Actual source code: lsqr.c

petsc-master 2019-10-23
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2: /* lourens.vanzanen@shell.com contributed the standard error estimates of the solution, Jul 25, 2006 */
3: /* Bas van't Hof contributed the preconditioned aspects Feb 10, 2010 */

5: #define SWAP(a,b,c) { c = a; a = b; b = c; }

7:  #include <petsc/private/kspimpl.h>

9: typedef struct {
10:   PetscInt  nwork_n,nwork_m;
11:   Vec       *vwork_m;   /* work vectors of length m, where the system is size m x n */
12:   Vec       *vwork_n;   /* work vectors of length n */
13:   Vec       se;         /* Optional standard error vector */
14:   PetscBool se_flg;     /* flag for -ksp_lsqr_set_standard_error */
15:   PetscBool exact_norm; /* flag for -ksp_lsqr_exact_mat_norm */
16:   PetscReal arnorm;     /* Good estimate of norm((A*inv(Pmat))'*r), where r = A*x - b, used in specific stopping criterion */
17:   PetscReal anorm;      /* Poor estimate of norm(A*inv(Pmat),'fro') used in specific stopping criterion */
18: } KSP_LSQR;

20: static PetscErrorCode  VecSquare(Vec v)
21: {
23:   PetscScalar    *x;
24:   PetscInt       i, n;

27:   VecGetLocalSize(v, &n);
28:   VecGetArray(v, &x);
29:   for (i = 0; i < n; i++) x[i] *= PetscConj(x[i]);
30:   VecRestoreArray(v, &x);
31:   return(0);
32: }

34: static PetscErrorCode KSPSetUp_LSQR(KSP ksp)
35: {
37:   KSP_LSQR       *lsqr = (KSP_LSQR*)ksp->data;
38:   PetscBool      nopreconditioner;

41:   PetscObjectTypeCompare((PetscObject)ksp->pc,PCNONE,&nopreconditioner);

43:   if (lsqr->vwork_m) {
44:     VecDestroyVecs(lsqr->nwork_m,&lsqr->vwork_m);
45:   }

47:   if (lsqr->vwork_n) {
48:     VecDestroyVecs(lsqr->nwork_n,&lsqr->vwork_n);
49:   }

51:   lsqr->nwork_m = 2;
52:   if (nopreconditioner) lsqr->nwork_n = 4;
53:   else lsqr->nwork_n = 5;
54:   KSPCreateVecs(ksp,lsqr->nwork_n,&lsqr->vwork_n,lsqr->nwork_m,&lsqr->vwork_m);

56:   if (lsqr->se_flg && !lsqr->se) {
57:     VecDuplicate(lsqr->vwork_n,&lsqr->se);
58:     VecSet(lsqr->se,PETSC_INFINITY);
59:   } else if (!lsqr->se_flg) {
60:     VecDestroy(&lsqr->se);
61:   }
62:   return(0);
63: }

65: static PetscErrorCode KSPSolve_LSQR(KSP ksp)
66: {
68:   PetscInt       i,size1,size2;
69:   PetscScalar    rho,rhobar,phi,phibar,theta,c,s,tmp,tau;
70:   PetscReal      beta,alpha,rnorm;
71:   Vec            X,B,V,V1,U,U1,TMP,W,W2,Z = NULL;
72:   Mat            Amat,Pmat;
73:   KSP_LSQR       *lsqr = (KSP_LSQR*)ksp->data;
74:   PetscBool      diagonalscale,nopreconditioner;

77:   PCGetDiagonalScale(ksp->pc,&diagonalscale);
78:   if (diagonalscale) SETERRQ1(PetscObjectComm((PetscObject)ksp),PETSC_ERR_SUP,"Krylov method %s does not support diagonal scaling",((PetscObject)ksp)->type_name);

80:   PCGetOperators(ksp->pc,&Amat,&Pmat);
81:   PetscObjectTypeCompare((PetscObject)ksp->pc,PCNONE,&nopreconditioner);

83:   /* vectors of length m, where system size is mxn */
84:   B  = ksp->vec_rhs;
85:   U  = lsqr->vwork_m;
86:   U1 = lsqr->vwork_m;

88:   /* vectors of length n */
89:   X  = ksp->vec_sol;
90:   W  = lsqr->vwork_n;
91:   V  = lsqr->vwork_n;
92:   V1 = lsqr->vwork_n;
93:   W2 = lsqr->vwork_n;
94:   if (!nopreconditioner) Z = lsqr->vwork_n;

