Actual source code: stoar.c
slepc-3.18.2 2023-01-26
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 polynomial eigensolver: "stoar"
13: Method: S-TOAR
15: Algorithm:
17: Symmetric Two-Level Orthogonal Arnoldi.
19: References:
21: [1] C. Campos and J.E. Roman, "Restarted Q-Arnoldi-type methods
22: exploiting symmetry in quadratic eigenvalue problems", BIT
23: Numer. Math. 56(4):1213-1236, 2016.
24: */
26: #include <slepc/private/pepimpl.h>
27: #include "../src/pep/impls/krylov/pepkrylov.h"
28: #include <slepcblaslapack.h>
30: static PetscBool cited = PETSC_FALSE;
31: static const char citation[] =
32: "@Article{slepc-stoar,\n"
33: " author = \"C. Campos and J. E. Roman\",\n"
34: " title = \"Restarted {Q-Arnoldi-type} methods exploiting symmetry in quadratic eigenvalue problems\",\n"
35: " journal = \"{BIT} Numer. Math.\",\n"
36: " volume = \"56\",\n"
37: " number = \"4\",\n"
38: " pages = \"1213--1236\",\n"
39: " year = \"2016,\"\n"
40: " doi = \"https://doi.org/10.1007/s10543-016-0601-5\"\n"
41: "}\n";
43: typedef struct {
44: PetscReal scal[2];
45: Mat A[2];
46: Vec t;
47: } PEP_STOAR_MATSHELL;
49: static PetscErrorCode MatMult_STOAR(Mat A,Vec x,Vec y)
50: {
51: PEP_STOAR_MATSHELL *ctx;
53: MatShellGetContext(A,&ctx);
54: MatMult(ctx->A[0],x,y);
55: VecScale(y,ctx->scal[0]);
56: if (ctx->scal[1]) {
57: MatMult(ctx->A[1],x,ctx->t);
58: VecAXPY(y,ctx->scal[1],ctx->t);
59: }
60: return 0;
61: }
63: static PetscErrorCode MatDestroy_STOAR(Mat A)
64: {
65: PEP_STOAR_MATSHELL *ctx;
67: MatShellGetContext(A,&ctx);
68: VecDestroy(&ctx->t);
69: PetscFree(ctx);
70: return 0;
71: }
73: PetscErrorCode PEPSTOARSetUpInnerMatrix(PEP pep,Mat *B)
74: {
75: Mat pB[4],Bs[3],D[3];
76: PetscInt i,j,n,m;
77: PEP_STOAR_MATSHELL *ctxMat[3];
78: PEP_STOAR *ctx=(PEP_STOAR*)pep->data;
80: for (i=0;i<3;i++) {
81: STGetMatrixTransformed(pep->st,i,&D[i]); /* D[2] = M */
82: }
83: MatGetLocalSize(D[2],&m,&n);
85: for (j=0;j<3;j++) {
86: PetscNew(ctxMat+j);
87: MatCreateShell(PetscObjectComm((PetscObject)pep),m,n,PETSC_DETERMINE,PETSC_DETERMINE,ctxMat[j],&Bs[j]);
88: MatShellSetOperation(Bs[j],MATOP_MULT,(void(*)(void))MatMult_STOAR);
89: MatShellSetOperation(Bs[j],MATOP_DESTROY,(void(*)(void))MatDestroy_STOAR);
90: }
91: for (i=0;i<4;i++) pB[i] = NULL;
92: if (ctx->alpha) {
93: ctxMat[0]->A[0] = D[0]; ctxMat[0]->scal[0] = ctx->alpha; ctxMat[0]->scal[1] = 0.0;
94: ctxMat[2]->A[0] = D[2]; ctxMat[2]->scal[0] = -ctx->alpha*pep->sfactor*pep->sfactor; ctxMat[2]->scal[1] = 0.0;
95: pB[0] = Bs[0]; pB[3] = Bs[2];
96: }
97: if (ctx->beta) {
98: i = (ctx->alpha)?1:0;
99: ctxMat[0]->scal[1] = 0.0;
100: ctxMat[0]->A[i] = D[1]; ctxMat[0]->scal[i] = -ctx->beta*pep->sfactor;
101: ctxMat[1]->A[0] = D[2]; ctxMat[1]->scal[0] = -ctx->beta*pep->sfactor*pep->sfactor; ctxMat[1]->scal[1] = 0.0;
102: pB[0] = Bs[0]; pB[1] = pB[2] = Bs[1];
103: }
104: BVCreateVec(pep->V,&ctxMat[0]->t);
105: MatCreateNest(PetscObjectComm((PetscObject)pep),2,NULL,2,NULL,pB,B);
106: for (j=0;j<3;j++) MatDestroy(&Bs[j]);
107: return 0;
108: }
110: PetscErrorCode PEPSetUp_STOAR(PEP pep)
111: {
112: PetscBool sinv,flg;
113: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
114: PetscInt ld,i;
115: PetscReal eta;
116: BVOrthogType otype;
117: BVOrthogBlockType obtype;
119: PEPCheckHermitian(pep);
120: PEPCheckQuadratic(pep);
121: PEPCheckShiftSinvert(pep);
122: /* spectrum slicing requires special treatment of default values */
123: if (pep->which==PEP_ALL) {
124: pep->ops->solve = PEPSolve_STOAR_QSlice;
125: pep->ops->extractvectors = NULL;
126: pep->ops->setdefaultst = NULL;
127: PEPSetUp_STOAR_QSlice(pep);
128: } else {
129: PEPSetDimensions_Default(pep,pep->nev,&pep->ncv,&pep->mpd);
