorm_Private(pep,er[i],ei[i],xr,xi,w,&norm);
    100:           if (norm>max) { max = norm; idxcpy=j; }
    101:         }
    102:         PetscStackCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&sone,S+idxcpy*ld,&lds_,X+i*ldds,&one,&szero,SS+i*nq,&one));
    103: #if !defined(PETSC_USE_COMPLEX)
    104:         if (PetscRealPart(ei[i])!=0.0) {
    105:           i++;
    106:           PetscStackCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&sone,S+idxcpy*ld,&lds_,X+i*ldds,&one,&szero,SS+i*nq,&one));
    107:         }
    108: #endif
    109:       }
    110:       VecDestroy(&xr);
    111:       VecDestroy(&w[0]);
    112:       VecDestroy(&w[1]);
    113: #if !defined(PETSC_USE_COMPLEX)
    114:       VecDestroy(&w[2]);
    115:       VecDestroy(&w[3]);
    116:       VecDestroy(&xi);
    117: #endif
    118:       break;
    119:     case PEP_EXTRACT_STRUCTURED:
    120:       PetscMalloc2(k,&tr,k,&ti);
    121:       for (i=0;i<k;i++) {
    122:         t = 0.0;
    123:         PEPEvaluateBasis(pep,er[i],ei[i],vals,ivals);
    124:         yr = X+i*ldds; yi = NULL;
    125: #if !defined(PETSC_USE_COMPLEX)
    126:         if (ei[i]!=0.0) { yr = tr; yi = ti; }
    127: #endif
    128:         for (j=0;j<deg;j++) {
    129:           alpha = PetscConj(vals[j]);
    130: #if !defined(PETSC_USE_COMPLEX)
    131:           if (ei[i]!=0.0) {
    132:             PetscArrayzero(tr,k);
    133:             PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&k_,&vals[j],X+i*ldds,&one,tr,&one));
    134:             PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&k_,&ivals[j],X+(i+1)*ldds,&one,tr,&one));
    135:             PetscArrayzero(ti,k);
    136:             PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&k_,&vals[j],X+(i+1)*ldds,&one,ti,&one));
    137:             alpha = -ivals[j];
    138:             PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&k_,&alpha,X+i*ldds,&one,ti,&one));
    139:             alpha = 1.0;
    140:           }
    141: #endif
    142:           PetscStackCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&alpha,S+j*ld,&lds_,yr,&one,&sone,SS+i*nq,&one));
    143:           t += SlepcAbsEigenvalue(vals[j],ivals[j])*SlepcAbsEigenvalue(vals[j],ivals[j]);
    144:           if (yi) {
    145:             PetscStackCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&alpha,S+j*ld,&lds_,yi,&one,&sone,SS+(i+1)*nq,&one));
    146:           }
    147:         }
    148:         t = 1.0/t;
    149:         PetscStackCallBLAS("BLASscal",BLASscal_(&nq_,&t,SS+i*nq,&one));
    150:         if (yi) {
    151:           PetscStackCallBLAS("BLASscal",BLASscal_(&nq_,&t,SS+(i+1)*nq,&one));
    152:           i++;
    153:         }
    154:       }
    155:       PetscFree2(tr,ti);
    156:       break;
    157:     }
    158:   }

    160:   /* update vectors V = V*S */
    161:   MatCreateSeqDense(PETSC_COMM_SELF,nq,k,NULL,&S0);
    162:   MatDenseGetArray(S0,&pS0);
    163:   for (i=0;i<k;i++) {
    164:     PetscArraycpy(pS0+i*nq,SS+i*nq,nq);
    165:   }
    166:   MatDenseRestoreArray(S0,&pS0);
    167:   BVMultInPlace(pep->V,S0,0,k);
    168:   MatDestroy(&S0);
    169:   PetscFree5(er,ei,SS,vals,ivals);
    170:   if (ctx->V) {
    171:     MatDenseRestoreArray(MS,&S);
    172:     BVTensorRestoreFactors(ctx->V,NULL,&MS);
    173:   }
    174:   return(0);
    175: }

    177: /*
    178:    PEPKrylovConvergence - This is the analogue to EPSKrylovConvergence, but
    179:    for polynomial Krylov methods.

    181:    Differences:
    182:    - Always non-symmetric
    183:    - Does not check for STSHIFT
    184:    - No correction factor
    185:    - No support for true residual
    186: */
    187: PetscErrorCode PEPKrylovConvergence(PEP pep,PetscBool getall,PetscInt kini,PetscInt nits,PetscReal beta,PetscInt *kout)
    188: {
    190:   PetscInt       k,newk,marker,inside;
    191:   PetscScalar    re,im;
    192:   PetscReal      resnorm;
    193:   PetscBool      istrivial;

    196:   RGIsTrivial(pep->rg,&istrivial);
    197:   marker = -1;
    198:   if (pep->trackall) getall = PETSC_TRUE;
    199:   for (k=kini;k<kini+nits;k++) {
    200:     /* eigenvalue */
    201:     re = pep->eigr[k];
    202:     im = pep->eigi[k];
    203:     if (!istrivial) {
    204:       STBackTransform(pep->st,1,&re,&im);
    205:       RGCheckInside(pep->rg,1,&re,&im,&inside);
    206:       if (marker==-1 && inside<0) marker = k;
    207:       re = pep->eigr[k];
    208:       im = pep->eigi[k];
    209:     }
    210:     newk = k;
    211:     DSVectors(pep->ds,DS_MAT_X,&newk,&resnorm);
    212:     resnorm *= beta;
    213:     /* error estimate */
    214:     (*pep->converged)(pep,re,im,resnorm,&pep->errest[k],pep->convergedctx);
    215:     if (marker==-1 && pep->errest[k] >= pep->tol) marker = k;
    216:     if (newk==k+1) {
    217:       pep->errest[k+1] = pep->errest[k];
    218:       k++;
    219:     }
    220:     if (marker!=-1 && !getall) break;
    221:   }
    222:   if (marker!=-1) k = marker;
    223:   *kout = k;
    224:   return(0);
    225: }