Actual source code: cheby.c

petsc-3.7.3 2016-07-24
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  2: #include <petsc/private/kspimpl.h>                    /*I "petscksp.h" I*/
  3: #include <../src/ksp/ksp/impls/cheby/chebyshevimpl.h>

  7: static PetscErrorCode KSPReset_Chebyshev(KSP ksp)
  8: {
  9:   KSP_Chebyshev  *cheb = (KSP_Chebyshev*)ksp->data;

 13:   KSPReset(cheb->kspest);
 14:   return(0);
 15: }

 19: static PetscErrorCode KSPSetUp_Chebyshev(KSP ksp)
 20: {
 21:   KSP_Chebyshev  *cheb = (KSP_Chebyshev*)ksp->data;

 25:   KSPSetWorkVecs(ksp,3);
 26:   if ((cheb->emin == 0. || cheb->emax == 0.) && !cheb->kspest) { /* We need to estimate eigenvalues */
 27:     KSPChebyshevEstEigSet(ksp,PETSC_DECIDE,PETSC_DECIDE,PETSC_DECIDE,PETSC_DECIDE);
 28:   }
 29:   return(0);
 30: }

 34: static PetscErrorCode KSPChebyshevSetEigenvalues_Chebyshev(KSP ksp,PetscReal emax,PetscReal emin)
 35: {
 36:   KSP_Chebyshev  *chebyshevP = (KSP_Chebyshev*)ksp->data;

 40:   if (emax <= emin) SETERRQ2(PetscObjectComm((PetscObject)ksp),PETSC_ERR_ARG_INCOMP,"Maximum eigenvalue must be larger than minimum: max %g min %g",(double)emax,(double)emin);
 41:   if (emax*emin <= 0.0) SETERRQ2(PetscObjectComm((PetscObject)ksp),PETSC_ERR_ARG_INCOMP,"Both eigenvalues must be of the same sign: max %g min %g",(double)emax,(double)emin);
 42:   chebyshevP->emax = emax;
 43:   chebyshevP->emin = emin;

 45:   KSPChebyshevEstEigSet(ksp,0.,0.,0.,0.); /* Destroy any estimation setup */
 46:   return(0);
 47: }

 51: static PetscErrorCode KSPChebyshevEstEigSet_Chebyshev(KSP ksp,PetscReal a,PetscReal b,PetscReal c,PetscReal d)
 52: {
 53:   KSP_Chebyshev  *cheb = (KSP_Chebyshev*)ksp->data;

 57:   if (a != 0.0 || b != 0.0 || c != 0.0 || d != 0.0) {
 58:     if (!cheb->kspest) { /* should this block of code be moved to KSPSetUp_Chebyshev()? */
 59:       KSPCreate(PetscObjectComm((PetscObject)ksp),&cheb->kspest);
 60:       PetscObjectIncrementTabLevel((PetscObject)cheb->kspest,(PetscObject)ksp,1);
 61:       KSPSetOptionsPrefix(cheb->kspest,((PetscObject)ksp)->prefix);
 62:       KSPAppendOptionsPrefix(cheb->kspest,"esteig_");
 63:       KSPSetSkipPCSetFromOptions(cheb->kspest,PETSC_TRUE);

 65:       KSPSetPC(cheb->kspest,ksp->pc);

 67:       KSPSetComputeEigenvalues(cheb->kspest,PETSC_TRUE);

 69:       /* We cannot turn off convergence testing because GMRES will break down if you attempt to keep iterating after a zero norm is obtained */
 70:       KSPSetTolerances(cheb->kspest,1.e-12,PETSC_DEFAULT,PETSC_DEFAULT,cheb->eststeps);
 71:     }
 72:     if (a >= 0) cheb->tform[0] = a;
 73:     if (b >= 0) cheb->tform[1] = b;
 74:     if (c >= 0) cheb->tform[2] = c;
 75:     if (d >= 0) cheb->tform[3] = d;
 76:     cheb->amatid    = 0;
 77:     cheb->pmatid    = 0;
 78:     cheb->amatstate = -1;
 79:     cheb->pmatstate = -1;
 80:   } else {
 81:     KSPDestroy(&cheb->kspest);
 82:   }
 83:   return(0);
 84: }