131: if (pep->max_it==PETSC_DEFAULT) pep->max_it = PetscMax(100,2*(pep->nmat-1)*pep->n/pep->ncv);
132: pep->ops->solve = PEPSolve_STOAR;
133: ld = pep->ncv+2;
134: DSSetType(pep->ds,DSGHIEP);
135: DSSetCompact(pep->ds,PETSC_TRUE);
136: DSSetExtraRow(pep->ds,PETSC_TRUE);
137: DSAllocate(pep->ds,ld);
138: PEPBasisCoefficients(pep,pep->pbc);
139: STGetTransform(pep->st,&flg);
140: if (!flg) {
141: PetscFree(pep->solvematcoeffs);
142: PetscMalloc1(pep->nmat,&pep->solvematcoeffs);
143: PetscObjectTypeCompare((PetscObject)pep->st,STSINVERT,&sinv);
144: if (sinv) PEPEvaluateBasis(pep,pep->target,0,pep->solvematcoeffs,NULL);
145: else {
146: for (i=0;i<pep->nmat-1;i++) pep->solvematcoeffs[i] = 0.0;
147: pep->solvematcoeffs[pep->nmat-1] = 1.0;
148: }
149: }
150: }
151: if (!pep->which) PEPSetWhichEigenpairs_Default(pep);
152: PEPCheckUnsupported(pep,PEP_FEATURE_NONMONOMIAL | PEP_FEATURE_REGION);
154: PEPAllocateSolution(pep,2);
155: PEPSetWorkVecs(pep,4);
156: BVDestroy(&ctx->V);
157: BVCreateTensor(pep->V,pep->nmat-1,&ctx->V);
158: BVGetOrthogonalization(pep->V,&otype,NULL,&eta,&obtype);
159: BVSetOrthogonalization(ctx->V,otype,BV_ORTHOG_REFINE_ALWAYS,eta,obtype);
160: return 0;
161: }
163: /*
164: Compute a run of Lanczos iterations. dim(work)=(ctx->ld)*4
165: */
166: static PetscErrorCode PEPSTOARrun(PEP pep,PetscReal *a,PetscReal *b,PetscReal *omega,PetscInt k,PetscInt *M,PetscBool *breakdown,PetscBool *symmlost,Vec *t_)
167: {
168: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
169: PetscInt i,j,m=*M,l,lock;
170: PetscInt lds,d,ld,offq,nqt,ldds;
171: Vec v=t_[0],t=t_[1],q=t_[2];
172: PetscReal norm,sym=0.0,fro=0.0,*f;
173: PetscScalar *y,*S,*x,sigma;
174: PetscBLASInt j_,one=1;
175: PetscBool lindep,flg,sinvert=PETSC_FALSE;
176: Mat MS;
178: PetscMalloc1(*M,&y);
179: BVGetSizes(pep->V,NULL,NULL,&ld);
180: BVTensorGetDegree(ctx->V,&d);
181: BVGetActiveColumns(pep->V,&lock,&nqt);
182: lds = d*ld;
183: offq = ld;
184: DSGetLeadingDimension(pep->ds,&ldds);
185: *breakdown = PETSC_FALSE; /* ----- */
186: DSGetDimensions(pep->ds,NULL,&l,NULL,NULL);
187: BVSetActiveColumns(ctx->V,0,m);
188: BVSetActiveColumns(pep->V,0,nqt);
189: STGetTransform(pep->st,&flg);
190: if (!flg) {
191: /* spectral transformation handled by the solver */
192: PetscObjectTypeCompareAny((PetscObject)pep->st,&flg,STSINVERT,STSHIFT,"");
194: PetscObjectTypeCompare((PetscObject)pep->st,STSINVERT,&sinvert);
195: STGetShift(pep->st,&sigma);
196: }
197: for (j=k;j<m;j++) {
198: /* apply operator */
199: BVTensorGetFactors(ctx->V,NULL,&MS);
200: MatDenseGetArray(MS,&S);
201: BVGetColumn(pep->V,nqt,&t);
202: BVMultVec(pep->V,1.0,0.0,v,S+j*lds);
203: if (!sinvert) {
204: STMatMult(pep->st,0,v,q);
205: BVMultVec(pep->V,1.0,0.0,v,S+offq+j*lds);
206: STMatMult(pep->st,1,v,t);
207: VecAXPY(q,pep->sfactor,t);
208: if (ctx->beta && ctx->alpha) {
209: STMatMult(pep->st,2,v,t);
210: VecAXPY(q,-pep->sfactor*pep->sfactor*ctx->beta/ctx->alpha,t);
211: }
212: STMatSolve(pep->st,q,t);
213: VecScale(t,-1.0/(pep->sfactor*pep->sfactor));
214: } else {
215: STMatMult(pep->st,1,v,q);
216: STMatMult(pep->st,2,v,t);
217: VecAXPY(q,sigma*pep->sfactor,t);
218: VecScale(q,pep->sfactor);
219: BVMultVec(pep->V,1.0,0.0,v,S+offq+j*lds);
220: STMatMult(pep->st,2,v,t);
221: VecAXPY(q,pep->sfactor*pep->sfactor,t);
222: STMatSolve(pep->st,q,t);
223: VecScale(t,-1.0);
224: }
225: BVRestoreColumn(pep->V,nqt,&t);
227: /* orthogonalize */
228: if (!sinvert) x = S+offq+(j+1)*lds;
229: else x = S+(j+1)*lds;
230: BVOrthogonalizeColumn(pep->V,nqt,x,&norm,&lindep);
232: if (!lindep) {
233: if (!sinvert) *(S+offq+(j+1)*lds+nqt) = norm;
234: else *(S+(j+1)*lds+nqt) = norm;
235: BVScaleColumn(pep->V,nqt,1.0/norm);
236: nqt++;
237: }
238: if (!sinvert) {
