Actual source code: dsnep.c

slepc-3.7.0 2016-05-16
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  1: /*
  2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
  4:    Copyright (c) 2002-2016, Universitat Politecnica de Valencia, Spain

  6:    This file is part of SLEPc.

  8:    SLEPc is free software: you can redistribute it and/or modify it under  the
  9:    terms of version 3 of the GNU Lesser General Public License as published by
 10:    the Free Software Foundation.

 12:    SLEPc  is  distributed in the hope that it will be useful, but WITHOUT  ANY
 13:    WARRANTY;  without even the implied warranty of MERCHANTABILITY or  FITNESS
 14:    FOR  A  PARTICULAR PURPOSE. See the GNU Lesser General Public  License  for
 15:    more details.

 17:    You  should have received a copy of the GNU Lesser General  Public  License
 18:    along with SLEPc. If not, see <http://www.gnu.org/licenses/>.
 19:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 20: */

 22: #include <slepc/private/dsimpl.h>       /*I "slepcds.h" I*/
 23: #include <slepcblaslapack.h>

 25: typedef struct {
 26:   PetscInt nf;                 /* number of functions in f[] */
 27:   FN       f[DS_NUM_EXTRA];    /* functions defining the nonlinear operator */
 28: } DS_NEP;

 32: /*
 33:    DSNEPComputeMatrix - Build the matrix associated with a nonlinear operator
 34:    T(lambda) or its derivative T'(lambda), given the parameter lambda, where
 35:    T(lambda) = sum_i E_i*f_i(lambda). The result is written in mat.
 36: */
 37: static PetscErrorCode DSNEPComputeMatrix(DS ds,PetscScalar lambda,PetscBool deriv,DSMatType mat)
 38: {
 40:   DS_NEP         *ctx = (DS_NEP*)ds->data;
 41:   PetscScalar    *T,*E,alpha;
 42:   PetscInt       i,ld,n;
 43:   PetscBLASInt   k,inc=1;

 46:   DSGetDimensions(ds,&n,NULL,NULL,NULL,NULL);
 47:   DSGetLeadingDimension(ds,&ld);
 48:   PetscBLASIntCast(ld*n,&k);
 49:   PetscLogEventBegin(DS_Other,ds,0,0,0);
 50:   DSGetArray(ds,mat,&T);
 51:   PetscMemzero(T,k*sizeof(PetscScalar));
 52:   for (i=0;i<ctx->nf;i++) {
 53:     if (deriv) {
 54:       FNEvaluateDerivative(ctx->f[i],lambda,&alpha);
 55:     } else {
 56:       FNEvaluateFunction(ctx->f[i],lambda,&alpha);
 57:     }
 58:     E = ds->mat[DSMatExtra[i]];
 59:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&k,&alpha,E,&inc,T,&inc));
 60:   }
 61:   DSRestoreArray(ds,mat,&T);
 62:   PetscLogEventEnd(DS_Other,ds,0,0,0);
 63:   return(0);
 64: }

 68: PetscErrorCode DSAllocate_NEP(DS ds,PetscInt ld)
 69: {
 71:   DS_NEP         *ctx = (DS_NEP*)ds->data;
 72:   PetscInt       i;

 75:   if (!ctx->nf) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"DSNEP requires passing some functions via DSSetFN()");
 76:   DSAllocateMat_Private(ds,DS_MAT_X);
 77:   for (i=0;i<ctx->nf;i++) {
 78:     DSAllocateMat_Private(ds,DSMatExtra[i]);
 79:   }
 80:   PetscFree(ds->perm);
 81:   PetscMalloc1(ld,&ds->perm);
 82:   PetscLogObjectMemory((PetscObject)ds,ld*sizeof(PetscInt));
 83:   return(0);
 84: }

 88: PetscErrorCode DSView_NEP(DS ds,PetscViewer viewer)
 89: {
 90:   PetscErrorCode    ierr;
 91:   DS_NEP            *ctx = (DS_NEP*)ds->data;
 92:   PetscViewerFormat format;
 93:   PetscInt          i;

 96:   PetscViewerGetFormat(viewer,&format);
 97:   PetscViewerASCIIPrintf(viewer,"  number of functions: %D\n",ctx->nf);
 98:   if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) return(0);
 99:   for (i=0;i<ctx->nf;i++) {
100:     FNView(ctx->f[i],viewer);
101:     DSViewMat(ds,viewer,DSMatExtra[i]);
102:   }
103:   if (ds->state>DS_STATE_INTERMEDIATE) {
104:     DSViewMat(ds,viewer,DS_MAT_X);
105:   }
106:   return(0);
107: }

