Date: 2023-01-26T12:19:03+00:00

Actual source code: gklanczos.c
slepc-3.18.2 2023-01-26
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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:    SLEPc singular value solver: "lanczos"

     13:    Method: Explicitly restarted Lanczos

     15:    Algorithm:

     17:        Golub-Kahan-Lanczos bidiagonalization with explicit restart.

     19:    References:

     21:        [1] G.H. Golub and W. Kahan, "Calculating the singular values
     22:            and pseudo-inverse of a matrix", SIAM J. Numer. Anal. Ser.
     23:            B 2:205-224, 1965.

     25:        [2] V. Hernandez, J.E. Roman, and A. Tomas, "A robust and
     26:            efficient parallel SVD solver based on restarted Lanczos
     27:            bidiagonalization", Elec. Trans. Numer. Anal. 31:68-85,
     28:            2008.
     29: */

     31: #include <slepc/private/svdimpl.h>

     33: typedef struct {
     34:   PetscBool oneside;
     35: } SVD_LANCZOS;

     37: PetscErrorCode SVDSetUp_Lanczos(SVD svd)
     38: {
     39:   SVD_LANCZOS    *lanczos = (SVD_LANCZOS*)svd->data;
     40:   PetscInt       N;

     42:   SVDCheckStandard(svd);
     43:   SVDCheckDefinite(svd);
     44:   MatGetSize(svd->A,NULL,&N);
     45:   SVDSetDimensions_Default(svd);
     47:   if (svd->max_it==PETSC_DEFAULT) svd->max_it = PetscMax(N/svd->ncv,100);
     48:   svd->leftbasis = PetscNot(lanczos->oneside);
     49:   SVDAllocateSolution(svd,1);
     50:   DSSetType(svd->ds,DSSVD);
     51:   DSSetCompact(svd->ds,PETSC_TRUE);
     52:   DSSetExtraRow(svd->ds,PETSC_TRUE);
     53:   DSAllocate(svd->ds,svd->ncv+1);
     54:   return 0;
     55: }

     57: PetscErrorCode SVDTwoSideLanczos(SVD svd,PetscReal *alpha,PetscReal *beta,BV V,BV U,PetscInt k,PetscInt *n,PetscBool *breakdown)
     58: {
     59:   PetscInt       i;
     60:   Vec            u,v;
     61:   PetscBool      lindep=PETSC_FALSE;

     63:   BVGetColumn(svd->V,k,&v);
     64:   BVGetColumn(svd->U,k,&u);
     65:   MatMult(svd->A,v,u);
     66:   BVRestoreColumn(svd->V,k,&v);
     67:   BVRestoreColumn(svd->U,k,&u);
     68:   BVOrthonormalizeColumn(svd->U,k,PETSC_FALSE,alpha+k,&lindep);
     69:   if (PetscUnlikely(lindep)) {
     70:     *n = k;
     71:     if (breakdown) *breakdown = lindep;
     72:     return 0;
     73:   }

     75:   for (i=k+1;i<*n;i++) {
     76:     BVGetColumn(svd->V,i,&v);
     77:     BVGetColumn(svd->U,i-1,&u);
     78:     MatMult(svd->AT,u,v);
     79:     BVRestoreColumn(svd->V,i,&v);
     80:     BVRestoreColumn(svd->U,i-1,&u);
     81:     BVOrthonormalizeColumn(svd->V,i,PETSC_FALSE,beta+i-1,&lindep);
     82:     if (PetscUnlikely(lindep)) {
     83:       *n = i;
     84:       break;
     85:     }
     86:     BVGetColumn(svd->V,i,&v);
     87:     BVGetColumn(svd->U,i,&u);
     88:     MatMult(svd->A,v,u);
     89:     BVRestoreColumn(svd->V,i,&v);
     90:     BVRestoreColumn(svd->U,i,&u);
     91:     BVOrthonormalizeColumn(svd->U,i,PETSC_FALSE,alpha+i,&lindep);
     92:     if (PetscUnlikely(lindep)) {
     93:       *n = i;
     94:       break;
     95:     }
     96:   }