96:   /* standard error vector */
97:   if (lsqr->se) {
98:     VecSet(lsqr->se,0.0);
99:   }

101:   /* Compute initial residual, temporarily use work vector u */
102:   if (!ksp->guess_zero) {
103:     KSP_MatMult(ksp,Amat,X,U);       /*   u <- b - Ax     */
104:     VecAYPX(U,-1.0,B);
105:   } else {
106:     VecCopy(B,U);            /*   u <- b (x is 0) */
107:   }

109:   /* Test for nothing to do */
110:   VecNorm(U,NORM_2,&rnorm);
111:   KSPCheckNorm(ksp,rnorm);
112:   PetscObjectSAWsTakeAccess((PetscObject)ksp);
113:   ksp->its   = 0;
114:   ksp->rnorm = rnorm;
115:   PetscObjectSAWsGrantAccess((PetscObject)ksp);
116:   KSPLogResidualHistory(ksp,rnorm);
117:   KSPMonitor(ksp,0,rnorm);
118:   (*ksp->converged)(ksp,0,rnorm,&ksp->reason,ksp->cnvP);
119:   if (ksp->reason) return(0);

121:   beta = rnorm;
122:   VecScale(U,1.0/beta);
123:   KSP_MatMultTranspose(ksp,Amat,U,V);
124:   if (nopreconditioner) {
125:     VecNorm(V,NORM_2,&alpha);
126:     KSPCheckNorm(ksp,rnorm);
127:   } else {
128:     /* this is an application of the preconditioner for the normal equations; not the operator, see the manual page */
129:     PCApply(ksp->pc,V,Z);
130:     VecDotRealPart(V,Z,&alpha);
131:     if (alpha <= 0.0) {
132:       ksp->reason = KSP_DIVERGED_BREAKDOWN;
133:       return(0);
134:     }
135:     alpha = PetscSqrtReal(alpha);
136:     VecScale(Z,1.0/alpha);
137:   }
138:   VecScale(V,1.0/alpha);

140:   if (nopreconditioner) {
141:     VecCopy(V,W);
142:   } else {
143:     VecCopy(Z,W);
144:   }

146:   if (lsqr->exact_norm) {
147:     MatNorm(Amat,NORM_FROBENIUS,&lsqr->anorm);
148:   } else lsqr->anorm = 0.0;

150:   lsqr->arnorm = alpha * beta;
151:   phibar       = beta;
152:   rhobar       = alpha;
153:   i            = 0;
154:   do {
155:     if (nopreconditioner) {
156:       KSP_MatMult(ksp,Amat,V,U1);
157:     } else {
158:       KSP_MatMult(ksp,Amat,Z,U1);
159:     }
160:     VecAXPY(U1,-alpha,U);
161:     VecNorm(U1,NORM_2,&beta);
162:     KSPCheckNorm(ksp,beta);
163:     if (beta > 0.0) {
164:       VecScale(U1,1.0/beta); /* beta*U1 = Amat*V - alpha*U */
165:       if (!lsqr->exact_norm) {
166:         lsqr->anorm = PetscSqrtScalar(PetscSqr(lsqr->anorm) + PetscSqr(alpha) + PetscSqr(beta));
167:       }
168:     }

170:     KSP_MatMultTranspose(ksp,Amat,U1,V1);
171:     VecAXPY(V1,-beta,V);
172:     if (nopreconditioner) {
173:       VecNorm(V1,NORM_2,&alpha);
174:       KSPCheckNorm(ksp,alpha);
175:     } else {
176:       PCApply(ksp->pc,V1,Z);
177:       VecDotRealPart(V1,Z,&alpha);
178:       if (alpha <= 0.0) {
179:         ksp->reason = KSP_DIVERGED_BREAKDOWN;
180:         break;
181:       }
182:       alpha = PetscSqrtReal(alpha);
183:       VecScale(Z,1.0/alpha);
184:     }
185:     VecScale(V1,1.0/alpha); /* alpha*V1 = Amat^T*U1 - beta*V */
186:     rho    = PetscSqrtScalar(rhobar*rhobar + beta*beta);
187:     c      = rhobar / rho;
188:     s      = beta / rho;
189:     theta  = s * alpha;
190:     rhobar = -c * alpha;
191:     phi    = c * phibar;
192:     phibar = s * phibar;
193:     tau    = s * phi;