 88: static PetscErrorCode KSPChebyshevEstEigSetRandom_Chebyshev(KSP ksp,PetscRandom random)
 89: {
 90:   KSP_Chebyshev  *cheb = (KSP_Chebyshev*)ksp->data;

 94:   if (random) {PetscObjectReference((PetscObject)random);}
 95:   PetscRandomDestroy(&cheb->random);
 96:   cheb->random = random;
 97:   return(0);
 98: }

102: static PetscErrorCode KSPChebyshevEstEigSetUseRandom_Chebyshev(KSP ksp,PetscBool use)
103: {
104:   KSP_Chebyshev  *cheb = (KSP_Chebyshev*)ksp->data;

107:   cheb->userandom = use;
108:   return(0);
109: }

113: /*@
114:    KSPChebyshevSetEigenvalues - Sets estimates for the extreme eigenvalues
115:    of the preconditioned problem.

117:    Logically Collective on KSP

119:    Input Parameters:
120: +  ksp - the Krylov space context
121: -  emax, emin - the eigenvalue estimates

123:   Options Database:
124: .  -ksp_chebyshev_eigenvalues emin,emax

126:    Note: Call KSPChebyshevEstEigSet() or use the option -ksp_chebyshev_esteig a,b,c,d to have the KSP
127:          estimate the eigenvalues and use these estimated values automatically

129:    Level: intermediate

131: .keywords: KSP, Chebyshev, set, eigenvalues
132: @*/
133: PetscErrorCode  KSPChebyshevSetEigenvalues(KSP ksp,PetscReal emax,PetscReal emin)
134: {

141:   PetscTryMethod(ksp,"KSPChebyshevSetEigenvalues_C",(KSP,PetscReal,PetscReal),(ksp,emax,emin));
142:   return(0);
143: }

147: /*@
148:    KSPChebyshevEstEigSet - Automatically estimate the eigenvalues to use for Chebyshev

150:    Logically Collective on KSP

152:    Input Parameters:
153: +  ksp - the Krylov space context
154: .  a - multiple of min eigenvalue estimate to use for min Chebyshev bound (or PETSC_DECIDE)
155: .  b - multiple of max eigenvalue estimate to use for min Chebyshev bound (or PETSC_DECIDE)
156: .  c - multiple of min eigenvalue estimate to use for max Chebyshev bound (or PETSC_DECIDE)
157: -  d - multiple of max eigenvalue estimate to use for max Chebyshev bound (or PETSC_DECIDE)

159:   Options Database:
160: .  -ksp_chebyshev_esteig a,b,c,d

162:    Notes:
163:    The Chebyshev bounds are set using
164: .vb
165:    minbound = a*minest + b*maxest
166:    maxbound = c*minest + d*maxest
167: .ve
168:    The default configuration targets the upper part of the spectrum for use as a multigrid smoother, so only the maximum eigenvalue estimate is used.
169:    The minimum eigenvalue estimate obtained by Krylov iteration is typically not accurate until the method has converged.

171:    If 0.0 is passed for all transform arguments (a,b,c,d), eigenvalue estimation is disabled.

173:    The default transform is (0,0.1; 0,1.1) which targets the "upper" part of the spectrum, as desirable for use with multigrid.

175:    The eigenvalues are estimated using the Lanczo (KSPCG) or Arnoldi (KSPGMRES) process using a random right hand side vector.

