Date: 2024-04-26T10:22:29+00:00

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

     30:   PetscFunctionBegin;
     31:   PetscCall(BVTensorGetFactors(ctx->V,NULL,&MS));
     32:   PetscCall(MatDenseGetArrayRead(MS,&S));
     33:   PetscCall(BVGetSizes(pep->V,NULL,NULL,&ld));
     34:   PetscCall(BVGetActiveColumns(pep->V,NULL,&nq));
     35:   k = pep->nconv;
     36:   if (k==0) PetscFunctionReturn(PETSC_SUCCESS);
     37:   lds = deg*ld;
     38:   PetscCall(DSGetLeadingDimension(pep->ds,&ldds));
     39:   PetscCall(PetscCalloc5(k,&er,k,&ei,nq*k,&SS,pep->nmat,&vals,pep->nmat,&ivals));
     40:   PetscCall(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:   PetscCall(STBackTransform(pep->st,k,er,ei));

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

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

     56:   if (pep->extract==PEP_EXTRACT_NONE || pep->refine==PEP_REFINE_MULTIPLE) {
     57:     PetscCallBLAS("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:         PetscCall(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:         PetscCallBLAS("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:           PetscCallBLAS("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:       PetscCall(VecDuplicate(pep->work[0],&xr));
     81:       PetscCall(VecDuplicate(pep->work[0],&w[0]));
     82:       PetscCall(VecDuplicate(pep->work[0],&w[1]));
     83: #if !defined(PETSC_USE_COMPLEX)
     84:       PetscCall(VecDuplicate(pep->work[0],&w[2]));
     85:       PetscCall(VecDuplicate(pep->work[0],&w[3]));
     86:       PetscCall(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:           PetscCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&sone,S+j*ld,&lds_,X+i*ldds,&one,&szero,SS+i*nq,&one));
     94:           PetscCall(BVMultVec(pep->V,1.0,0.0,xr,SS+i*nq));
     95: #if !defined(PETSC_USE_COMPLEX)
     96:           PetscCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&sone,S+j*ld,&lds_,X+(i+1)*ldds,&one,&szero,SS+i*nq,&one));
     97:           PetscCall(BVMultVec(pep->V,1.0,0.0,xi,SS+i*nq));
     98: #endif
     99:           PetscCall(PEPComputeResidualNorm_Private(pep,er[i],ei[i],xr,xi,w,&norm));
    100:           if (norm>max) { max = norm; idxcpy=j; }
    101:         }
    102:         PetscCallBLAS("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:           PetscCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&sone,S+idxcpy*ld,&lds_,X+i*ldds,&one,&szero,SS+i*nq,&one));
    107:         }
    108: #endif
    109:       }
    110:       PetscCall(VecDestroy(&xr));
    111:       PetscCall(VecDestroy(&w[0]));
    112:       PetscCall(VecDestroy(&w[1]));
    113: #if !defined(PETSC_USE_COMPLEX)
    114:       PetscCall(VecDestroy(&w[2]));
    115:       PetscCall(VecDestroy(&w[3]));
    116:       PetscCall(VecDestroy(&xi));
    117: #endif
    118:       break;
    119:     case PEP_EXTRACT_STRUCTURED:
    120:       PetscCall(PetscMalloc2(k,&tr,k,&ti));
    121:       for (i=0;i<k;i++) {
    122:         t = 0.0;
    123:         PetscCall(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:             PetscCall(PetscArrayzero(tr,k));
    133:             PetscCallBLAS("BLASaxpy",BLASaxpy_(&k_,&vals[j],X+i*ldds,&one,tr,&one));
    134:             PetscCallBLAS("BLASaxpy",BLASaxpy_(&k_,&ivals[j],X+(i+1)*ldds,&one,tr,&one));
    135:             PetscCall(PetscArrayzero(ti,k));
    136:             PetscCallBLAS("BLASaxpy",BLASaxpy_(&k_,&vals[j],X+(i+1)*ldds,&one,ti,&one));
    137:             alpha = -ivals[j];
    138:             PetscCallBLAS("BLASaxpy",BLASaxpy_(&k_,&alpha,X+i*ldds,&one,ti,&one));
    139:             alpha = 1.0;
    140:           }
    141: #endif
    142:           PetscCallBLAS("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:             PetscCallBLAS("BLASgemv",BLASgemv_("N",&nq_,&k_,&alpha,S+j*ld,&lds_,yi,&one,&sone,SS+(i+1)*nq,&one));
    146:           }
    147:         }
    148:         cols = yi? 2: 1;
    149:         PetscCallBLAS("LAPACKlascl",LAPACKlascl_("G",&zero,&zero,&t,&done,&nq_,&cols,SS+i*nq,&nq_,&info));
    150:         SlepcCheckLapackInfo("lascl",info);
    151:         if (yi) i++;
    152:       }
    153:       PetscCall(PetscFree2(tr,ti));
    154:       break;
    155:     }
    156:   }