195:     VecAXPY(X,phi/rho,W);  /*    x <- x + (phi/rho) w   */

197:     if (lsqr->se) {
198:       VecCopy(W,W2);
199:       VecSquare(W2);
200:       VecScale(W2,1.0/(rho*rho));
201:       VecAXPY(lsqr->se, 1.0, W2); /* lsqr->se <- lsqr->se + (w^2/rho^2) */
202:     }
203:     if (nopreconditioner) {
204:       VecAYPX(W,-theta/rho,V1);  /* w <- v - (theta/rho) w */
205:     } else {
206:       VecAYPX(W,-theta/rho,Z);   /* w <- z - (theta/rho) w */
207:     }

209:     lsqr->arnorm = alpha*PetscAbsScalar(tau);
210:     rnorm        = PetscRealPart(phibar);

212:     PetscObjectSAWsTakeAccess((PetscObject)ksp);
213:     ksp->its++;
214:     ksp->rnorm = rnorm;
215:     PetscObjectSAWsGrantAccess((PetscObject)ksp);
216:     KSPLogResidualHistory(ksp,rnorm);
217:     KSPMonitor(ksp,i+1,rnorm);
218:     (*ksp->converged)(ksp,i+1,rnorm,&ksp->reason,ksp->cnvP);
219:     if (ksp->reason) break;
220:     SWAP(U1,U,TMP);
221:     SWAP(V1,V,TMP);

223:     i++;
224:   } while (i<ksp->max_it);
225:   if (i >= ksp->max_it && !ksp->reason) ksp->reason = KSP_DIVERGED_ITS;

227:   /* Finish off the standard error estimates */
228:   if (lsqr->se) {
229:     tmp  = 1.0;
230:     MatGetSize(Amat,&size1,&size2);
231:     if (size1 > size2) tmp = size1 - size2;
232:     tmp  = rnorm / PetscSqrtScalar(tmp);
233:     VecSqrtAbs(lsqr->se);
234:     VecScale(lsqr->se,tmp);
235:   }
236:   return(0);
237: }

240: PetscErrorCode KSPDestroy_LSQR(KSP ksp)
241: {
242:   KSP_LSQR       *lsqr = (KSP_LSQR*)ksp->data;

246:   /* Free work vectors */
247:   if (lsqr->vwork_n) {
248:     VecDestroyVecs(lsqr->nwork_n,&lsqr->vwork_n);
249:   }
250:   if (lsqr->vwork_m) {
251:     VecDestroyVecs(lsqr->nwork_m,&lsqr->vwork_m);
252:   }
253:   VecDestroy(&lsqr->se);
254:   PetscFree(ksp->data);
255:   return(0);
256: }

258: /*@
259:    KSPLSQRSetComputeStandardErrorVec - Compute vector of standard error estimates during KSPSolve_LSQR().

261:    Not Collective

263:    Input Parameters:
264: +  ksp   - iterative context
265: -  flg   - compute the vector of standard estimates or not

267:    Developer notes:
268:    Vaclav: I'm not sure whether this vector is useful for anything.

270:    Level: intermediate

272: .seealso: KSPSolve(), KSPLSQR, KSPLSQRGetStandardErrorVec()
273: @*/
274: PetscErrorCode  KSPLSQRSetComputeStandardErrorVec(KSP ksp, PetscBool flg)
275: {
276:   KSP_LSQR       *lsqr = (KSP_LSQR*)ksp->data;

279:   lsqr->se_flg = flg;
280:   return(0);
281: }

283: /*@
284:    KSPLSQRSetExactMatNorm - Compute exact matrix norm instead of iteratively refined estimate.

286:    Not Collective

288:    Input Parameters:
289: +  ksp   - iterative context
290: -  flg   - compute exact matrix norm or not

292:    Notes:
293:    By default, flg=PETSC_FALSE. This is usually preferred to avoid possibly expensive computation of the norm.
294:    For flg=PETSC_TRUE, we call MatNorm(Amat,NORM_FROBENIUS,&lsqr->anorm) which will work only for some types of explicitly assembled matrices.
295:    This can affect convergence rate as KSPLSQRConvergedDefault() assumes different value of ||A|| used in normal equation stopping criterion.

297:    Level: intermediate

299: .seealso: KSPSolve(), KSPLSQR, KSPLSQRGetNorms(), KSPLSQRConvergedDefault()
300: @*/
301: PetscErrorCode  KSPLSQRSetExactMatNorm(KSP ksp, PetscBool flg)
302: {
303:   KSP_LSQR       *lsqr = (KSP_LSQR*)ksp->data;

306:   lsqr->exact_norm = flg;
307:   return(0);
308: }

310: /*@
311:    KSPLSQRGetStandardErrorVec - Get vector of standard error estimates.
312:    Only available if -ksp_lsqr_set_standard_error was set to true
313:    or KSPLSQRSetComputeStandardErrorVec(ksp, PETSC_TRUE) was called.
314:    Otherwise returns NULL.