177:    Level: intermediate

179: .keywords: KSP, Chebyshev, set, eigenvalues, PCMG
180: @*/
181: PetscErrorCode KSPChebyshevEstEigSet(KSP ksp,PetscReal a,PetscReal b,PetscReal c,PetscReal d)
182: {

191:   PetscTryMethod(ksp,"KSPChebyshevEstEigSet_C",(KSP,PetscReal,PetscReal,PetscReal,PetscReal),(ksp,a,b,c,d));
192:   return(0);
193: }

197: /*@
198:    KSPChebyshevEstEigSetUseRandom - use a random right hand side in order to do the estimate instead of the given right hand side

200:    Logically Collective

202:    Input Arguments:
203: +  ksp - linear solver context
204: -  use - PETSC_TRUE to use random

206:    Options Database:
207: +  -ksp_chebyshev_esteig_random <true,false>

209:   Notes: This alledgely works better for multigrid smoothers

211:   Use KSPChebyshevEstEigSetRandom() to provide the random number generator to be used. Otherwise it creates a default

213:   Level: intermediate

215: .seealso: KSPChebyshevEstEigSet(), PetscRandomCreate(), KSPChebyshevEstEigSetRandom()
216: @*/
217: PetscErrorCode KSPChebyshevEstEigSetUseRandom(KSP ksp,PetscBool use)
218: {

223:   PetscTryMethod(ksp,"KSPChebyshevEstEigSetUseRandom_C",(KSP,PetscBool),(ksp,use));
224:   return(0);
225: }

229: /*@
230:    KSPChebyshevEstEigSetRandom - set random context for estimating eigenvalues

232:    Logically Collective

234:    Input Arguments:
235: +  ksp - linear solver context
236: -  random - random number context or NULL to use default

238:   Level: intermediate

240: .seealso: KSPChebyshevEstEigSet(), PetscRandomCreate()
241: @*/
242: PetscErrorCode KSPChebyshevEstEigSetRandom(KSP ksp,PetscRandom random)
243: {

249:   PetscTryMethod(ksp,"KSPChebyshevEstEigSetRandom_C",(KSP,PetscRandom),(ksp,random));
250:   return(0);
251: }

255: /*@
256:   KSPChebyshevEstEigGetKSP - Get the Krylov method context used to estimate eigenvalues for the Chebyshev method.  If
257:   a Krylov method is not being used for this purpose, NULL is returned.  The reference count of the returned KSP is
258:   not incremented: it should not be destroyed by the user.

260:   Input Parameters:
261: . ksp - the Krylov space context

263:   Output Parameters:
264: . kspest the eigenvalue estimation Krylov space context

266:   Level: intermediate

268: .seealso: KSPChebyshevEstEigSet()
269: @*/
270: PetscErrorCode KSPChebyshevEstEigGetKSP(KSP ksp, KSP *kspest)
271: {

276:   *kspest = NULL;
277:   PetscTryMethod(ksp,"KSPChebyshevEstEigGetKSP_C",(KSP,KSP*),(ksp,kspest));
278:   return(0);
279: }

283: static PetscErrorCode KSPChebyshevEstEigGetKSP_Chebyshev(KSP ksp, KSP *kspest)
284: {
285:   KSP_Chebyshev *cheb = (KSP_Chebyshev*)ksp->data;

288:   *kspest = cheb->kspest;
289:   return(0);
290: }

294: static PetscErrorCode KSPSetFromOptions_Chebyshev(PetscOptionItems *PetscOptionsObject,KSP ksp)
295: {
296:   KSP_Chebyshev  *cheb = (KSP_Chebyshev*)ksp->data;
298:   PetscInt       neigarg = 2, nestarg = 4;
299:   PetscReal      eminmax[2] = {0., 0.};
300:   PetscReal      tform[4] = {PETSC_DECIDE,PETSC_DECIDE,PETSC_DECIDE,PETSC_DECIDE};
301:   PetscBool      flgeig, flgest;