111: PetscErrorCode DSVectors_NEP(DS ds,DSMatType mat,PetscInt *j,PetscReal *rnorm)
112: {
114:   if (rnorm) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not implemented yet");
115:   switch (mat) {
116:     case DS_MAT_X:
117:       break;
118:     case DS_MAT_Y:
119:       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not implemented yet");
120:       break;
121:     default:
122:       SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Invalid mat parameter");
123:   }
124:   return(0);
125: }

129: PetscErrorCode DSNormalize_NEP(DS ds,DSMatType mat,PetscInt col)
130: {
132:   PetscInt       i,i0,i1;
133:   PetscBLASInt   ld,n,one = 1;
134:   PetscScalar    norm,*x;

137:   switch (mat) {
138:     case DS_MAT_X:
139:       break;
140:     case DS_MAT_Y:
141:       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not implemented yet");
142:       break;
143:     default:
144:       SETERRQ(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Invalid mat parameter");
145:   }
146:   PetscBLASIntCast(ds->n,&n);
147:   PetscBLASIntCast(ds->ld,&ld);
148:   DSGetArray(ds,mat,&x);
149:   if (col < 0) {
150:     i0 = 0; i1 = ds->n;
151:   } else {
152:     i0 = col; i1 = col+1;
153:   }
154:   for (i=i0;i<i1;i++) {
155:     norm = BLASnrm2_(&n,&x[ld*i],&one);
156:     norm = 1.0/norm;
157:     PetscStackCallBLAS("BLASscal",BLASscal_(&n,&norm,&x[ld*i],&one));
158:   }
159:   return(0);
160: }

164: PetscErrorCode DSSort_NEP(DS ds,PetscScalar *wr,PetscScalar *wi,PetscScalar *rr,PetscScalar *ri,PetscInt *k)
165: {
167:   PetscInt       n,l,i,*perm,ld=ds->ld;
168:   PetscScalar    *A;

171:   if (!ds->sc) return(0);
172:   n = ds->n;
173:   l = ds->l;
174:   A  = ds->mat[DS_MAT_A];
175:   perm = ds->perm;
176:   for (i=l;i<n;i++) wr[i] = A[i+i*ld];
177:   if (rr) {
178:     DSSortEigenvalues_Private(ds,rr,ri,perm,PETSC_FALSE);
179:   } else {
180:     DSSortEigenvalues_Private(ds,wr,NULL,perm,PETSC_FALSE);
181:   }
182:   for (i=l;i<n;i++) A[i+i*ld] = wr[perm[i]];
183:   for (i=l;i<n;i++) wr[i] = A[i+i*ld];
184:   DSPermuteColumns_Private(ds,l,n,DS_MAT_Q,perm);
185:   return(0);
186: }

190: PetscErrorCode DSSolve_NEP_SLP(DS ds,PetscScalar *wr,PetscScalar *wi)
191: {
192: #if defined(SLEPC_MISSING_LAPACK_GGEV)
194:   SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"GGEV - Lapack routine is unavailable");
195: #else
197:   PetscScalar    *A,*B,*W,*X,*work,*alpha,*beta;
198:   PetscScalar    norm,sigma,lambda,mu,re,re2;
199:   PetscBLASInt   info,n,ld,lrwork=0,lwork,one=1;
200:   PetscInt       it,pos,j,maxit=100,result;
201:   PetscReal      tol;
202: #if defined(PETSC_USE_COMPLEX)
203:   PetscReal      *rwork;
204: #else
205:   PetscReal      *alphai,im,im2;
206: #endif

209:   if (!ds->mat[DS_MAT_A]) {
210:     DSAllocateMat_Private(ds,DS_MAT_A);
211:   }
212:   if (!ds->mat[DS_MAT_B]) {
213:     DSAllocateMat_Private(ds,DS_MAT_B);
214:   }
215:   if (!ds->mat[DS_MAT_W]) {
216:     DSAllocateMat_Private(ds,DS_MAT_W);
217:   }
218:   PetscBLASIntCast(ds->n,&n);
219:   PetscBLASIntCast(ds->ld,&ld);
220: #if defined(PETSC_USE_COMPLEX)
221:   PetscBLASIntCast(2*ds->n+2*ds->n,&lwork);
222:   PetscBLASIntCast(8*ds->n,&lrwork);
223: #else
224:   PetscBLASIntCast(3*ds->n+8*ds->n,&lwork);
225: #endif
226:   DSAllocateWork_Private(ds,lwork,lrwork,0);
227:   alpha = ds->work;
228:   beta = ds->work + ds->n;
229: #if defined(PETSC_USE_COMPLEX)
230:   work = ds->work + 2*ds->n;
231:   lwork -= 2*ds->n;
232: #else
233:   alphai = ds->work + 2*ds->n;
234:   work = ds->work + 3*ds->n;
235:   lwork -= 3*ds->n;
236: #endif
237:   A = ds->mat[DS_MAT_A];
238:   B = ds->mat[DS_MAT_B];
239:   W = ds->mat[DS_MAT_W];
240:   X = ds->mat[DS_MAT_X];

