Date: 2021-03-03T22:27:25+00:00

Actual source code: rich.c
petsc-3.14.5 2021-03-03
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      2: /*
      3:             This implements Richardson Iteration.
      4: */
      5: #include <../src/ksp/ksp/impls/rich/richardsonimpl.h>

      7: PetscErrorCode KSPSetUp_Richardson(KSP ksp)
      8: {
     10:   KSP_Richardson *richardsonP = (KSP_Richardson*)ksp->data;

     13:   if (richardsonP->selfscale) {
     14:     KSPSetWorkVecs(ksp,4);
     15:   } else {
     16:     KSPSetWorkVecs(ksp,2);
     17:   }
     18:   return(0);
     19: }

     21: PetscErrorCode  KSPSolve_Richardson(KSP ksp)
     22: {
     24:   PetscInt       i,maxit;
     25:   PetscReal      rnorm = 0.0,abr;
     26:   PetscScalar    scale,rdot;
     27:   Vec            x,b,r,z,w = NULL,y = NULL;
     28:   PetscInt       xs, ws;
     29:   Mat            Amat,Pmat;
     30:   KSP_Richardson *richardsonP = (KSP_Richardson*)ksp->data;
     31:   PetscBool      exists,diagonalscale;
     32:   MatNullSpace   nullsp;

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

     38:   ksp->its = 0;

     40:   PCGetOperators(ksp->pc,&Amat,&Pmat);
     41:   x    = ksp->vec_sol;
     42:   b    = ksp->vec_rhs;
     43:   VecGetSize(x,&xs);
     44:   VecGetSize(ksp->work[0],&ws);
     45:   if (xs != ws) {
     46:     if (richardsonP->selfscale) {
     47:       KSPSetWorkVecs(ksp,4);
     48:     } else {
     49:       KSPSetWorkVecs(ksp,2);
     50:     }
     51:   }
     52:   r = ksp->work[0];
     53:   z = ksp->work[1];
     54:   if (richardsonP->selfscale) {
     55:     w = ksp->work[2];
     56:     y = ksp->work[3];
     57:   }
     58:   maxit = ksp->max_it;

     60:   /* if user has provided fast Richardson code use that */
     61:   PCApplyRichardsonExists(ksp->pc,&exists);
     62:   MatGetNullSpace(Pmat,&nullsp);
     63:   if (exists && maxit > 0 && richardsonP->scale == 1.0 && (ksp->converged == KSPConvergedDefault || ksp->converged == KSPConvergedSkip) && !ksp->numbermonitors && !ksp->transpose_solve && !nullsp) {
     64:     PCRichardsonConvergedReason reason;
     65:     PCApplyRichardson(ksp->pc,b,x,r,ksp->rtol,ksp->abstol,ksp->divtol,maxit,ksp->guess_zero,&ksp->its,&reason);
     66:     ksp->reason = (KSPConvergedReason)reason;
     67:     return(0);
     68:   } else {
     69:     PetscInfo(ksp,"KSPSolve_Richardson: Warning, skipping optimized PCApplyRichardson()\n");
     70:   }

     72:   if (!ksp->guess_zero) {                          /*   r <- b - A x     */
     73:     KSP_MatMult(ksp,Amat,x,r);
     74:     VecAYPX(r,-1.0,b);
     75:   } else {
     76:     VecCopy(b,r);
     77:   }

     79:   ksp->its = 0;
     80:   if (richardsonP->selfscale) {
     81:     KSP_PCApply(ksp,r,z);         /*   z <- B r          */
     82:     for (i=0; i<maxit; i++) {

     84:       if (ksp->normtype == KSP_NORM_UNPRECONDITIONED) {
     85:         VecNorm(r,NORM_2,&rnorm); /*   rnorm <- r'*r     */
     86:       } else if (ksp->normtype == KSP_NORM_PRECONDITIONED) {
     87:         VecNorm(z,NORM_2,&rnorm); /*   rnorm <- z'*z     */
     88:       } else rnorm = 0.0;

     90:       KSPCheckNorm(ksp,rnorm);
     91:       ksp->rnorm = rnorm;
     92:       KSPMonitor(ksp,i,rnorm);
     93:       KSPLogResidualHistory(ksp,rnorm);
     94:       (*ksp->converged)(ksp,i,rnorm,&ksp->reason,ksp->cnvP);
     95:       if (ksp->reason) break;
     96:       KSP_PCApplyBAorAB(ksp,z,y,w); /* y = BAz = BABr */
     97:       VecDotNorm2(z,y,&rdot,&abr);   /*   rdot = (Br)^T(BABR); abr = (BABr)^T (BABr) */
     98:       scale = rdot/abr;
     99:       PetscInfo1(ksp,"Self-scale factor %g\n",(double)PetscRealPart(scale));
    100:       VecAXPY(x,scale,z);   /*   x  <- x + scale z */
    101:       VecAXPY(r,-scale,w);  /*  r <- r - scale*Az */
    102:       VecAXPY(z,-scale,y);  /*  z <- z - scale*y */
    103:       ksp->its++;
    104:     }
    105:   } else {
    106:     for (i=0; i<maxit; i++) {