316:    Not Collective

318:    Input Parameters:
319: .  ksp   - iterative context

321:    Output Parameters:
322: .  se - vector of standard estimates

324:    Options Database Keys:
325: .   -ksp_lsqr_set_standard_error  - set standard error estimates of solution

327:    Developer notes:
328:    Vaclav: I'm not sure whether this vector is useful for anything.

330:    Level: intermediate

332: .seealso: KSPSolve(), KSPLSQR, KSPLSQRSetComputeStandardErrorVec()
333: @*/
334: PetscErrorCode  KSPLSQRGetStandardErrorVec(KSP ksp,Vec *se)
335: {
336:   KSP_LSQR *lsqr = (KSP_LSQR*)ksp->data;

339:   *se = lsqr->se;
340:   return(0);
341: }

343: /*@
344:    KSPLSQRGetNorms - Get norm estimates that LSQR computes internally during KSPSolve().

346:    Not Collective

348:    Input Parameters:
349: .  ksp   - iterative context

351:    Output Parameters:
352: +  arnorm - good estimate of norm((A*inv(Pmat))'*r), where r = A*x - b, used in specific stopping criterion
353: -  anorm - poor estimate of norm(A*inv(Pmat),'fro') used in specific stopping criterion

355:    Notes:
356:    Output parameters are meaningful only after KSPSolve().
357:    These are the same quantities as normar and norma in MATLAB's lsqr(), whose output lsvec is a vector of normar / norma for all iterations.
358:    If -ksp_lsqr_exact_mat_norm is set or KSPLSQRSetExactMatNorm(ksp, PETSC_TRUE) called, then anorm is exact Frobenius norm.

360:    Level: intermediate

362: .seealso: KSPSolve(), KSPLSQR, KSPLSQRSetExactMatNorm()
363: @*/
364: PetscErrorCode  KSPLSQRGetNorms(KSP ksp,PetscReal *arnorm, PetscReal *anorm)
365: {
366:   KSP_LSQR       *lsqr = (KSP_LSQR*)ksp->data;

369:   if (arnorm)   *arnorm = lsqr->arnorm;
370:   if (anorm)    *anorm = lsqr->anorm;
371:   return(0);
372: }

374: /*@C
375:    KSPLSQRMonitorDefault - Print the residual norm at each iteration of the LSQR method,
376:    norm of the residual of the normal equations A'*A x = A' b, and estimate of matrix norm ||A||.

378:    Collective on ksp

380:    Input Parameters:
381: +  ksp   - iterative context
382: .  n     - iteration number
383: .  rnorm - 2-norm (preconditioned) residual value (may be estimated).
384: -  dummy - viewer and format context

386:    Level: intermediate

388: .seealso: KSPLSQR, KSPMonitorSet(), KSPMonitorTrueResidualNorm(), KSPMonitorLGResidualNormCreate(), KSPMonitorDefault()
389: @*/
390: PetscErrorCode  KSPLSQRMonitorDefault(KSP ksp,PetscInt n,PetscReal rnorm,PetscViewerAndFormat *dummy)
391: {
393:   PetscViewer    viewer = dummy->viewer;
394:   KSP_LSQR       *lsqr  = (KSP_LSQR*)ksp->data;

397:   PetscViewerPushFormat(viewer,dummy->format);
399:   if (!n && ((PetscObject)ksp)->prefix) {
400:     PetscViewerASCIIPrintf(viewer,"  Residual norm, norm of normal equations, and matrix norm for %s solve.\n",((PetscObject)ksp)->prefix);
401:   }

403:   if (!n) {
404:     PetscViewerASCIIPrintf(viewer,"%3D KSP resid norm %14.12e\n",n,(double)rnorm);
405:   } else {
406:     PetscViewerASCIIPrintf(viewer,"%3D KSP resid norm %14.12e normal eq resid norm %14.12e matrix norm %14.12e\n",n,(double)rnorm,(double)lsqr->arnorm,(double)lsqr->anorm);
407:   }

409:   PetscViewerASCIISubtractTab(viewer,((PetscObject)ksp)->tablevel);
410:   PetscViewerPopFormat(viewer);
411:   return(0);
412: }

