xref: /petsc/src/tao/unconstrained/impls/cg/taocg.c (revision 8931d48269a3fd1175e58f4f026fe4afbe678bc2)

#include <petsctaolinesearch.h>
#include <../src/tao/unconstrained/impls/cg/taocg.h>

#define CG_FletcherReeves       0
#define CG_PolakRibiere         1
#define CG_PolakRibierePlus     2
#define CG_HestenesStiefel      3
#define CG_DaiYuan              4
#define CG_Types                5

 static const char *CG_Table[64] = {"fr", "pr", "prp", "hs", "dy"};

 #undef __FUNCT__
 #define __FUNCT__ "TaoSolve_CG"
 static PetscErrorCode TaoSolve_CG(Tao tao)
 {
   TAO_CG                       *cgP = (TAO_CG*)tao->data;
   PetscErrorCode               ierr;
   TaoConvergedReason           reason = TAO_CONTINUE_ITERATING;
   TaoLineSearchConvergedReason ls_status = TAOLINESEARCH_CONTINUE_ITERATING;
   PetscReal                    step=1.0,f,gnorm,gnorm2,delta,gd,ginner,beta;
   PetscReal                    gd_old,gnorm2_old,f_old;

   PetscFunctionBegin;
   if (tao->XL || tao->XU || tao->ops->computebounds) {
     ierr = PetscPrintf(((PetscObject)tao)->comm,"WARNING: Variable bounds have been set but will be ignored by cg algorithm\n");CHKERRQ(ierr);
   }

   /*  Check convergence criteria */
   ierr = TaoComputeObjectiveAndGradient(tao, tao->solution, &f, tao->gradient);CHKERRQ(ierr);
   ierr = VecNorm(tao->gradient,NORM_2,&gnorm);CHKERRQ(ierr);
   if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1, "User provided compute function generated Inf or NaN");

   ierr = TaoMonitor(tao, tao->niter, f, gnorm, 0.0, step, &reason);CHKERRQ(ierr);
   if (reason != TAO_CONTINUE_ITERATING) PetscFunctionReturn(0);

   /*  Set initial direction to -gradient */
   ierr = VecCopy(tao->gradient, tao->stepdirection);CHKERRQ(ierr);
   ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr);
   gnorm2 = gnorm*gnorm;

   /*  Set initial scaling for the function */
   if (f != 0.0) {
     delta = 2.0*PetscAbsScalar(f) / gnorm2;
     delta = PetscMax(delta,cgP->delta_min);
     delta = PetscMin(delta,cgP->delta_max);
   } else {
     delta = 2.0 / gnorm2;
     delta = PetscMax(delta,cgP->delta_min);
     delta = PetscMin(delta,cgP->delta_max);
   }
   /*  Set counter for gradient and reset steps */
   cgP->ngradsteps = 0;
   cgP->nresetsteps = 0;

   while (1) {
     /*  Save the current gradient information */
     f_old = f;
     gnorm2_old = gnorm2;
     ierr = VecCopy(tao->solution, cgP->X_old);CHKERRQ(ierr);
     ierr = VecCopy(tao->gradient, cgP->G_old);CHKERRQ(ierr);
     ierr = VecDot(tao->gradient, tao->stepdirection, &gd);CHKERRQ(ierr);
     if ((gd >= 0) || PetscIsInfOrNanReal(gd)) {
       ++cgP->ngradsteps;
       if (f != 0.0) {
         delta = 2.0*PetscAbsScalar(f) / gnorm2;
         delta = PetscMax(delta,cgP->delta_min);
         delta = PetscMin(delta,cgP->delta_max);
       } else {
         delta = 2.0 / gnorm2;
         delta = PetscMax(delta,cgP->delta_min);
         delta = PetscMin(delta,cgP->delta_max);
       }

       ierr = VecCopy(tao->gradient, tao->stepdirection);CHKERRQ(ierr);
       ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr);
     }