304:   PetscOptionsHead(PetscOptionsObject,"KSP Chebyshev Options");
305:   PetscOptionsInt("-ksp_chebyshev_esteig_steps","Number of est steps in Chebyshev","",cheb->eststeps,&cheb->eststeps,NULL);
306:   PetscOptionsRealArray("-ksp_chebyshev_eigenvalues","extreme eigenvalues","KSPChebyshevSetEigenvalues",eminmax,&neigarg,&flgeig);
307:   if (flgeig) {
308:     if (neigarg != 2) SETERRQ(PetscObjectComm((PetscObject)ksp),PETSC_ERR_ARG_INCOMP,"-ksp_chebyshev_eigenvalues: must specify 2 parameters, min and max eigenvalues");
309:     KSPChebyshevSetEigenvalues(ksp, eminmax[1], eminmax[0]);
310:   }
311:   PetscOptionsRealArray("-ksp_chebyshev_esteig","estimate eigenvalues using a Krylov method, then use this transform for Chebyshev eigenvalue bounds","KSPChebyshevEstEigSet",tform,&nestarg,&flgest);
312:   if (flgest) {
313:     switch (nestarg) {
314:     case 0:
315:       KSPChebyshevEstEigSet(ksp,PETSC_DECIDE,PETSC_DECIDE,PETSC_DECIDE,PETSC_DECIDE);
316:       break;
317:     case 2:                     /* Base everything on the max eigenvalues */
318:       KSPChebyshevEstEigSet(ksp,PETSC_DECIDE,tform[0],PETSC_DECIDE,tform[1]);
319:       break;
320:     case 4:                     /* Use the full 2x2 linear transformation */
321:       KSPChebyshevEstEigSet(ksp,tform[0],tform[1],tform[2],tform[3]);
322:       break;
323:     default: SETERRQ(PetscObjectComm((PetscObject)ksp),PETSC_ERR_ARG_INCOMP,"Must specify either 0, 2, or 4 parameters for eigenvalue estimation");
324:     }
325:   }

327:   /* We need to estimate eigenvalues; need to set this here so that KSPSetFromOptions() is called on the estimator */
328:   if ((cheb->emin == 0. || cheb->emax == 0.) && !cheb->kspest) {
329:    KSPChebyshevEstEigSet(ksp,PETSC_DECIDE,PETSC_DECIDE,PETSC_DECIDE,PETSC_DECIDE);
330:   }

332:   if (cheb->kspest) {
333:     PetscOptionsBool("-ksp_chebyshev_esteig_random","Use random right hand side for estimate","KSPChebyshevEstEigSetUseRandom",cheb->userandom,&cheb->userandom,NULL);
334:     if (cheb->userandom) {
335:       const char  *ksppre;
336:       if (!cheb->random) {
337:         PetscRandomCreate(PetscObjectComm((PetscObject)ksp),&cheb->random);
338:       }
339:       KSPGetOptionsPrefix(cheb->kspest, &ksppre);
340:       PetscObjectSetOptionsPrefix((PetscObject)cheb->random,ksppre);
341:       PetscRandomSetFromOptions(cheb->random);
342:     }
343:     KSPSetFromOptions(cheb->kspest);
344:   }
345:   PetscOptionsTail();
346:   return(0);
347: }

351: /*
352:  * Must be passed a KSP solver that has "converged", with KSPSetComputeEigenvalues() called before the solve
353:  */
354: static PetscErrorCode KSPChebyshevComputeExtremeEigenvalues_Private(KSP kspest,PetscReal *emin,PetscReal *emax)
355: {
357:   PetscInt       n,neig;
358:   PetscReal      *re,*im,min,max;

361:   KSPGetIterationNumber(kspest,&n);
362:   PetscMalloc2(n,&re,n,&im);
363:   KSPComputeEigenvalues(kspest,n,re,im,&neig);
364:   min  = PETSC_MAX_REAL;
365:   max  = PETSC_MIN_REAL;
366:   for (n=0; n<neig; n++) {
367:     min = PetscMin(min,re[n]);
368:     max = PetscMax(max,re[n]);
369:   }
370:   PetscFree2(re,im);
371:   *emax = max;
372:   *emin = min;
373:   return(0);
374: }