242:   sigma = 0.0;
243:   lambda = sigma;
244:   tol = 1000*n*PETSC_MACHINE_EPSILON;

246:   for (it=0;it<maxit;it++) {

248:     /* evaluate T and T' */
249:     DSNEPComputeMatrix(ds,lambda,PETSC_FALSE,DS_MAT_A);
250:     DSNEPComputeMatrix(ds,lambda,PETSC_TRUE,DS_MAT_B);

252:     /* compute eigenvalue correction mu and eigenvector u */
253: #if defined(PETSC_USE_COMPLEX)
254:     rwork = ds->rwork;
255:     PetscStackCallBLAS("LAPACKggev",LAPACKggev_("N","V",&n,A,&ld,B,&ld,alpha,beta,NULL,&ld,W,&ld,work,&lwork,rwork,&info));
256:     if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in Lapack ZGGEV %d",info);
257: #else
258:     PetscStackCallBLAS("LAPACKggev",LAPACKggev_("N","V",&n,A,&ld,B,&ld,alpha,alphai,beta,NULL,&ld,W,&ld,work,&lwork,&info));
259:     if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error in Lapack DGGEV %d",info);
260: #endif

262:     /* find smallest eigenvalue */
263:     j = 0;
264:     if (beta[j]==0.0) re = (PetscRealPart(alpha[j])>0.0)? PETSC_MAX_REAL: PETSC_MIN_REAL;
265:     else re = alpha[j]/beta[j];
266: #if !defined(PETSC_USE_COMPLEX)
267:     if (beta[j]==0.0) im = (alphai[j]>0.0)? PETSC_MAX_REAL: PETSC_MIN_REAL;
268:     else im = alphai[j]/beta[j];
269: #endif
270:     pos = 0;
271:     for (j=1;j<n;j++) {
272:       if (beta[j]==0.0) re2 = (PetscRealPart(alpha[j])>0.0)? PETSC_MAX_REAL: PETSC_MIN_REAL;
273:       else re2 = alpha[j]/beta[j];
274: #if !defined(PETSC_USE_COMPLEX)
275:       if (beta[j]==0.0) im2 = (alphai[j]>0.0)? PETSC_MAX_REAL: PETSC_MIN_REAL;
276:       else im2 = alphai[j]/beta[j];
277:       SlepcCompareSmallestMagnitude(re,im,re2,im2,&result,NULL);
278: #else
279:       SlepcCompareSmallestMagnitude(re,0.0,re2,0.0,&result,NULL);
280: #endif
281:       if (result > 0) {
282:         re = re2;
283: #if !defined(PETSC_USE_COMPLEX)
284:         im = im2;
285: #endif
286:         pos = j;
287:       }
288:     }

290: #if !defined(PETSC_USE_COMPLEX)
291:     if (im!=0.0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"DSNEP found a complex eigenvalue; try rerunning with complex scalars");
292: #endif
293:     mu = alpha[pos];
294:     PetscMemcpy(X,W+pos*ld,n*sizeof(PetscScalar));
295:     norm = BLASnrm2_(&n,X,&one);
296:     norm = 1.0/norm;
297:     PetscStackCallBLAS("BLASscal",BLASscal_(&n,&norm,X,&one));

299:     /* correct eigenvalue approximation */
300:     lambda = lambda - mu;
301:     if (PetscAbsScalar(mu)<=tol) break;
302:   }

304:   wr[0] = lambda;
305:   if (wi) wi[0] = 0.0;

307:   if (it==maxit) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_CONV_FAILED,"DSNEP did not converge");
308:   return(0);
309: #endif
310: }

314: static PetscErrorCode DSNEPSetFN_NEP(DS ds,PetscInt n,FN fn[])
315: {
317:   DS_NEP         *ctx = (DS_NEP*)ds->data;
318:   PetscInt       i;

321:   if (n<=0) SETERRQ1(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Must have one or more functions, you have %D",n);
322:   if (n>DS_NUM_EXTRA) SETERRQ2(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"Too many functions, you specified %D but the limit is %D",n,DS_NUM_EXTRA);
323:   if (ds->ld) { PetscInfo(ds,"DSNEPSetFN() called after DSAllocate()\n"); }
324:   for (i=0;i<ctx->nf;i++) {
325:     FNDestroy(&ctx->f[i]);
326:   }
327:   for (i=0;i<n;i++) {
328:     PetscObjectReference((PetscObject)fn[i]);
329:     ctx->f[i] = fn[i];
330:   }
331:   ctx->nf = n;
332:   return(0);
333: }

337: /*@
338:    DSNEPSetFN - Sets a number of functions that define the nonlinear
339:    eigenproblem.