    108:       if (ksp->normtype == KSP_NORM_UNPRECONDITIONED) {
    109:         VecNorm(r,NORM_2,&rnorm); /*   rnorm <- r'*r     */
    110:       } else if (ksp->normtype == KSP_NORM_PRECONDITIONED) {
    111:         KSP_PCApply(ksp,r,z);    /*   z <- B r          */
    112:         VecNorm(z,NORM_2,&rnorm); /*   rnorm <- z'*z     */
    113:       } else rnorm = 0.0;
    114:       ksp->rnorm = rnorm;
    115:       KSPMonitor(ksp,i,rnorm);
    116:       KSPLogResidualHistory(ksp,rnorm);
    117:       (*ksp->converged)(ksp,i,rnorm,&ksp->reason,ksp->cnvP);
    118:       if (ksp->reason) break;
    119:       if (ksp->normtype != KSP_NORM_PRECONDITIONED) {
    120:         KSP_PCApply(ksp,r,z);    /*   z <- B r          */
    121:       }

    123:       VecAXPY(x,richardsonP->scale,z);    /*   x  <- x + scale z */
    124:       ksp->its++;

    126:       if (i+1 < maxit || ksp->normtype != KSP_NORM_NONE) {
    127:         KSP_MatMult(ksp,Amat,x,r);      /*   r  <- b - Ax      */
    128:         VecAYPX(r,-1.0,b);
    129:       }
    130:     }
    131:   }
    132:   if (!ksp->reason) {
    133:     if (ksp->normtype == KSP_NORM_UNPRECONDITIONED) {
    134:       VecNorm(r,NORM_2,&rnorm);     /*   rnorm <- r'*r     */
    135:     } else if (ksp->normtype == KSP_NORM_PRECONDITIONED) {
    136:       KSP_PCApply(ksp,r,z);   /*   z <- B r          */
    137:       VecNorm(z,NORM_2,&rnorm);     /*   rnorm <- z'*z     */
    138:     } else rnorm = 0.0;

    140:     KSPCheckNorm(ksp,rnorm);
    141:     ksp->rnorm = rnorm;
    142:     KSPLogResidualHistory(ksp,rnorm);
    143:     KSPMonitor(ksp,i,rnorm);
    144:     if (ksp->its >= ksp->max_it) {
    145:       if (ksp->normtype != KSP_NORM_NONE) {
    146:         (*ksp->converged)(ksp,i,rnorm,&ksp->reason,ksp->cnvP);
    147:         if (!ksp->reason) ksp->reason = KSP_DIVERGED_ITS;
    148:       } else {
    149:         ksp->reason = KSP_CONVERGED_ITS;
    150:       }
    151:     }
    152:   }
    153:   return(0);
    154: }

    156: PetscErrorCode KSPView_Richardson(KSP ksp,PetscViewer viewer)
    157: {
    158:   KSP_Richardson *richardsonP = (KSP_Richardson*)ksp->data;
    160:   PetscBool      iascii;

    163:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    164:   if (iascii) {
    165:     if (richardsonP->selfscale) {
    166:       PetscViewerASCIIPrintf(viewer,"  using self-scale best computed damping factor\n");
    167:     } else {
    168:       PetscViewerASCIIPrintf(viewer,"  damping factor=%g\n",(double)richardsonP->scale);
    169:     }
    170:   }
    171:   return(0);
    172: }

    174: PetscErrorCode KSPSetFromOptions_Richardson(PetscOptionItems *PetscOptionsObject,KSP ksp)
    175: {
    176:   KSP_Richardson *rich = (KSP_Richardson*)ksp->data;
    178:   PetscReal      tmp;
    179:   PetscBool      flg,flg2;

    182:   PetscOptionsHead(PetscOptionsObject,"KSP Richardson Options");
    183:   PetscOptionsReal("-ksp_richardson_scale","damping factor","KSPRichardsonSetScale",rich->scale,&tmp,&flg);
    184:   if (flg) { KSPRichardsonSetScale(ksp,tmp); }
    185:   PetscOptionsBool("-ksp_richardson_self_scale","dynamically determine optimal damping factor","KSPRichardsonSetSelfScale",rich->selfscale,&flg2,&flg);
    186:   if (flg) { KSPRichardsonSetSelfScale(ksp,flg2); }
    187:   PetscOptionsTail();
    188:   return(0);
    189: }

    191: PetscErrorCode KSPDestroy_Richardson(KSP ksp)
    192: {

    196:   PetscObjectComposeFunction((PetscObject)ksp,"KSPRichardsonSetScale_C",NULL);
    197:   KSPDestroyDefault(ksp);
    198:   return(0);
    199: }