378: static PetscErrorCode KSPSolve_Chebyshev(KSP ksp)
379: {
380:   KSP_Chebyshev  *cheb = (KSP_Chebyshev*)ksp->data;
382:   PetscInt       k,kp1,km1,maxit,ktmp,i;
383:   PetscScalar    alpha,omegaprod,mu,omega,Gamma,c[3],scale;
384:   PetscReal      rnorm = 0.0;
385:   Vec            sol_orig,b,p[3],r;
386:   Mat            Amat,Pmat;
387:   PetscBool      diagonalscale;

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

393:   PCGetOperators(ksp->pc,&Amat,&Pmat);
394:   if (cheb->kspest) {
395:     PetscObjectId    amatid,    pmatid;
396:     PetscObjectState amatstate, pmatstate;

398:     PetscObjectGetId((PetscObject)Amat,&amatid);
399:     PetscObjectGetId((PetscObject)Pmat,&pmatid);
400:     PetscObjectStateGet((PetscObject)Amat,&amatstate);
401:     PetscObjectStateGet((PetscObject)Pmat,&pmatstate);
402:     if (amatid != cheb->amatid || pmatid != cheb->pmatid || amatstate != cheb->amatstate || pmatstate != cheb->pmatstate) {
403:       PetscReal          max=0.0,min=0.0;
404:       Vec                B;
405:       KSPConvergedReason reason;

407:       if (cheb->userandom) {
408:         B  = ksp->work[1];
409:         if (!cheb->random) {
410:           PetscRandomCreate(PetscObjectComm((PetscObject)B),&cheb->random);
411:         }
412:         VecSetRandom(B,cheb->random);
413:       } else {
414:         PC        pc;
415:         PetscBool change;

417:         KSPGetPC(cheb->kspest,&pc);
418:         PCPreSolveChangeRHS(pc,&change);
419:         if (change) {
420:           B = ksp->work[1];
421:           VecCopy(ksp->vec_rhs,B);
422:         } else {
423:           B = ksp->vec_rhs;
424:         }
425:       }
426:       KSPSolve(cheb->kspest,B,ksp->work[0]);

428:       KSPGetConvergedReason(cheb->kspest,&reason);
429:       if (reason < 0) {
430:         if (reason == KSP_DIVERGED_ITS) {
431:           PetscInfo(ksp,"Eigen estimator ran for prescribed number of iterations\n");
432:         } else {
433:           PetscInt its;
434:           KSPGetIterationNumber(cheb->kspest,&its);
435:           if (reason == KSP_DIVERGED_PCSETUP_FAILED) {
436:             PetscInfo(ksp,"Eigen estimator KSP_DIVERGED_PCSETUP_FAILED\n");
437:             ksp->reason = KSP_DIVERGED_PCSETUP_FAILED;
438:             VecSetInf(ksp->vec_sol);
439:           } else {
440:             SETERRQ2(PetscObjectComm((PetscObject)ksp),PETSC_ERR_PLIB,"Eigen estimator failed: %s at iteration %D",KSPConvergedReasons[reason],its);
441:           }
442:         }
443:       } else if (reason==KSP_CONVERGED_RTOL ||reason==KSP_CONVERGED_ATOL) {
444:         PetscInfo(ksp,"Eigen estimator converged prematurely. Should not happen except for small or low rank problem\n");
445:       } else {
446:         PetscInfo1(ksp,"Eigen estimator did not converge by iteration: %s\n",KSPConvergedReasons[reason]);
447:       }

449:       KSPChebyshevComputeExtremeEigenvalues_Private(cheb->kspest,&min,&max);

451:       cheb->emin = cheb->tform[0]*min + cheb->tform[1]*max;
452:       cheb->emax = cheb->tform[2]*min + cheb->tform[3]*max;