239: for (i=0;i<=nqt-1;i++) *(S+(j+1)*lds+i) = *(S+offq+j*lds+i);
240: if (ctx->beta && ctx->alpha) {
241: for (i=0;i<=nqt-1;i++) *(S+(j+1)*lds+offq+i) -= *(S+(j+1)*lds+i)*ctx->beta/ctx->alpha;
242: }
243: } else for (i=0;i<nqt;i++) *(S+(j+1)*lds+offq+i) = *(S+j*lds+i)+sigma*(*(S+(j+1)*lds+i));
244: BVSetActiveColumns(pep->V,0,nqt);
245: MatDenseRestoreArray(MS,&S);
246: BVTensorRestoreFactors(ctx->V,NULL,&MS);
248: /* level-2 orthogonalization */
249: BVOrthogonalizeColumn(ctx->V,j+1,y,&norm,&lindep);
250: a[j] = PetscRealPart(y[j]);
251: omega[j+1] = (norm > 0)?1.0:-1.0;
252: BVScaleColumn(ctx->V,j+1,1.0/norm);
253: b[j] = PetscAbsReal(norm);
255: /* check symmetry */
256: DSGetArrayReal(pep->ds,DS_MAT_T,&f);
257: if (j==k) {
258: for (i=l;i<j-1;i++) y[i] = PetscAbsScalar(y[i])-PetscAbsReal(f[2*ldds+i]);
259: for (i=0;i<l;i++) y[i] = 0.0;
260: }
261: DSRestoreArrayReal(pep->ds,DS_MAT_T,&f);
262: if (j>0) y[j-1] = PetscAbsScalar(y[j-1])-PetscAbsReal(b[j-1]);
263: PetscBLASIntCast(j,&j_);
264: sym = SlepcAbs(BLASnrm2_(&j_,y,&one),sym);
265: fro = SlepcAbs(fro,SlepcAbs(a[j],b[j]));
266: if (j>0) fro = SlepcAbs(fro,b[j-1]);
267: if (sym/fro>PetscMax(PETSC_SQRT_MACHINE_EPSILON,10*pep->tol)) {
268: *symmlost = PETSC_TRUE;
269: *M=j;
270: break;
271: }
272: }
273: BVSetActiveColumns(pep->V,lock,nqt);
274: BVSetActiveColumns(ctx->V,0,*M);
275: PetscFree(y);
276: return 0;
277: }
279: #if 0
280: static PetscErrorCode PEPSTOARpreKConvergence(PEP pep,PetscInt nv,PetscReal *norm,Vec *w)
281: {
282: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
283: PetscBLASInt n_,one=1;
284: PetscInt lds=2*ctx->ld;
285: PetscReal t1,t2;
286: PetscScalar *S=ctx->S;
288: PetscBLASIntCast(nv+2,&n_);
289: t1 = BLASnrm2_(&n_,S+nv*2*ctx->ld,&one);
290: t2 = BLASnrm2_(&n_,S+(nv*2+1)*ctx->ld,&one);
291: *norm = SlepcAbs(t1,t2);
292: BVSetActiveColumns(pep->V,0,nv+2);
293: BVMultVec(pep->V,1.0,0.0,w[1],S+nv*lds);
294: STMatMult(pep->st,0,w[1],w[2]);
295: VecNorm(w[2],NORM_2,&t1);
296: BVMultVec(pep->V,1.0,0.0,w[1],S+ctx->ld+nv*lds);
297: STMatMult(pep->st,2,w[1],w[2]);
298: VecNorm(w[2],NORM_2,&t2);
299: t2 *= pep->sfactor*pep->sfactor;
300: *norm = PetscMax(*norm,SlepcAbs(t1,t2));
301: return 0;
302: }
303: #endif
305: PetscErrorCode PEPSolve_STOAR(PEP pep)
306: {
307: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
308: PetscInt j,k,l,nv=0,ld,ldds,t,nq=0;
309: PetscInt nconv=0,deg=pep->nmat-1;
310: PetscScalar sigma;
311: PetscReal beta,norm=1.0,*omega,*a,*b;
312: PetscBool breakdown,symmlost=PETSC_FALSE,sinv=PETSC_FALSE,falselock=PETSC_TRUE,flg;
313: Mat MQ,A,D;
314: Vec vomega;
316: PetscCitationsRegister(citation,&cited);
317: PEPSTOARSetUpInnerMatrix(pep,&A);
318: BVSetMatrix(ctx->V,A,PETSC_TRUE);
319: MatDestroy(&A);
320: if (ctx->lock) {
321: /* undocumented option to use a cheaper locking instead of the true locking */
322: PetscOptionsGetBool(NULL,NULL,"-pep_stoar_falselocking",&falselock,NULL);
323: }
324: BVGetSizes(pep->V,NULL,NULL,&ld);
325: STGetShift(pep->st,&sigma);
326: STGetTransform(pep->st,&flg);
327: if (pep->sfactor!=1.0) {
328: if (!flg) {
329: pep->target /= pep->sfactor;
330: RGPushScale(pep->rg,1.0/pep->sfactor);
331: STScaleShift(pep->st,1.0/pep->sfactor);
332: sigma /= pep->sfactor;
333: } else {
334: PetscObjectTypeCompare((PetscObject)pep->st,STSINVERT,&sinv);
335: pep->target = sinv?pep->target*pep->sfactor:pep->target/pep->sfactor;
336: RGPushScale(pep->rg,sinv?pep->sfactor:1.0/pep->sfactor);
337: STScaleShift(pep->st,sinv?pep->sfactor:1.0/pep->sfactor);
338: }
339: }
340: if (flg) sigma = 0.0;
342: /* Get the starting Arnoldi vector */
343: BVTensorBuildFirstColumn(ctx->V,pep->nini);
344: DSSetDimensions(pep->ds,1,PETSC_DEFAULT,PETSC_DEFAULT);