     /*  Search direction for improving point */
     ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,delta);CHKERRQ(ierr);
     ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, tao->stepdirection, &step, &ls_status);CHKERRQ(ierr);
     ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);
     if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {
       /*  Linesearch failed */
       /*  Reset factors and use scaled gradient step */
       ++cgP->nresetsteps;
       f = f_old;
       gnorm2 = gnorm2_old;
       ierr = VecCopy(cgP->X_old, tao->solution);CHKERRQ(ierr);
       ierr = VecCopy(cgP->G_old, tao->gradient);CHKERRQ(ierr);

       if (f != 0.0) {
         delta = 2.0*PetscAbsScalar(f) / gnorm2;
         delta = PetscMax(delta,cgP->delta_min);
         delta = PetscMin(delta,cgP->delta_max);
       } else {
         delta = 2.0 / gnorm2;
         delta = PetscMax(delta,cgP->delta_min);
         delta = PetscMin(delta,cgP->delta_max);
       }

       ierr = VecCopy(tao->gradient, tao->stepdirection);CHKERRQ(ierr);
       ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr);

       ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,delta);CHKERRQ(ierr);
       ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, tao->stepdirection, &step, &ls_status);CHKERRQ(ierr);
       ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);

       if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {
         /*  Linesearch failed again */
         /*  switch to unscaled gradient */
         f = f_old;
         gnorm2 = gnorm2_old;
         ierr = VecCopy(cgP->X_old, tao->solution);CHKERRQ(ierr);
         ierr = VecCopy(cgP->G_old, tao->gradient);CHKERRQ(ierr);
         delta = 1.0;
         ierr = VecCopy(tao->solution, tao->stepdirection);CHKERRQ(ierr);
         ierr = VecScale(tao->stepdirection, -1.0);CHKERRQ(ierr);

         ierr = TaoLineSearchSetInitialStepLength(tao->linesearch,delta);CHKERRQ(ierr);
         ierr = TaoLineSearchApply(tao->linesearch, tao->solution, &f, tao->gradient, tao->stepdirection, &step, &ls_status);CHKERRQ(ierr);
         ierr = TaoAddLineSearchCounts(tao);CHKERRQ(ierr);
         if (ls_status != TAOLINESEARCH_SUCCESS && ls_status != TAOLINESEARCH_SUCCESS_USER) {

           /*  Line search failed for last time -- give up */
           f = f_old;
           gnorm2 = gnorm2_old;
           ierr = VecCopy(cgP->X_old, tao->solution);CHKERRQ(ierr);
           ierr = VecCopy(cgP->G_old, tao->gradient);CHKERRQ(ierr);
           step = 0.0;
           reason = TAO_DIVERGED_LS_FAILURE;
           tao->reason = TAO_DIVERGED_LS_FAILURE;
         }
       }
     }

     /*  Check for bad value */
     ierr = VecNorm(tao->gradient,NORM_2,&gnorm);CHKERRQ(ierr);
     if (PetscIsInfOrNanReal(f) || PetscIsInfOrNanReal(gnorm)) SETERRQ(PETSC_COMM_SELF,1,"User-provided compute function generated Inf or NaN");

     /*  Check for termination */
     gnorm2 =gnorm * gnorm;
     tao->niter++;
     ierr = TaoMonitor(tao, tao->niter, f, gnorm, 0.0, step, &reason);CHKERRQ(ierr);
     if (reason != TAO_CONTINUE_ITERATING) {
       break;
     }