454:       cheb->amatid    = amatid;
455:       cheb->pmatid    = pmatid;
456:       cheb->amatstate = amatstate;
457:       cheb->pmatstate = pmatstate;
458:     }
459:   }

461:   ksp->its = 0;
462:   maxit    = ksp->max_it;

464:   /* These three point to the three active solutions, we
465:      rotate these three at each solution update */
466:   km1      = 0; k = 1; kp1 = 2;
467:   sol_orig = ksp->vec_sol; /* ksp->vec_sol will be asigned to rotating vector p[k], thus save its address */
468:   b        = ksp->vec_rhs;
469:   p[km1]   = sol_orig;
470:   p[k]     = ksp->work[0];
471:   p[kp1]   = ksp->work[1];
472:   r        = ksp->work[2];

474:   /* use scale*B as our preconditioner */
475:   scale = 2.0/(cheb->emax + cheb->emin);

477:   /*   -alpha <=  scale*lambda(B^{-1}A) <= alpha   */
478:   alpha     = 1.0 - scale*(cheb->emin);
479:   Gamma     = 1.0;
480:   mu        = 1.0/alpha;
481:   omegaprod = 2.0/alpha;

483:   c[km1] = 1.0;
484:   c[k]   = mu;

486:   if (!ksp->guess_zero) {
487:     KSP_MatMult(ksp,Amat,p[km1],r);     /*  r = b - A*p[km1] */
488:     VecAYPX(r,-1.0,b);
489:   } else {
490:     VecCopy(b,r);
491:   }

493:   KSP_PCApply(ksp,r,p[k]);  /* p[k] = scale B^{-1}r + p[km1] */
494:   VecAYPX(p[k],scale,p[km1]);

496:   for (i=0; i<maxit; i++) {
497:     PetscObjectSAWsTakeAccess((PetscObject)ksp);

499:     ksp->its++;
500:     PetscObjectSAWsGrantAccess((PetscObject)ksp);
501:     c[kp1] = 2.0*mu*c[k] - c[km1];
502:     omega  = omegaprod*c[k]/c[kp1];

504:     KSP_MatMult(ksp,Amat,p[k],r);          /*  r = b - Ap[k]    */
505:     VecAYPX(r,-1.0,b);
506:     KSP_PCApply(ksp,r,p[kp1]);             /*  p[kp1] = B^{-1}r  */
507:     ksp->vec_sol = p[k];

509:     /* calculate residual norm if requested */
510:     if (ksp->normtype != KSP_NORM_NONE || ksp->numbermonitors) {
511:       if (ksp->normtype == KSP_NORM_UNPRECONDITIONED) {
512:         VecNorm(r,NORM_2,&rnorm);
513:       } else {
514:         VecNorm(p[kp1],NORM_2,&rnorm);
515:       }
516:       PetscObjectSAWsTakeAccess((PetscObject)ksp);
517:       ksp->rnorm   = rnorm;
518:       PetscObjectSAWsGrantAccess((PetscObject)ksp);
519:       KSPLogResidualHistory(ksp,rnorm);
520:       KSPMonitor(ksp,i,rnorm);
521:       (*ksp->converged)(ksp,i,rnorm,&ksp->reason,ksp->cnvP);
522:       if (ksp->reason) break;
523:     }

525:     /* y^{k+1} = omega(y^{k} - y^{k-1} + Gamma*r^{k}) + y^{k-1} */
526:     VecAXPBYPCZ(p[kp1],1.0-omega,omega,omega*Gamma*scale,p[km1],p[k]);