345: BVSetActiveColumns(ctx->V,0,1);
346: DSGetMatAndColumn(pep->ds,DS_MAT_D,0,&D,&vomega);
347: BVGetSignature(ctx->V,vomega);
348: DSRestoreMatAndColumn(pep->ds,DS_MAT_D,0,&D,&vomega);
350: /* Restart loop */
351: l = 0;
352: DSGetLeadingDimension(pep->ds,&ldds);
353: while (pep->reason == PEP_CONVERGED_ITERATING) {
354: pep->its++;
355: DSGetArrayReal(pep->ds,DS_MAT_T,&a);
356: b = a+ldds;
357: DSGetArrayReal(pep->ds,DS_MAT_D,&omega);
359: /* Compute an nv-step Lanczos factorization */
360: nv = PetscMin(pep->nconv+pep->mpd,pep->ncv);
361: DSSetDimensions(pep->ds,nv,pep->nconv,pep->nconv+l);
362: PEPSTOARrun(pep,a,b,omega,pep->nconv+l,&nv,&breakdown,&symmlost,pep->work);
363: beta = b[nv-1];
364: if (symmlost && nv==pep->nconv+l) {
365: pep->reason = PEP_DIVERGED_SYMMETRY_LOST;
366: pep->nconv = nconv;
367: if (falselock || !ctx->lock) {
368: BVSetActiveColumns(ctx->V,0,pep->nconv);
369: BVTensorCompress(ctx->V,0);
370: }
371: break;
372: }
373: DSRestoreArrayReal(pep->ds,DS_MAT_T,&a);
374: DSRestoreArrayReal(pep->ds,DS_MAT_D,&omega);
375: DSSetDimensions(pep->ds,nv,pep->nconv,pep->nconv+l);
376: if (l==0) DSSetState(pep->ds,DS_STATE_INTERMEDIATE);
377: else DSSetState(pep->ds,DS_STATE_RAW);
379: /* Solve projected problem */
380: DSSolve(pep->ds,pep->eigr,pep->eigi);
381: DSSort(pep->ds,pep->eigr,pep->eigi,NULL,NULL,NULL);
382: DSUpdateExtraRow(pep->ds);
383: DSSynchronize(pep->ds,pep->eigr,pep->eigi);
385: /* Check convergence */
386: /* PEPSTOARpreKConvergence(pep,nv,&norm,pep->work);*/
387: norm = 1.0;
388: DSGetDimensions(pep->ds,NULL,NULL,NULL,&t);
389: PEPKrylovConvergence(pep,PETSC_FALSE,pep->nconv,t-pep->nconv,PetscAbsReal(beta)*norm,&k);
390: (*pep->stopping)(pep,pep->its,pep->max_it,k,pep->nev,&pep->reason,pep->stoppingctx);
392: /* Update l */
393: if (pep->reason != PEP_CONVERGED_ITERATING || breakdown) l = 0;
394: else {
395: l = PetscMax(1,(PetscInt)((nv-k)/2));
396: l = PetscMin(l,t);
397: DSGetTruncateSize(pep->ds,k,t,&l);
398: if (!breakdown) {
399: /* Prepare the Rayleigh quotient for restart */
400: DSTruncate(pep->ds,k+l,PETSC_FALSE);
401: }
402: }
403: nconv = k;
404: if (!ctx->lock && pep->reason == PEP_CONVERGED_ITERATING && !breakdown) { l += k; k = 0; } /* non-locking variant: reset no. of converged pairs */
405: if (l) PetscInfo(pep,"Preparing to restart keeping l=%" PetscInt_FMT " vectors\n",l);
407: /* Update S */
408: DSGetMat(pep->ds,DS_MAT_Q,&MQ);
409: BVMultInPlace(ctx->V,MQ,pep->nconv,k+l);
410: DSRestoreMat(pep->ds,DS_MAT_Q,&MQ);
412: /* Copy last column of S */
413: BVCopyColumn(ctx->V,nv,k+l);
414: BVSetActiveColumns(ctx->V,0,k+l);
415: DSSetDimensions(pep->ds,k+l,PETSC_DEFAULT,PETSC_DEFAULT);
416: DSGetMatAndColumn(pep->ds,DS_MAT_D,0,&D,&vomega);
417: BVSetSignature(ctx->V,vomega);
418: DSRestoreMatAndColumn(pep->ds,DS_MAT_D,0,&D,&vomega);
420: if (breakdown && pep->reason == PEP_CONVERGED_ITERATING) {
421: /* stop if breakdown */
422: PetscInfo(pep,"Breakdown TOAR method (it=%" PetscInt_FMT " norm=%g)\n",pep->its,(double)beta);
423: pep->reason = PEP_DIVERGED_BREAKDOWN;
424: }
425: if (pep->reason != PEP_CONVERGED_ITERATING) l--;
426: BVGetActiveColumns(pep->V,NULL,&nq);
427: if (k+l+deg<=nq) {
428: BVSetActiveColumns(ctx->V,pep->nconv,k+l+1);
429: if (!falselock && ctx->lock) BVTensorCompress(ctx->V,k-pep->nconv);
430: else BVTensorCompress(ctx->V,0);
431: }
432: pep->nconv = k;
433: PEPMonitor(pep,pep->its,nconv,pep->eigr,pep->eigi,pep->errest,nv);
434: }
436: if (pep->nconv>0) {
437: BVSetActiveColumns(ctx->V,0,pep->nconv);
438: BVGetActiveColumns(pep->V,NULL,&nq);
439: BVSetActiveColumns(pep->V,0,nq);
440: if (nq>pep->nconv) {
441: BVTensorCompress(ctx->V,pep->nconv);
442: BVSetActiveColumns(pep->V,0,pep->nconv);