414: PetscErrorCode KSPSetFromOptions_LSQR(PetscOptionItems *PetscOptionsObject,KSP ksp)
415: {
417:   KSP_LSQR       *lsqr = (KSP_LSQR*)ksp->data;

420:   PetscOptionsHead(PetscOptionsObject,"KSP LSQR Options");
421:   PetscOptionsBool("-ksp_lsqr_compute_standard_error","Set Standard Error Estimates of Solution","KSPLSQRSetComputeStandardErrorVec",lsqr->se_flg,&lsqr->se_flg,NULL);
422:   PetscOptionsBool("-ksp_lsqr_exact_mat_norm","Compute exact matrix norm instead of iteratively refined estimate","KSPLSQRSetExactMatNorm",lsqr->exact_norm,&lsqr->exact_norm,NULL);
423:   KSPMonitorSetFromOptions(ksp,"-ksp_lsqr_monitor","Monitor residual norm and norm of residual of normal equations","KSPMonitorSet",KSPLSQRMonitorDefault);
424:   PetscOptionsTail();
425:   return(0);
426: }

428: PetscErrorCode KSPView_LSQR(KSP ksp,PetscViewer viewer)
429: {
430:   KSP_LSQR       *lsqr = (KSP_LSQR*)ksp->data;
432:   PetscBool      iascii;

435:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
436:   if (iascii) {
437:     if (lsqr->se) {
438:       PetscReal rnorm;
439:       VecNorm(lsqr->se,NORM_2,&rnorm);
440:       PetscViewerASCIIPrintf(viewer,"  norm of standard error %g, iterations %d\n",(double)rnorm,ksp->its);
441:     } else {
442:       PetscViewerASCIIPrintf(viewer,"  standard error not computed\n");
443:     }
444:     if (lsqr->exact_norm) {
445:       PetscViewerASCIIPrintf(viewer,"  using exact matrix norm\n");
446:     } else {
447:       PetscViewerASCIIPrintf(viewer,"  using inexact matrix norm\n");
448:     }
449:   }
450:   return(0);
451: }

453: /*@C
454:    KSPLSQRConvergedDefault - Determines convergence of the LSQR Krylov method.

456:    Collective on ksp

458:    Input Parameters:
459: +  ksp   - iterative context
460: .  n     - iteration number
461: .  rnorm - 2-norm residual value (may be estimated)
462: -  ctx - convergence context which must be created by KSPConvergedDefaultCreate()

464:    reason is set to:
465: +   positive - if the iteration has converged;
466: .   negative - if residual norm exceeds divergence threshold;
467: -   0 - otherwise.

469:    Notes:
470:    KSPConvergedDefault() is called first to check for convergence in A*x=b.
471:    If that does not determine convergence then checks convergence for the least squares problem, i.e. in min{|b-A*x|}.
472:    Possible convergence for the least squares problem (which is based on the residual of the normal equations) are KSP_CONVERGED_RTOL_NORMAL norm and KSP_CONVERGED_ATOL_NORMAL.
473:    KSP_CONVERGED_RTOL_NORMAL is returned if ||A'*r|| < rtol * ||A|| * ||r||.
474:    Matrix norm ||A|| is iteratively refined estimate, see KSPLSQRGetNorms().
475:    This criterion is now largely compatible with that in MATLAB lsqr().

477:    Level: intermediate

479: .seealso: KSPLSQR, KSPSetConvergenceTest(), KSPSetTolerances(), KSPConvergedSkip(), KSPConvergedReason, KSPGetConvergedReason(),
480:           KSPConvergedDefaultSetUIRNorm(), KSPConvergedDefaultSetUMIRNorm(), KSPConvergedDefaultCreate(), KSPConvergedDefaultDestroy(), KSPConvergedDefault(), KSPLSQRGetNorms(), KSPLSQRSetExactMatNorm()
481: @*/
482: PetscErrorCode  KSPLSQRConvergedDefault(KSP ksp,PetscInt n,PetscReal rnorm,KSPConvergedReason *reason,void *ctx)
483: {
485:   KSP_LSQR       *lsqr = (KSP_LSQR*)ksp->data;

488:   /* check for convergence in A*x=b */
489:   KSPConvergedDefault(ksp,n,rnorm,reason,ctx);
490:   if (!n || *reason) return(0);