341:    Collective on DS and FN

343:    Input Parameters:
344: +  ds - the direct solver context
345: .  n  - number of functions
346: -  fn - array of functions

348:    Notes:
349:    The nonlinear eigenproblem is defined in terms of the split nonlinear
350:    operator T(lambda) = sum_i A_i*f_i(lambda).

352:    This function must be called before DSAllocate(). Then DSAllocate()
353:    will allocate an extra matrix A_i per each function, that can be
354:    filled in the usual way.

356:    Level: advanced

358: .seealso: DSNEPGetFN(), DSAllocate()
359:  @*/
360: PetscErrorCode DSNEPSetFN(DS ds,PetscInt n,FN fn[])
361: {
362:   PetscInt       i;

369:   for (i=0;i<n;i++) {
372:   }
373:   PetscTryMethod(ds,"DSNEPSetFN_C",(DS,PetscInt,FN[]),(ds,n,fn));
374:   return(0);
375: }

379: static PetscErrorCode DSNEPGetFN_NEP(DS ds,PetscInt k,FN *fn)
380: {
381:   DS_NEP *ctx = (DS_NEP*)ds->data;

384:   if (k<0 || k>=ctx->nf) SETERRQ1(PetscObjectComm((PetscObject)ds),PETSC_ERR_ARG_OUTOFRANGE,"k must be between 0 and %D",ctx->nf-1);
385:   *fn = ctx->f[k];
386:   return(0);
387: }

391: /*@
392:    DSNEPGetFN - Gets the functions associated with the nonlinear DS.

394:    Not collective, though parallel FNs are returned if the DS is parallel

396:    Input Parameter:
397: +  ds - the direct solver context
398: -  k  - the index of the requested function (starting in 0)

400:    Output Parameter:
401: .  fn - the function

403:    Level: advanced

405: .seealso: DSNEPSetFN()
406: @*/
407: PetscErrorCode DSNEPGetFN(DS ds,PetscInt k,FN *fn)
408: {

414:   PetscUseMethod(ds,"DSNEPGetFN_C",(DS,PetscInt,FN*),(ds,k,fn));
415:   return(0);
416: }

420: static PetscErrorCode DSNEPGetNumFN_NEP(DS ds,PetscInt *n)
421: {
422:   DS_NEP *ctx = (DS_NEP*)ds->data;

425:   *n = ctx->nf;
426:   return(0);
427: }

431: /*@
432:    DSNEPGetNumFN - Returns the number of functions stored internally by
433:    the DS.

435:    Not collective

437:    Input Parameter:
438: .  ds - the direct solver context

440:    Output Parameters:
441: .  n - the number of functions passed in DSNEPSetFN()

443:    Level: advanced

445: .seealso: DSNEPSetFN()
446: @*/
447: PetscErrorCode DSNEPGetNumFN(DS ds,PetscInt *n)
448: {

454:   PetscUseMethod(ds,"DSNEPGetNumFN_C",(DS,PetscInt*),(ds,n));
455:   return(0);
456: }

460: PetscErrorCode DSDestroy_NEP(DS ds)
461: {
463:   DS_NEP         *ctx = (DS_NEP*)ds->data;
464:   PetscInt       i;

467:   for (i=0;i<ctx->nf;i++) {
468:     FNDestroy(&ctx->f[i]);
469:   }
470:   PetscFree(ds->data);
471:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPSetFN_C",NULL);
472:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPGetFN_C",NULL);
473:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPGetNumFN_C",NULL);
474:   return(0);
475: }

479: PETSC_EXTERN PetscErrorCode DSCreate_NEP(DS ds)
480: {
481:   DS_NEP         *ctx;

485:   PetscNewLog(ds,&ctx);
486:   ds->data = (void*)ctx;

488:   ds->ops->allocate      = DSAllocate_NEP;
489:   ds->ops->view          = DSView_NEP;
490:   ds->ops->vectors       = DSVectors_NEP;
491:   ds->ops->solve[0]      = DSSolve_NEP_SLP;
492:   ds->ops->sort          = DSSort_NEP;
493:   ds->ops->normalize     = DSNormalize_NEP;
494:   ds->ops->destroy       = DSDestroy_NEP;
495:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPSetFN_C",DSNEPSetFN_NEP);
496:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPGetFN_C",DSNEPGetFN_NEP);
497:   PetscObjectComposeFunction((PetscObject)ds,"DSNEPGetNumFN_C",DSNEPGetNumFN_NEP);
498:   return(0);
499: }