528:     ktmp = km1;
529:     km1  = k;
530:     k    = kp1;
531:     kp1  = ktmp;
532:   }
533:   if (!ksp->reason) {
534:     if (ksp->normtype != KSP_NORM_NONE) {
535:       KSP_MatMult(ksp,Amat,p[k],r);       /*  r = b - Ap[k]    */
536:       VecAYPX(r,-1.0,b);
537:       if (ksp->normtype == KSP_NORM_UNPRECONDITIONED) {
538:         VecNorm(r,NORM_2,&rnorm);
539:       } else {
540:         KSP_PCApply(ksp,r,p[kp1]); /* p[kp1] = B^{-1}r */
541:         VecNorm(p[kp1],NORM_2,&rnorm);
542:       }
543:       PetscObjectSAWsTakeAccess((PetscObject)ksp);
544:       ksp->rnorm   = rnorm;
545:       PetscObjectSAWsGrantAccess((PetscObject)ksp);
546:       ksp->vec_sol = p[k];
547:       KSPLogResidualHistory(ksp,rnorm);
548:       KSPMonitor(ksp,i,rnorm);
549:     }
550:     if (ksp->its >= ksp->max_it) {
551:       if (ksp->normtype != KSP_NORM_NONE) {
552:         (*ksp->converged)(ksp,i,rnorm,&ksp->reason,ksp->cnvP);
553:         if (!ksp->reason) ksp->reason = KSP_DIVERGED_ITS;
554:       } else ksp->reason = KSP_CONVERGED_ITS;
555:     }
556:   }

558:   /* make sure solution is in vector x */
559:   ksp->vec_sol = sol_orig;
560:   if (k) {
561:     VecCopy(p[k],sol_orig);
562:   }
563:   return(0);
564: }

568: static  PetscErrorCode KSPView_Chebyshev(KSP ksp,PetscViewer viewer)
569: {
570:   KSP_Chebyshev  *cheb = (KSP_Chebyshev*)ksp->data;
572:   PetscBool      iascii;

575:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
576:   if (iascii) {
577:     PetscViewerASCIIPrintf(viewer,"  Chebyshev: eigenvalue estimates:  min = %g, max = %g\n",(double)cheb->emin,(double)cheb->emax);
578:     if (cheb->kspest) {
579:       PetscViewerASCIIPrintf(viewer,"  Chebyshev: eigenvalues estimated using %s with translations  [%g %g; %g %g]\n",((PetscObject) cheb->kspest)->type_name,(double)cheb->tform[0],(double)cheb->tform[1],(double)cheb->tform[2],(double)cheb->tform[3]);
580:       PetscViewerASCIIPushTab(viewer);
581:       KSPView(cheb->kspest,viewer);
582:       PetscViewerASCIIPopTab(viewer);
583:       if (cheb->userandom) {
584:         PetscViewerASCIIPrintf(viewer,"  Chebyshev: estimating eigenvalues using random right hand side\n");
585:         PetscViewerASCIIPushTab(viewer);
586:         PetscRandomView(cheb->random,viewer);
587:         PetscViewerASCIIPopTab(viewer);
588:       }
589:     }
590:   }
591:   return(0);
592: }

596: static PetscErrorCode KSPDestroy_Chebyshev(KSP ksp)
597: {
598:   KSP_Chebyshev  *cheb = (KSP_Chebyshev*)ksp->data;

602:   KSPDestroy(&cheb->kspest);
603:   PetscRandomDestroy(&cheb->random);
604:   PetscObjectComposeFunction((PetscObject)ksp,"KSPChebyshevSetEigenvalues_C",NULL);
605:   PetscObjectComposeFunction((PetscObject)ksp,"KSPChebyshevEstEigSet_C",NULL);
606:   PetscObjectComposeFunction((PetscObject)ksp,"KSPChebyshevEstEigSetRandom_C",NULL);
607:   PetscObjectComposeFunction((PetscObject)ksp,"KSPChebyshevEstEigGetKSP_C",NULL);
608:   KSPDestroyDefault(ksp);
609:   return(0);
610: }