492:   /* check for convergence in min{|b-A*x|} */
493:   if (lsqr->arnorm < ksp->abstol) {
494:     PetscInfo3(ksp,"LSQR solver has converged. Normal equation residual %14.12e is less than absolute tolerance %14.12e at iteration %D\n",(double)lsqr->arnorm,(double)ksp->abstol,n);
495:     *reason = KSP_CONVERGED_ATOL_NORMAL;
496:   } else if (lsqr->arnorm < ksp->rtol * lsqr->anorm * rnorm) {
497:     PetscInfo6(ksp,"LSQR solver has converged. Normal equation residual %14.12e is less than rel. tol. %14.12e times %s Frobenius norm of matrix %14.12e times residual %14.12e at iteration %D\n",(double)lsqr->arnorm,(double)ksp->rtol,lsqr->exact_norm?"exact":"approx.",(double)lsqr->anorm,(double)rnorm,n);
498:     *reason = KSP_CONVERGED_RTOL_NORMAL;
499:   }
500:   return(0);
501: }

503: /*MC
504:      KSPLSQR - This implements LSQR

506:    Options Database Keys:
507: +   -ksp_lsqr_set_standard_error  - set standard error estimates of solution, see KSPLSQRSetComputeStandardErrorVec() and KSPLSQRGetStandardErrorVec()
508: .   -ksp_lsqr_exact_mat_norm - compute exact matrix norm instead of iteratively refined estimate, see KSPLSQRSetExactMatNorm()
509: -   -ksp_lsqr_monitor - monitor residual norm, norm of residual of normal equations A'*A x = A' b, and estimate of matrix norm ||A||

511:    Level: beginner

513:    Notes:
514:      Supports non-square (rectangular) matrices.

516:      This varient, when applied with no preconditioning is identical to the original algorithm in exact arithematic; however, in practice, with no preconditioning
517:      due to inexact arithematic, it can converge differently. Hence when no preconditioner is used (PCType PCNONE) it automatically reverts to the original algorithm.

519:      With the PETSc built-in preconditioners, such as ICC, one should call KSPSetOperators(ksp,A,A'*A)) since the preconditioner needs to work
520:      for the normal equations A'*A.

522:      Supports only left preconditioning.

524:      For least squares problems wit nonzero residual A*x - b, there are additional convergence tests for the residual of the normal equations, A'*(b - Ax), see KSPLSQRConvergedDefault().

526:    References:
527: .  1. - The original unpreconditioned algorithm can be found in Paige and Saunders, ACM Transactions on Mathematical Software, Vol 8, 1982.

529:      In exact arithmetic the LSQR method (with no preconditioning) is identical to the KSPCG algorithm applied to the normal equations.
530:      The preconditioned variant was implemented by Bas van't Hof and is essentially a left preconditioning for the Normal Equations. It appears the implementation with preconditioner
531:      track the true norm of the residual and uses that in the convergence test.

533:    Developer Notes:
534:     How is this related to the KSPCGNE implementation? One difference is that KSPCGNE applies
535:             the preconditioner transpose times the preconditioner,  so one does not need to pass A'*A as the third argument to KSPSetOperators().

539: .seealso:  KSPCreate(), KSPSetType(), KSPType (for list of available types), KSP, KSPSolve(), KSPLSQRConvergedDefault(), KSPLSQRSetComputeStandardErrorVec(), KSPLSQRGetStandardErrorVec(), KSPLSQRSetExactMatNorm()

541: M*/
542: PETSC_EXTERN PetscErrorCode KSPCreate_LSQR(KSP ksp)
543: {
544:   KSP_LSQR       *lsqr;
545:   void           *ctx;

549:   PetscNewLog(ksp,&lsqr);
550:   lsqr->se     = NULL;
551:   lsqr->se_flg = PETSC_FALSE;
552:   lsqr->exact_norm = PETSC_FALSE;
553:   lsqr->anorm  = -1.0;
554:   lsqr->arnorm = -1.0;
555:   ksp->data    = (void*)lsqr;
556:   KSPSetSupportedNorm(ksp,KSP_NORM_UNPRECONDITIONED,PC_LEFT,3);

558:   ksp->ops->setup          = KSPSetUp_LSQR;
559:   ksp->ops->solve          = KSPSolve_LSQR;
560:   ksp->ops->destroy        = KSPDestroy_LSQR;
561:   ksp->ops->setfromoptions = KSPSetFromOptions_LSQR;
562:   ksp->ops->view           = KSPView_LSQR;

564:   KSPConvergedDefaultCreate(&ctx);
565:   KSPSetConvergenceTest(ksp,KSPLSQRConvergedDefault,ctx,KSPConvergedDefaultDestroy);
566:   return(0);
567: }