     /*  Check for restart condition */
     ierr = VecDot(tao->gradient, cgP->G_old, &ginner);CHKERRQ(ierr);
     if (PetscAbsScalar(ginner) >= cgP->eta * gnorm2) {
       /*  Gradients far from orthognal; use steepest descent direction */
       beta = 0.0;
     } else {
       /*  Gradients close to orthogonal; use conjugate gradient formula */
       switch (cgP->cg_type) {
       case CG_FletcherReeves:
         beta = gnorm2 / gnorm2_old;
         break;

       case CG_PolakRibiere:
         beta = (gnorm2 - ginner) / gnorm2_old;
         break;

       case CG_PolakRibierePlus:
         beta = PetscMax((gnorm2-ginner)/gnorm2_old, 0.0);
         break;

       case CG_HestenesStiefel:
         ierr = VecDot(tao->gradient, tao->stepdirection, &gd);CHKERRQ(ierr);
         ierr = VecDot(cgP->G_old, tao->stepdirection, &gd_old);CHKERRQ(ierr);
         beta = (gnorm2 - ginner) / (gd - gd_old);
         break;

       case CG_DaiYuan:
         ierr = VecDot(tao->gradient, tao->stepdirection, &gd);CHKERRQ(ierr);
         ierr = VecDot(cgP->G_old, tao->stepdirection, &gd_old);CHKERRQ(ierr);
         beta = gnorm2 / (gd - gd_old);
         break;

       default:
         beta = 0.0;
         break;
       }
     }

     /*  Compute the direction d=-g + beta*d */
     ierr = VecAXPBY(tao->stepdirection, -1.0, beta, tao->gradient);CHKERRQ(ierr);

     /*  update initial steplength choice */
     delta = 1.0;
     delta = PetscMax(delta, cgP->delta_min);
     delta = PetscMin(delta, cgP->delta_max);
   }
   PetscFunctionReturn(0);
 }

 #undef __FUNCT__
 #define __FUNCT__ "TaoSetUp_CG"
 static PetscErrorCode TaoSetUp_CG(Tao tao)
 {
   TAO_CG         *cgP = (TAO_CG*)tao->data;
   PetscErrorCode ierr;

   PetscFunctionBegin;
   if (!tao->gradient) {ierr = VecDuplicate(tao->solution,&tao->gradient);CHKERRQ(ierr);}
   if (!tao->stepdirection) {ierr = VecDuplicate(tao->solution,&tao->stepdirection);CHKERRQ(ierr); }
   if (!cgP->X_old) {ierr = VecDuplicate(tao->solution,&cgP->X_old);CHKERRQ(ierr);}
   if (!cgP->G_old) {ierr = VecDuplicate(tao->gradient,&cgP->G_old);CHKERRQ(ierr); }
    PetscFunctionReturn(0);
 }

 #undef __FUNCT__
 #define __FUNCT__ "TaoDestroy_CG"
 static PetscErrorCode TaoDestroy_CG(Tao tao)
 {
   TAO_CG         *cgP = (TAO_CG*) tao->data;
   PetscErrorCode ierr;

   PetscFunctionBegin;
   if (tao->setupcalled) {
     ierr = VecDestroy(&cgP->X_old);CHKERRQ(ierr);
     ierr = VecDestroy(&cgP->G_old);CHKERRQ(ierr);
   }
   ierr = TaoLineSearchDestroy(&tao->linesearch);CHKERRQ(ierr);
   ierr = PetscFree(tao->data);CHKERRQ(ierr);
   PetscFunctionReturn(0);
 }

 #undef __FUNCT__
 #define __FUNCT__ "TaoSetFromOptions_CG"
static PetscErrorCode TaoSetFromOptions_CG(PetscOptions *PetscOptionsObject,Tao tao)
 {
    TAO_CG         *cgP = (TAO_CG*)tao->data;
    PetscErrorCode ierr;