443: }
444: }
445: STGetTransform(pep->st,&flg);
446: if (!flg) PetscTryTypeMethod(pep,backtransform);
447: if (pep->sfactor!=1.0) {
448: for (j=0;j<pep->nconv;j++) {
449: pep->eigr[j] *= pep->sfactor;
450: pep->eigi[j] *= pep->sfactor;
451: }
452: }
453: /* restore original values */
454: if (!flg) {
455: pep->target *= pep->sfactor;
456: STScaleShift(pep->st,pep->sfactor);
457: } else {
458: STScaleShift(pep->st,sinv?1.0/pep->sfactor:pep->sfactor);
459: pep->target = (sinv)?pep->target/pep->sfactor:pep->target*pep->sfactor;
460: }
461: if (pep->sfactor!=1.0) RGPopScale(pep->rg);
463: DSTruncate(pep->ds,pep->nconv,PETSC_TRUE);
464: return 0;
465: }
467: PetscErrorCode PEPSetFromOptions_STOAR(PEP pep,PetscOptionItems *PetscOptionsObject)
468: {
469: PetscBool flg,lock,b,f1,f2,f3;
470: PetscInt i,j,k;
471: PetscReal array[2]={0,0};
472: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
474: PetscOptionsHeadBegin(PetscOptionsObject,"PEP STOAR Options");
476: PetscOptionsBool("-pep_stoar_locking","Choose between locking and non-locking variants","PEPSTOARSetLocking",PETSC_FALSE,&lock,&flg);
477: if (flg) PEPSTOARSetLocking(pep,lock);
479: b = ctx->detect;
480: PetscOptionsBool("-pep_stoar_detect_zeros","Check zeros during factorizations at interval boundaries","PEPSTOARSetDetectZeros",ctx->detect,&b,&flg);
481: if (flg) PEPSTOARSetDetectZeros(pep,b);
483: i = 1;
484: j = k = PETSC_DECIDE;
485: PetscOptionsInt("-pep_stoar_nev","Number of eigenvalues to compute in each subsolve (only for spectrum slicing)","PEPSTOARSetDimensions",20,&i,&f1);
486: PetscOptionsInt("-pep_stoar_ncv","Number of basis vectors in each subsolve (only for spectrum slicing)","PEPSTOARSetDimensions",40,&j,&f2);
487: PetscOptionsInt("-pep_stoar_mpd","Maximum dimension of projected problem in each subsolve (only for spectrum slicing)","PEPSTOARSetDimensions",40,&k,&f3);
488: if (f1 || f2 || f3) PEPSTOARSetDimensions(pep,i,j,k);
490: k = 2;
491: PetscOptionsRealArray("-pep_stoar_linearization","Parameters of the linearization","PEPSTOARSetLinearization",array,&k,&flg);
492: if (flg) PEPSTOARSetLinearization(pep,array[0],array[1]);
494: b = ctx->checket;
495: PetscOptionsBool("-pep_stoar_check_eigenvalue_type","Check eigenvalue type during spectrum slicing","PEPSTOARSetCheckEigenvalueType",ctx->checket,&b,&flg);
496: if (flg) PEPSTOARSetCheckEigenvalueType(pep,b);
498: PetscOptionsHeadEnd();
499: return 0;
500: }
502: static PetscErrorCode PEPSTOARSetLocking_STOAR(PEP pep,PetscBool lock)
503: {
504: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
506: ctx->lock = lock;
507: return 0;
508: }
510: /*@
511: PEPSTOARSetLocking - Choose between locking and non-locking variants of
512: the STOAR method.
514: Logically Collective on pep
516: Input Parameters:
517: + pep - the eigenproblem solver context
518: - lock - true if the locking variant must be selected
520: Options Database Key:
521: . -pep_stoar_locking - Sets the locking flag
523: Notes:
524: The default is to lock converged eigenpairs when the method restarts.
525: This behaviour can be changed so that all directions are kept in the
526: working subspace even if already converged to working accuracy (the
527: non-locking variant).
529: Level: advanced
531: .seealso: PEPSTOARGetLocking()
532: @*/
533: PetscErrorCode PEPSTOARSetLocking(PEP pep,PetscBool lock)
534: {
537: PetscTryMethod(pep,"PEPSTOARSetLocking_C",(PEP,PetscBool),(pep,lock));
538: return 0;
539: }
541: static PetscErrorCode PEPSTOARGetLocking_STOAR(PEP pep,PetscBool *lock)
542: {
543: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
545: *lock = ctx->lock;
546: return 0;
547: }
549: /*@
550: PEPSTOARGetLocking - Gets the locking flag used in the STOAR method.