     98:   if (!lindep) {
     99:     BVGetColumn(svd->V,*n,&v);
    100:     BVGetColumn(svd->U,*n-1,&u);
    101:     MatMult(svd->AT,u,v);
    102:     BVRestoreColumn(svd->V,*n,&v);
    103:     BVRestoreColumn(svd->U,*n-1,&u);
    104:     BVOrthogonalizeColumn(svd->V,*n,NULL,beta+*n-1,&lindep);
    105:   }
    106:   if (PetscUnlikely(breakdown)) *breakdown = lindep;
    107:   return 0;
    108: }

    110: static PetscErrorCode SVDOneSideLanczos(SVD svd,PetscReal *alpha,PetscReal *beta,BV V,Vec u,Vec u_1,PetscInt k,PetscInt n,PetscScalar* work)
    111: {
    112:   PetscInt       i,bvl,bvk;
    113:   PetscReal      a,b;
    114:   Vec            z,temp;

    116:   BVGetActiveColumns(V,&bvl,&bvk);
    117:   BVGetColumn(V,k,&z);
    118:   MatMult(svd->A,z,u);
    119:   BVRestoreColumn(V,k,&z);

    121:   for (i=k+1;i<n;i++) {
    122:     BVGetColumn(V,i,&z);
    123:     MatMult(svd->AT,u,z);
    124:     BVRestoreColumn(V,i,&z);
    125:     VecNormBegin(u,NORM_2,&a);
    126:     BVSetActiveColumns(V,0,i);
    127:     BVDotColumnBegin(V,i,work);
    128:     VecNormEnd(u,NORM_2,&a);
    129:     BVDotColumnEnd(V,i,work);
    130:     VecScale(u,1.0/a);
    131:     BVMultColumn(V,-1.0/a,1.0/a,i,work);

    133:     /* h = V^* z, z = z - V h  */
    134:     BVDotColumn(V,i,work);
    135:     BVMultColumn(V,-1.0,1.0,i,work);
    136:     BVNormColumn(V,i,NORM_2,&b);
    138:     BVScaleColumn(V,i,1.0/b);

    140:     BVGetColumn(V,i,&z);
    141:     MatMult(svd->A,z,u_1);
    142:     BVRestoreColumn(V,i,&z);
    143:     VecAXPY(u_1,-b,u);
    144:     alpha[i-1] = a;
    145:     beta[i-1] = b;
    146:     temp = u;
    147:     u = u_1;
    148:     u_1 = temp;
    149:   }

    151:   BVGetColumn(V,n,&z);
    152:   MatMult(svd->AT,u,z);
    153:   BVRestoreColumn(V,n,&z);
    154:   VecNormBegin(u,NORM_2,&a);
    155:   BVDotColumnBegin(V,n,work);
    156:   VecNormEnd(u,NORM_2,&a);
    157:   BVDotColumnEnd(V,n,work);
    158:   VecScale(u,1.0/a);
    159:   BVMultColumn(V,-1.0/a,1.0/a,n,work);

    161:   /* h = V^* z, z = z - V h  */
    162:   BVDotColumn(V,n,work);
    163:   BVMultColumn(V,-1.0,1.0,n,work);
    164:   BVNormColumn(V,i,NORM_2,&b);

    166:   alpha[n-1] = a;
    167:   beta[n-1] = b;
    168:   BVSetActiveColumns(V,bvl,bvk);
    169:   return 0;
    170: }

    172: /*
    173:    SVDKrylovConvergence - Implements the loop that checks for convergence
    174:    in Krylov methods.