    PetscFunctionBegin;
    ierr = TaoLineSearchSetFromOptions(tao->linesearch);CHKERRQ(ierr);
    ierr = PetscOptionsHead(PetscOptionsObject,"Nonlinear Conjugate Gradient method for unconstrained optimization");CHKERRQ(ierr);
    ierr = PetscOptionsReal("-tao_cg_eta","restart tolerance", "", cgP->eta,&cgP->eta,NULL);CHKERRQ(ierr);
    ierr = PetscOptionsEList("-tao_cg_type","cg formula", "", CG_Table, CG_Types, CG_Table[cgP->cg_type], &cgP->cg_type,NULL);CHKERRQ(ierr);
    ierr = PetscOptionsReal("-tao_cg_delta_min","minimum delta value", "", cgP->delta_min,&cgP->delta_min,NULL);CHKERRQ(ierr);
    ierr = PetscOptionsReal("-tao_cg_delta_max","maximum delta value", "", cgP->delta_max,&cgP->delta_max,NULL);CHKERRQ(ierr);
   ierr = PetscOptionsTail();CHKERRQ(ierr);
   PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoView_CG"
static PetscErrorCode TaoView_CG(Tao tao, PetscViewer viewer)
{
  PetscBool      isascii;
  TAO_CG         *cgP = (TAO_CG*)tao->data;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii);CHKERRQ(ierr);
  if (isascii) {
    ierr = PetscViewerASCIIPushTab(viewer);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer, "CG Type: %s\n", CG_Table[cgP->cg_type]);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPrintf(viewer, "Gradient steps: %D\n", cgP->ngradsteps);CHKERRQ(ierr);
    ierr= PetscViewerASCIIPrintf(viewer, "Reset steps: %D\n", cgP->nresetsteps);CHKERRQ(ierr);
    ierr = PetscViewerASCIIPopTab(viewer);CHKERRQ(ierr);
  }
  PetscFunctionReturn(0);
}

/*MC
     TAOCG -   Nonlinear conjugate gradient method is an extension of the
nonlinear conjugate gradient solver for nonlinear optimization.

   Options Database Keys:
+      -tao_cg_eta <r> - restart tolerance
.      -tao_cg_type <taocg_type> - cg formula
.      -tao_cg_delta_min <r> - minimum delta value
-      -tao_cg_delta_max <r> - maximum delta value

  Notes:
     CG formulas are:
         "fr" - Fletcher-Reeves
         "pr" - Polak-Ribiere
         "prp" - Polak-Ribiere-Plus
         "hs" - Hestenes-Steifel
         "dy" - Dai-Yuan
  Level: beginner
M*/


#undef __FUNCT__
#define __FUNCT__ "TaoCreate_CG"
PETSC_EXTERN PetscErrorCode TaoCreate_CG(Tao tao)
{
  TAO_CG         *cgP;
  const char     *morethuente_type = TAOLINESEARCHMT;
  PetscErrorCode ierr;

  PetscFunctionBegin;
  tao->ops->setup = TaoSetUp_CG;
  tao->ops->solve = TaoSolve_CG;
  tao->ops->view = TaoView_CG;
  tao->ops->setfromoptions = TaoSetFromOptions_CG;
  tao->ops->destroy = TaoDestroy_CG;

  tao->max_it = 2000;
  tao->max_funcs = 4000;
  tao->fatol = 1e-4;
  tao->frtol = 1e-4;

  /*  Note: nondefault values should be used for nonlinear conjugate gradient  */
  /*  method.  In particular, gtol should be less that 0.5; the value used in  */
  /*  Nocedal and Wright is 0.10.  We use the default values for the  */
  /*  linesearch because it seems to work better. */
  ierr = TaoLineSearchCreate(((PetscObject)tao)->comm, &tao->linesearch);CHKERRQ(ierr);
  ierr = TaoLineSearchSetType(tao->linesearch, morethuente_type);CHKERRQ(ierr);
  ierr = TaoLineSearchUseTaoRoutines(tao->linesearch, tao);CHKERRQ(ierr);

  ierr = PetscNewLog(tao,&cgP);CHKERRQ(ierr);
  tao->data = (void*)cgP;
  cgP->eta = 0.1;
  cgP->delta_min = 1e-7;
  cgP->delta_max = 100;
  cgP->cg_type = CG_PolakRibierePlus;
  PetscFunctionReturn(0);
}