552: Not Collective
554: Input Parameter:
555: . pep - the eigenproblem solver context
557: Output Parameter:
558: . lock - the locking flag
560: Level: advanced
562: .seealso: PEPSTOARSetLocking()
563: @*/
564: PetscErrorCode PEPSTOARGetLocking(PEP pep,PetscBool *lock)
565: {
568: PetscUseMethod(pep,"PEPSTOARGetLocking_C",(PEP,PetscBool*),(pep,lock));
569: return 0;
570: }
572: static PetscErrorCode PEPSTOARGetInertias_STOAR(PEP pep,PetscInt *n,PetscReal **shifts,PetscInt **inertias)
573: {
574: PetscInt i,numsh;
575: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
576: PEP_SR sr = ctx->sr;
580: switch (pep->state) {
581: case PEP_STATE_INITIAL:
582: break;
583: case PEP_STATE_SETUP:
584: if (n) *n = 2;
585: if (shifts) {
586: PetscMalloc1(2,shifts);
587: (*shifts)[0] = pep->inta;
588: (*shifts)[1] = pep->intb;
589: }
590: if (inertias) {
591: PetscMalloc1(2,inertias);
592: (*inertias)[0] = (sr->dir==1)?sr->inertia0:sr->inertia1;
593: (*inertias)[1] = (sr->dir==1)?sr->inertia1:sr->inertia0;
594: }
595: break;
596: case PEP_STATE_SOLVED:
597: case PEP_STATE_EIGENVECTORS:
598: numsh = ctx->nshifts;
599: if (n) *n = numsh;
600: if (shifts) {
601: PetscMalloc1(numsh,shifts);
602: for (i=0;i<numsh;i++) (*shifts)[i] = ctx->shifts[i];
603: }
604: if (inertias) {
605: PetscMalloc1(numsh,inertias);
606: for (i=0;i<numsh;i++) (*inertias)[i] = ctx->inertias[i];
607: }
608: break;
609: }
610: return 0;
611: }
613: /*@C
614: PEPSTOARGetInertias - Gets the values of the shifts and their
615: corresponding inertias in case of doing spectrum slicing for a
616: computational interval.
618: Not Collective
620: Input Parameter:
621: . pep - the eigenproblem solver context
623: Output Parameters:
624: + n - number of shifts, including the endpoints of the interval
625: . shifts - the values of the shifts used internally in the solver
626: - inertias - the values of the inertia in each shift
628: Notes:
629: If called after PEPSolve(), all shifts used internally by the solver are
630: returned (including both endpoints and any intermediate ones). If called
631: before PEPSolve() and after PEPSetUp() then only the information of the
632: endpoints of subintervals is available.
634: This function is only available for spectrum slicing runs.
636: The returned arrays should be freed by the user. Can pass NULL in any of
637: the two arrays if not required.
639: Fortran Notes:
640: The calling sequence from Fortran is
641: .vb
642: PEPSTOARGetInertias(pep,n,shifts,inertias,ierr)
643: integer n
644: double precision shifts(*)
645: integer inertias(*)
646: .ve
647: The arrays should be at least of length n. The value of n can be determined
648: by an initial call
649: .vb
650: PEPSTOARGetInertias(pep,n,PETSC_NULL_REAL,PETSC_NULL_INTEGER,ierr)
651: .ve
653: Level: advanced
655: .seealso: PEPSetInterval()
656: @*/
657: PetscErrorCode PEPSTOARGetInertias(PEP pep,PetscInt *n,PetscReal **shifts,PetscInt **inertias)
658: {
661: PetscUseMethod(pep,"PEPSTOARGetInertias_C",(PEP,PetscInt*,PetscReal**,PetscInt**),(pep,n,shifts,inertias));
662: return 0;
663: }
665: static PetscErrorCode PEPSTOARSetDetectZeros_STOAR(PEP pep,PetscBool detect)
666: {
667: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
669: ctx->detect = detect;
670: pep->state = PEP_STATE_INITIAL;
671: return 0;
672: }
674: /*@
675: PEPSTOARSetDetectZeros - Sets a flag to enforce detection of
676: zeros during the factorizations throughout the spectrum slicing computation.
678: Logically Collective on pep
680: Input Parameters:
681: + pep - the eigenproblem solver context
682: - detect - check for zeros
684: Options Database Key:
685: . -pep_stoar_detect_zeros - Check for zeros; this takes an optional
686: bool value (0/1/no/yes/true/false)
688: Notes:
689: A zero in the factorization indicates that a shift coincides with an eigenvalue.
691: This flag is turned off by default, and may be necessary in some cases.
692: This feature currently requires an external package for factorizations
693: with support for zero detection, e.g. MUMPS.
695: Level: advanced
697: .seealso: PEPSetInterval()
698: @*/
699: PetscErrorCode PEPSTOARSetDetectZeros(PEP pep,PetscBool detect)
700: {
703: PetscTryMethod(pep,"PEPSTOARSetDetectZeros_C",(PEP,PetscBool),(pep,detect));
704: return 0;
705: }
707: static PetscErrorCode PEPSTOARGetDetectZeros_STOAR(PEP pep,PetscBool *detect)
708: {
709: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
711: *detect = ctx->detect;
712: return 0;
713: }
715: /*@
716: PEPSTOARGetDetectZeros - Gets the flag that enforces zero detection
717: in spectrum slicing.
719: Not Collective
721: Input Parameter:
722: . pep - the eigenproblem solver context
724: Output Parameter:
725: . detect - whether zeros detection is enforced during factorizations
727: Level: advanced
729: .seealso: PEPSTOARSetDetectZeros()
730: @*/
731: PetscErrorCode PEPSTOARGetDetectZeros(PEP pep,PetscBool *detect)
732: {
735: PetscUseMethod(pep,"PEPSTOARGetDetectZeros_C",(PEP,PetscBool*),(pep,detect));
736: return 0;
737: }
739: static PetscErrorCode PEPSTOARSetLinearization_STOAR(PEP pep,PetscReal alpha,PetscReal beta)
740: {
741: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
744: ctx->alpha = alpha;
745: ctx->beta = beta;
746: return 0;
747: }
749: /*@
750: PEPSTOARSetLinearization - Set the coefficients that define
751: the linearization of a quadratic eigenproblem.