    176:    Input Parameters:
    177:      svd     - the solver; some error estimates are updated in svd->errest
    178:      getall  - whether all residuals must be computed
    179:      kini    - initial value of k (the loop variable)
    180:      nits    - number of iterations of the loop
    181:      normr   - norm of triangular factor of qr([A;B]), used only in GSVD

    183:    Output Parameter:
    184:      kout  - the first index where the convergence test failed
    185: */
    186: PetscErrorCode SVDKrylovConvergence(SVD svd,PetscBool getall,PetscInt kini,PetscInt nits,PetscReal normr,PetscInt *kout)
    187: {
    188:   PetscInt       k,marker,ld;
    189:   PetscReal      *alpha,*beta,*betah,resnorm;
    190:   PetscBool      extra;

    192:   if (PetscUnlikely(svd->conv == SVD_CONV_MAXIT && svd->its >= svd->max_it)) *kout = svd->nsv;
    193:   else {
    194:     DSGetLeadingDimension(svd->ds,&ld);
    195:     DSGetExtraRow(svd->ds,&extra);
    197:     marker = -1;
    198:     if (svd->trackall) getall = PETSC_TRUE;
    199:     DSGetArrayReal(svd->ds,DS_MAT_T,&alpha);
    200:     beta = alpha + ld;
    201:     betah = alpha + 2*ld;
    202:     for (k=kini;k<kini+nits;k++) {
    203:       if (svd->isgeneralized) resnorm = SlepcAbs(beta[k],betah[k])*normr;
    204:       else resnorm = PetscAbsReal(beta[k]);
    205:       (*svd->converged)(svd,svd->sigma[k],resnorm,&svd->errest[k],svd->convergedctx);
    206:       if (marker==-1 && svd->errest[k] >= svd->tol) marker = k;
    207:       if (marker!=-1 && !getall) break;
    208:     }
    209:     DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha);
    210:     if (marker!=-1) k = marker;
    211:     *kout = k;
    212:   }
    213:   return 0;
    214: }

    216: PetscErrorCode SVDSolve_Lanczos(SVD svd)
    217: {
    218:   SVD_LANCZOS    *lanczos = (SVD_LANCZOS*)svd->data;
    219:   PetscReal      *alpha,*beta;
    220:   PetscScalar    *swork,*w,*P;
    221:   PetscInt       i,k,j,nv,ld;
    222:   Vec            u=NULL,u_1=NULL;
    223:   Mat            U,V;

    225:   /* allocate working space */
    226:   DSGetLeadingDimension(svd->ds,&ld);
    227:   PetscMalloc2(ld,&w,svd->ncv,&swork);

    229:   if (lanczos->oneside) {
    230:     MatCreateVecs(svd->A,NULL,&u);
    231:     MatCreateVecs(svd->A,NULL,&u_1);
    232:   }

    234:   /* normalize start vector */
    235:   if (!svd->nini) {
    236:     BVSetRandomColumn(svd->V,0);
    237:     BVOrthonormalizeColumn(svd->V,0,PETSC_TRUE,NULL,NULL);
    238:   }

    240:   while (svd->reason == SVD_CONVERGED_ITERATING) {
    241:     svd->its++;

    243:     /* inner loop */
    244:     nv = PetscMin(svd->nconv+svd->mpd,svd->ncv);
    245:     BVSetActiveColumns(svd->V,svd->nconv,nv);
    246:     DSGetArrayReal(svd->ds,DS_MAT_T,&alpha);
    247:     beta = alpha + ld;
    248:     if (lanczos->oneside) SVDOneSideLanczos(svd,alpha,beta,svd->V,u,u_1,svd->nconv,nv,swork);
    249:     else {
    250:       BVSetActiveColumns(svd->U,svd->nconv,nv);
    251:       SVDTwoSideLanczos(svd,alpha,beta,svd->V,svd->U,svd->nconv,&nv,NULL);
    252:     }
    253:     DSRestoreArrayReal(svd->ds,DS_MAT_T,&alpha);