612: /*MC
613:      KSPCHEBYSHEV - The preconditioned Chebyshev iterative method

615:    Options Database Keys:
616: +   -ksp_chebyshev_eigenvalues <emin,emax> - set approximations to the smallest and largest eigenvalues
617:                   of the preconditioned operator. If these are accurate you will get much faster convergence.
618: .   -ksp_chebyshev_esteig <a,b,c,d> - estimate eigenvalues using a Krylov method, then use this
619:                          transform for Chebyshev eigenvalue bounds (KSPChebyshevEstEigSet())
620: .   -ksp_chebyshev_esteig_steps - number of estimation steps
621: -   -ksp_chebyshev_esteig_random - use random number generator to create right hand side for eigenvalue estimator

623:    Level: beginner

625:    Notes: The Chebyshev method requires both the matrix and preconditioner to
626:           be symmetric positive (semi) definite.
627:           Only support for left preconditioning.

629:           Chebyshev is configured as a smoother by default, targetting the "upper" part of the spectrum.
630:           The user should call KSPChebyshevSetEigenvalues() if they have eigenvalue estimates.

632: .seealso:  KSPCreate(), KSPSetType(), KSPType (for list of available types), KSP,
633:            KSPChebyshevSetEigenvalues(), KSPChebyshevEstEigSet(), KSPChebyshevEstEigSetUseRandom(), KSPChebyshevEstEigSetRandom(),
634:            KSPRICHARDSON, KSPCG, PCMG

636: M*/

640: PETSC_EXTERN PetscErrorCode KSPCreate_Chebyshev(KSP ksp)
641: {
643:   KSP_Chebyshev  *chebyshevP;

646:   PetscNewLog(ksp,&chebyshevP);

648:   ksp->data = (void*)chebyshevP;
649:   KSPSetSupportedNorm(ksp,KSP_NORM_PRECONDITIONED,PC_LEFT,3);
650:   KSPSetSupportedNorm(ksp,KSP_NORM_UNPRECONDITIONED,PC_LEFT,2);

652:   chebyshevP->emin = 0.;
653:   chebyshevP->emax = 0.;

655:   chebyshevP->tform[0] = 0.0;
656:   chebyshevP->tform[1] = 0.1;
657:   chebyshevP->tform[2] = 0;
658:   chebyshevP->tform[3] = 1.1;
659:   chebyshevP->eststeps = 10;
660:   chebyshevP->userandom = PETSC_FALSE;

662:   ksp->ops->setup          = KSPSetUp_Chebyshev;
663:   ksp->ops->solve          = KSPSolve_Chebyshev;
664:   ksp->ops->destroy        = KSPDestroy_Chebyshev;
665:   ksp->ops->buildsolution  = KSPBuildSolutionDefault;
666:   ksp->ops->buildresidual  = KSPBuildResidualDefault;
667:   ksp->ops->setfromoptions = KSPSetFromOptions_Chebyshev;
668:   ksp->ops->view           = KSPView_Chebyshev;
669:   ksp->ops->reset          = KSPReset_Chebyshev;

671:   PetscObjectComposeFunction((PetscObject)ksp,"KSPChebyshevSetEigenvalues_C",KSPChebyshevSetEigenvalues_Chebyshev);
672:   PetscObjectComposeFunction((PetscObject)ksp,"KSPChebyshevEstEigSet_C",KSPChebyshevEstEigSet_Chebyshev);
673:   PetscObjectComposeFunction((PetscObject)ksp,"KSPChebyshevEstEigSetRandom_C",KSPChebyshevEstEigSetRandom_Chebyshev);
674:   PetscObjectComposeFunction((PetscObject)ksp,"KSPChebyshevEstEigSetUseRandom_C",KSPChebyshevEstEigSetUseRandom_Chebyshev);
675:   PetscObjectComposeFunction((PetscObject)ksp,"KSPChebyshevEstEigGetKSP_C",KSPChebyshevEstEigGetKSP_Chebyshev);
676:   return(0);
677: }