753: Logically Collective on pep
755: Input Parameters:
756: + pep - polynomial eigenvalue solver
757: . alpha - first parameter of the linearization
758: - beta - second parameter of the linearization
760: Options Database Key:
761: . -pep_stoar_linearization <alpha,beta> - Sets the coefficients
763: Notes:
764: Cannot pass zero for both alpha and beta. The default values are
765: alpha=1 and beta=0.
767: Level: advanced
769: .seealso: PEPSTOARGetLinearization()
770: @*/
771: PetscErrorCode PEPSTOARSetLinearization(PEP pep,PetscReal alpha,PetscReal beta)
772: {
776: PetscTryMethod(pep,"PEPSTOARSetLinearization_C",(PEP,PetscReal,PetscReal),(pep,alpha,beta));
777: return 0;
778: }
780: static PetscErrorCode PEPSTOARGetLinearization_STOAR(PEP pep,PetscReal *alpha,PetscReal *beta)
781: {
782: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
784: if (alpha) *alpha = ctx->alpha;
785: if (beta) *beta = ctx->beta;
786: return 0;
787: }
789: /*@
790: PEPSTOARGetLinearization - Returns the coefficients that define
791: the linearization of a quadratic eigenproblem.
793: Not Collective
795: Input Parameter:
796: . pep - polynomial eigenvalue solver
798: Output Parameters:
799: + alpha - the first parameter of the linearization
800: - beta - the second parameter of the linearization
802: Level: advanced
804: .seealso: PEPSTOARSetLinearization()
805: @*/
806: PetscErrorCode PEPSTOARGetLinearization(PEP pep,PetscReal *alpha,PetscReal *beta)
807: {
809: PetscUseMethod(pep,"PEPSTOARGetLinearization_C",(PEP,PetscReal*,PetscReal*),(pep,alpha,beta));
810: return 0;
811: }
813: static PetscErrorCode PEPSTOARSetDimensions_STOAR(PEP pep,PetscInt nev,PetscInt ncv,PetscInt mpd)
814: {
815: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
818: ctx->nev = nev;
819: if (ncv == PETSC_DECIDE || ncv == PETSC_DEFAULT) {
820: ctx->ncv = PETSC_DEFAULT;
821: } else {
823: ctx->ncv = ncv;
824: }
825: if (mpd == PETSC_DECIDE || mpd == PETSC_DEFAULT) {
826: ctx->mpd = PETSC_DEFAULT;
827: } else {
829: ctx->mpd = mpd;
830: }
831: pep->state = PEP_STATE_INITIAL;
832: return 0;
833: }
835: /*@
836: PEPSTOARSetDimensions - Sets the dimensions used for each subsolve
837: step in case of doing spectrum slicing for a computational interval.
838: The meaning of the parameters is the same as in PEPSetDimensions().
840: Logically Collective on pep
842: Input Parameters:
843: + pep - the eigenproblem solver context
844: . nev - number of eigenvalues to compute
845: . ncv - the maximum dimension of the subspace to be used by the subsolve
846: - mpd - the maximum dimension allowed for the projected problem
848: Options Database Key:
849: + -eps_stoar_nev <nev> - Sets the number of eigenvalues
850: . -eps_stoar_ncv <ncv> - Sets the dimension of the subspace
851: - -eps_stoar_mpd <mpd> - Sets the maximum projected dimension
853: Level: advanced
855: .seealso: PEPSTOARGetDimensions(), PEPSetDimensions(), PEPSetInterval()
856: @*/
857: PetscErrorCode PEPSTOARSetDimensions(PEP pep,PetscInt nev,PetscInt ncv,PetscInt mpd)
858: {
863: PetscTryMethod(pep,"PEPSTOARSetDimensions_C",(PEP,PetscInt,PetscInt,PetscInt),(pep,nev,ncv,mpd));
864: return 0;
865: }
867: static PetscErrorCode PEPSTOARGetDimensions_STOAR(PEP pep,PetscInt *nev,PetscInt *ncv,PetscInt *mpd)
868: {
869: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
871: if (nev) *nev = ctx->nev;
872: if (ncv) *ncv = ctx->ncv;
873: if (mpd) *mpd = ctx->mpd;
874: return 0;
875: }
877: /*@
878: PEPSTOARGetDimensions - Gets the dimensions used for each subsolve
879: step in case of doing spectrum slicing for a computational interval.
881: Not Collective
883: Input Parameter:
884: . pep - the eigenproblem solver context
886: Output Parameters:
887: + nev - number of eigenvalues to compute
888: . ncv - the maximum dimension of the subspace to be used by the subsolve
889: - mpd - the maximum dimension allowed for the projected problem
891: Level: advanced
893: .seealso: PEPSTOARSetDimensions()
894: @*/
895: PetscErrorCode PEPSTOARGetDimensions(PEP pep,PetscInt *nev,PetscInt *ncv,PetscInt *mpd)
896: {
898: PetscUseMethod(pep,"PEPSTOARGetDimensions_C",(PEP,PetscInt*,PetscInt*,PetscInt*),(pep,nev,ncv,mpd));
899: return 0;
900: }
902: static PetscErrorCode PEPSTOARSetCheckEigenvalueType_STOAR(PEP pep,PetscBool checket)
903: {
904: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
906: ctx->checket = checket;
907: pep->state = PEP_STATE_INITIAL;
908: return 0;
909: }
911: /*@
912: PEPSTOARSetCheckEigenvalueType - Sets a flag to check that all the eigenvalues
913: obtained throughout the spectrum slicing computation have the same definite type.
915: Logically Collective on pep
917: Input Parameters:
918: + pep - the eigenproblem solver context
919: - checket - check eigenvalue type
921: Options Database Key:
922: . -pep_stoar_check_eigenvalue_type - Check eigenvalue type; this takes an optional
923: bool value (0/1/no/yes/true/false)
925: Notes:
926: This option is relevant only for spectrum slicing computations, but it is
927: ignored if the problem type is PEP_HYPERBOLIC.