    201: static PetscErrorCode  KSPRichardsonSetScale_Richardson(KSP ksp,PetscReal scale)
    202: {
    203:   KSP_Richardson *richardsonP;

    206:   richardsonP        = (KSP_Richardson*)ksp->data;
    207:   richardsonP->scale = scale;
    208:   return(0);
    209: }

    211: static PetscErrorCode  KSPRichardsonSetSelfScale_Richardson(KSP ksp,PetscBool selfscale)
    212: {
    213:   KSP_Richardson *richardsonP;

    216:   richardsonP            = (KSP_Richardson*)ksp->data;
    217:   richardsonP->selfscale = selfscale;
    218:   return(0);
    219: }

    221: static PetscErrorCode KSPBuildResidual_Richardson(KSP ksp,Vec t,Vec v,Vec *V)
    222: {

    226:   if (ksp->normtype == KSP_NORM_NONE) {
    227:     KSPBuildResidualDefault(ksp,t,v,V);
    228:   } else {
    229:     VecCopy(ksp->work[0],v);
    230:     *V   = v;
    231:   }
    232:   return(0);
    233: }

    235: /*MC
    236:      KSPRICHARDSON - The preconditioned Richardson iterative method

    238:    Options Database Keys:
    239: .   -ksp_richardson_scale - damping factor on the correction (defaults to 1.0)

    241:    Level: beginner

    243:    Notes:
    244:     x^{n+1} = x^{n} + scale*B(b - A x^{n})

    246:           Here B is the application of the preconditioner

    248:           This method often (usually) will not converge unless scale is very small.

    250:    Notes:
    251:     For some preconditioners, currently SOR, the convergence test is skipped to improve speed,
    252:     thus it always iterates the maximum number of iterations you've selected. When -ksp_monitor
    253:     (or any other monitor) is turned on, the norm is computed at each iteration and so the convergence test is run unless
    254:     you specifically call KSPSetNormType(ksp,KSP_NORM_NONE);

    256:          For some preconditioners, currently PCMG and PCHYPRE with BoomerAMG if -ksp_monitor (and also
    257:     any other monitor) is not turned on then the convergence test is done by the preconditioner itself and
    258:     so the solver may run more or fewer iterations then if -ksp_monitor is selected.

    260:     Supports only left preconditioning

    262:     If using direct solvers such as PCLU and PCCHOLESKY one generally uses KSPPREONLY which uses exactly one iteration

    264: $    -ksp_type richardson -pc_type jacobi gives one classically Jacobi preconditioning

    266:   References:
    267: .  1. - L. F. Richardson, "The Approximate Arithmetical Solution by Finite Differences of Physical Problems Involving
    268:    Differential Equations, with an Application to the Stresses in a Masonry Dam",
    269:   Philosophical Transactions of the Royal Society of London. Series A,
    270:   Containing Papers of a Mathematical or Physical Character, Vol. 210, 1911 (1911).

    272: .seealso:  KSPCreate(), KSPSetType(), KSPType (for list of available types), KSP,
    273:            KSPRichardsonSetScale(), KSPPREONLY

    275: M*/

    277: PETSC_EXTERN PetscErrorCode KSPCreate_Richardson(KSP ksp)
    278: {
    280:   KSP_Richardson *richardsonP;

    283:   PetscNewLog(ksp,&richardsonP);
    284:   ksp->data = (void*)richardsonP;

    286:   KSPSetSupportedNorm(ksp,KSP_NORM_PRECONDITIONED,PC_LEFT,3);
    287:   KSPSetSupportedNorm(ksp,KSP_NORM_UNPRECONDITIONED,PC_LEFT,2);
    288:   KSPSetSupportedNorm(ksp,KSP_NORM_NONE,PC_LEFT,1);

    290:   ksp->ops->setup          = KSPSetUp_Richardson;
    291:   ksp->ops->solve          = KSPSolve_Richardson;
    292:   ksp->ops->destroy        = KSPDestroy_Richardson;
    293:   ksp->ops->buildsolution  = KSPBuildSolutionDefault;
    294:   ksp->ops->buildresidual  = KSPBuildResidual_Richardson;
    295:   ksp->ops->view           = KSPView_Richardson;
    296:   ksp->ops->setfromoptions = KSPSetFromOptions_Richardson;

    298:   PetscObjectComposeFunction((PetscObject)ksp,"KSPRichardsonSetScale_C",KSPRichardsonSetScale_Richardson);
    299:   PetscObjectComposeFunction((PetscObject)ksp,"KSPRichardsonSetSelfScale_C",KSPRichardsonSetSelfScale_Richardson);

    301:   richardsonP->scale = 1.0;
    302:   return(0);
    303: }