    158:   /* update vectors V = V*S */
    159:   PetscCall(MatCreateSeqDense(PETSC_COMM_SELF,nq,k,NULL,&S0));
    160:   PetscCall(MatDenseGetArrayWrite(S0,&pS0));
    161:   for (i=0;i<k;i++) PetscCall(PetscArraycpy(pS0+i*nq,SS+i*nq,nq));
    162:   PetscCall(MatDenseRestoreArrayWrite(S0,&pS0));
    163:   PetscCall(BVMultInPlace(pep->V,S0,0,k));
    164:   PetscCall(MatDestroy(&S0));
    165:   PetscCall(PetscFree5(er,ei,SS,vals,ivals));
    166:   PetscCall(MatDenseRestoreArrayRead(MS,&S));
    167:   PetscCall(BVTensorRestoreFactors(ctx->V,NULL,&MS));
    168:   PetscFunctionReturn(PETSC_SUCCESS);
    169: }

    171: /*
    172:    PEPKrylovConvergence - This is the analogue to EPSKrylovConvergence, but
    173:    for polynomial Krylov methods.

    175:    Differences:
    176:    - Always non-symmetric
    177:    - Does not check for STSHIFT
    178:    - No correction factor
    179:    - No support for true residual
    180: */
    181: PetscErrorCode PEPKrylovConvergence(PEP pep,PetscBool getall,PetscInt kini,PetscInt nits,PetscReal beta,PetscInt *kout)
    182: {
    183:   PetscInt       k,newk,marker,inside;
    184:   PetscScalar    re,im;
    185:   PetscReal      resnorm;
    186:   PetscBool      istrivial;

    188:   PetscFunctionBegin;
    189:   PetscCall(RGIsTrivial(pep->rg,&istrivial));
    190:   marker = -1;
    191:   if (pep->trackall) getall = PETSC_TRUE;
    192:   for (k=kini;k<kini+nits;k++) {
    193:     /* eigenvalue */
    194:     re = pep->eigr[k];
    195:     im = pep->eigi[k];
    196:     if (PetscUnlikely(!istrivial)) {
    197:       PetscCall(STBackTransform(pep->st,1,&re,&im));
    198:       PetscCall(RGCheckInside(pep->rg,1,&re,&im,&inside));
    199:       if (marker==-1 && inside<0) marker = k;
    200:       re = pep->eigr[k];
    201:       im = pep->eigi[k];
    202:     }
    203:     newk = k;
    204:     PetscCall(DSVectors(pep->ds,DS_MAT_X,&newk,&resnorm));
    205:     resnorm *= beta;
    206:     /* error estimate */
    207:     PetscCall((*pep->converged)(pep,re,im,resnorm,&pep->errest[k],pep->convergedctx));
    208:     if (marker==-1 && pep->errest[k] >= pep->tol) marker = k;
    209:     if (PetscUnlikely(newk==k+1)) {
    210:       pep->errest[k+1] = pep->errest[k];
    211:       k++;
    212:     }
    213:     if (marker!=-1 && !getall) break;
    214:   }
    215:   if (marker!=-1) k = marker;
    216:   *kout = k;
    217:   PetscFunctionReturn(PETSC_SUCCESS);
    218: }