    255:     /* compute SVD of bidiagonal matrix */
    256:     DSSetDimensions(svd->ds,nv,svd->nconv,0);
    257:     DSSVDSetDimensions(svd->ds,nv);
    258:     DSSetState(svd->ds,DS_STATE_INTERMEDIATE);
    259:     DSSolve(svd->ds,w,NULL);
    260:     DSSort(svd->ds,w,NULL,NULL,NULL,NULL);
    261:     DSUpdateExtraRow(svd->ds);
    262:     DSSynchronize(svd->ds,w,NULL);
    263:     for (i=svd->nconv;i<nv;i++) svd->sigma[i] = PetscRealPart(w[i]);

    265:     /* check convergence */
    266:     SVDKrylovConvergence(svd,PETSC_FALSE,svd->nconv,nv-svd->nconv,1.0,&k);
    267:     (*svd->stopping)(svd,svd->its,svd->max_it,k,svd->nsv,&svd->reason,svd->stoppingctx);

    269:     /* compute restart vector */
    270:     if (svd->reason == SVD_CONVERGED_ITERATING) {
    271:       if (k<nv) {
    272:         DSGetArray(svd->ds,DS_MAT_V,&P);
    273:         for (j=svd->nconv;j<nv;j++) swork[j-svd->nconv] = PetscConj(P[j+k*ld]);
    274:         DSRestoreArray(svd->ds,DS_MAT_V,&P);
    275:         BVMultColumn(svd->V,1.0,0.0,nv,swork);
    276:       } else {
    277:         /* all approximations have converged, generate a new initial vector */
    278:         BVSetRandomColumn(svd->V,nv);
    279:         BVOrthonormalizeColumn(svd->V,nv,PETSC_FALSE,NULL,NULL);
    280:       }
    281:     }

    283:     /* compute converged singular vectors */
    284:     DSGetMat(svd->ds,DS_MAT_V,&V);
    285:     BVMultInPlace(svd->V,V,svd->nconv,k);
    286:     DSRestoreMat(svd->ds,DS_MAT_V,&V);
    287:     if (!lanczos->oneside) {
    288:       DSGetMat(svd->ds,DS_MAT_U,&U);
    289:       BVMultInPlace(svd->U,U,svd->nconv,k);
    290:       DSRestoreMat(svd->ds,DS_MAT_U,&U);
    291:     }

    293:     /* copy restart vector from the last column */
    294:     if (svd->reason == SVD_CONVERGED_ITERATING) BVCopyColumn(svd->V,nv,k);

    296:     svd->nconv = k;
    297:     SVDMonitor(svd,svd->its,svd->nconv,svd->sigma,svd->errest,nv);
    298:   }

    300:   /* free working space */
    301:   VecDestroy(&u);
    302:   VecDestroy(&u_1);
    303:   PetscFree2(w,swork);
    304:   return 0;
    305: }

    307: PetscErrorCode SVDSetFromOptions_Lanczos(SVD svd,PetscOptionItems *PetscOptionsObject)
    308: {
    309:   PetscBool      set,val;
    310:   SVD_LANCZOS    *lanczos = (SVD_LANCZOS*)svd->data;

    312:   PetscOptionsHeadBegin(PetscOptionsObject,"SVD Lanczos Options");

    314:     PetscOptionsBool("-svd_lanczos_oneside","Use one-side reorthogonalization","SVDLanczosSetOneSide",lanczos->oneside,&val,&set);
    315:     if (set) SVDLanczosSetOneSide(svd,val);

    317:   PetscOptionsHeadEnd();
    318:   return 0;
    319: }

    321: static PetscErrorCode SVDLanczosSetOneSide_Lanczos(SVD svd,PetscBool oneside)
    322: {
    323:   SVD_LANCZOS *lanczos = (SVD_LANCZOS*)svd->data;

    325:   if (lanczos->oneside != oneside) {
    326:     lanczos->oneside = oneside;
    327:     svd->state = SVD_STATE_INITIAL;
    328:   }
    329:   return 0;
    330: }

    332: /*@
    333:    SVDLanczosSetOneSide - Indicate if the variant of the Lanczos method
    334:    to be used is one-sided or two-sided.