929: This flag is turned on by default, to guarantee that the computed eigenvalues
930: have the same type (otherwise the computed solution might be wrong). But since
931: the check is computationally quite expensive, the check may be turned off if
932: the user knows for sure that all eigenvalues in the requested interval have
933: the same type.
935: Level: advanced
937: .seealso: PEPSetProblemType(), PEPSetInterval()
938: @*/
939: PetscErrorCode PEPSTOARSetCheckEigenvalueType(PEP pep,PetscBool checket)
940: {
943: PetscTryMethod(pep,"PEPSTOARSetCheckEigenvalueType_C",(PEP,PetscBool),(pep,checket));
944: return 0;
945: }
947: static PetscErrorCode PEPSTOARGetCheckEigenvalueType_STOAR(PEP pep,PetscBool *checket)
948: {
949: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
951: *checket = ctx->checket;
952: return 0;
953: }
955: /*@
956: PEPSTOARGetCheckEigenvalueType - Gets the flag for the eigenvalue type
957: check in spectrum slicing.
959: Not Collective
961: Input Parameter:
962: . pep - the eigenproblem solver context
964: Output Parameter:
965: . checket - whether eigenvalue type must be checked during spectrum slcing
967: Level: advanced
969: .seealso: PEPSTOARSetCheckEigenvalueType()
970: @*/
971: PetscErrorCode PEPSTOARGetCheckEigenvalueType(PEP pep,PetscBool *checket)
972: {
975: PetscUseMethod(pep,"PEPSTOARGetCheckEigenvalueType_C",(PEP,PetscBool*),(pep,checket));
976: return 0;
977: }
979: PetscErrorCode PEPView_STOAR(PEP pep,PetscViewer viewer)
980: {
981: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
982: PetscBool isascii;
984: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&isascii);
985: if (isascii) {
986: PetscViewerASCIIPrintf(viewer," using the %slocking variant\n",ctx->lock?"":"non-");
987: PetscViewerASCIIPrintf(viewer," linearization parameters: alpha=%g beta=%g\n",(double)ctx->alpha,(double)ctx->beta);
988: if (pep->which==PEP_ALL && !ctx->hyperbolic) PetscViewerASCIIPrintf(viewer," checking eigenvalue type: %s\n",ctx->checket?"enabled":"disabled");
989: }
990: return 0;
991: }
993: PetscErrorCode PEPReset_STOAR(PEP pep)
994: {
995: if (pep->which==PEP_ALL) PEPReset_STOAR_QSlice(pep);
996: return 0;
997: }
999: PetscErrorCode PEPDestroy_STOAR(PEP pep)
1000: {
1001: PEP_STOAR *ctx = (PEP_STOAR*)pep->data;
1003: BVDestroy(&ctx->V);
1004: PetscFree(pep->data);
1005: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLocking_C",NULL);
1006: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLocking_C",NULL);
1007: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDetectZeros_C",NULL);
1008: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDetectZeros_C",NULL);
1009: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetInertias_C",NULL);
1010: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDimensions_C",NULL);
1011: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDimensions_C",NULL);
1012: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLinearization_C",NULL);
1013: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLinearization_C",NULL);
1014: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetCheckEigenvalueType_C",NULL);
1015: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetCheckEigenvalueType_C",NULL);
1016: return 0;
1017: }
1019: SLEPC_EXTERN PetscErrorCode PEPCreate_STOAR(PEP pep)
1020: {
1021: PEP_STOAR *ctx;
1023: PetscNew(&ctx);
1024: pep->data = (void*)ctx;
1026: pep->lineariz = PETSC_TRUE;
1027: ctx->lock = PETSC_TRUE;
1028: ctx->nev = 1;
1029: ctx->ncv = PETSC_DEFAULT;
1030: ctx->mpd = PETSC_DEFAULT;
1031: ctx->alpha = 1.0;
1032: ctx->beta = 0.0;
1033: ctx->checket = PETSC_TRUE;
1035: pep->ops->setup = PEPSetUp_STOAR;
1036: pep->ops->setfromoptions = PEPSetFromOptions_STOAR;
1037: pep->ops->destroy = PEPDestroy_STOAR;
1038: pep->ops->view = PEPView_STOAR;
1039: pep->ops->backtransform = PEPBackTransform_Default;
1040: pep->ops->computevectors = PEPComputeVectors_Default;
1041: pep->ops->extractvectors = PEPExtractVectors_TOAR;
1042: pep->ops->reset = PEPReset_STOAR;
1044: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLocking_C",PEPSTOARSetLocking_STOAR);
1045: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLocking_C",PEPSTOARGetLocking_STOAR);
1046: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDetectZeros_C",PEPSTOARSetDetectZeros_STOAR);
1047: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDetectZeros_C",PEPSTOARGetDetectZeros_STOAR);
1048: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetInertias_C",PEPSTOARGetInertias_STOAR);
1049: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetDimensions_C",PEPSTOARGetDimensions_STOAR);
1050: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetDimensions_C",PEPSTOARSetDimensions_STOAR);
1051: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetLinearization_C",PEPSTOARSetLinearization_STOAR);
1052: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetLinearization_C",PEPSTOARGetLinearization_STOAR);
1053: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARSetCheckEigenvalueType_C",PEPSTOARSetCheckEigenvalueType_STOAR);
1054: PetscObjectComposeFunction((PetscObject)pep,"PEPSTOARGetCheckEigenvalueType_C",PEPSTOARGetCheckEigenvalueType_STOAR);
1055: return 0;
1056: }