    336:    Logically Collective on svd

    338:    Input Parameters:
    339: +  svd     - singular value solver
    340: -  oneside - boolean flag indicating if the method is one-sided or not

    342:    Options Database Key:
    343: .  -svd_lanczos_oneside <boolean> - Indicates the boolean flag

    345:    Note:
    346:    By default, a two-sided variant is selected, which is sometimes slightly
    347:    more robust. However, the one-sided variant is faster because it avoids
    348:    the orthogonalization associated to left singular vectors. It also saves
    349:    the memory required for storing such vectors.

    351:    Level: advanced

    353: .seealso: SVDTRLanczosSetOneSide()
    354: @*/
    355: PetscErrorCode SVDLanczosSetOneSide(SVD svd,PetscBool oneside)
    356: {
    359:   PetscTryMethod(svd,"SVDLanczosSetOneSide_C",(SVD,PetscBool),(svd,oneside));
    360:   return 0;
    361: }

    363: static PetscErrorCode SVDLanczosGetOneSide_Lanczos(SVD svd,PetscBool *oneside)
    364: {
    365:   SVD_LANCZOS *lanczos = (SVD_LANCZOS*)svd->data;

    367:   *oneside = lanczos->oneside;
    368:   return 0;
    369: }

    371: /*@
    372:    SVDLanczosGetOneSide - Gets if the variant of the Lanczos method
    373:    to be used is one-sided or two-sided.

    375:    Not Collective

    377:    Input Parameters:
    378: .  svd     - singular value solver

    380:    Output Parameters:
    381: .  oneside - boolean flag indicating if the method is one-sided or not

    383:    Level: advanced

    385: .seealso: SVDLanczosSetOneSide()
    386: @*/
    387: PetscErrorCode SVDLanczosGetOneSide(SVD svd,PetscBool *oneside)
    388: {
    391:   PetscUseMethod(svd,"SVDLanczosGetOneSide_C",(SVD,PetscBool*),(svd,oneside));
    392:   return 0;
    393: }

    395: PetscErrorCode SVDDestroy_Lanczos(SVD svd)
    396: {
    397:   PetscFree(svd->data);
    398:   PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosSetOneSide_C",NULL);
    399:   PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosGetOneSide_C",NULL);
    400:   return 0;
    401: }

    403: PetscErrorCode SVDView_Lanczos(SVD svd,PetscViewer viewer)
    404: {
    405:   SVD_LANCZOS    *lanczos = (SVD_LANCZOS*)svd->data;
    406:   PetscBool      isascii;

    408:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii);
    409:   if (isascii) PetscViewerASCIIPrintf(viewer,"  %s-sided reorthogonalization\n",lanczos->oneside? "one": "two");
    410:   return 0;
    411: }

    413: SLEPC_EXTERN PetscErrorCode SVDCreate_Lanczos(SVD svd)
    414: {
    415:   SVD_LANCZOS    *ctx;

    417:   PetscNew(&ctx);
    418:   svd->data = (void*)ctx;

    420:   svd->ops->setup          = SVDSetUp_Lanczos;
    421:   svd->ops->solve          = SVDSolve_Lanczos;
    422:   svd->ops->destroy        = SVDDestroy_Lanczos;
    423:   svd->ops->setfromoptions = SVDSetFromOptions_Lanczos;
    424:   svd->ops->view           = SVDView_Lanczos;
    425:   svd->ops->computevectors = SVDComputeVectors_Left;
    426:   PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosSetOneSide_C",SVDLanczosSetOneSide_Lanczos);
    427:   PetscObjectComposeFunction((PetscObject)svd,"SVDLanczosGetOneSide_C",SVDLanczosGetOneSide_Lanczos);
    428:   return 0;
    429: }