xref: /petsc/src/tao/unconstrained/tutorials/rosenbrock1.c (revision b122ec5aa1bd4469eb4e0673542fb7de3f411254)

/* Program usage: mpiexec -n 1 rosenbrock1 [-help] [all TAO options] */

/*  Include "petsctao.h" so we can use TAO solvers.  */
#include <petsctao.h>

static  char help[] = "This example demonstrates use of the TAO package to \n\
solve an unconstrained minimization problem on a single processor.  We \n\
minimize the extended Rosenbrock function: \n\
   sum_{i=0}^{n/2-1} (alpha*(x_{2i+1}-x_{2i}^2)^2 + (1-x_{2i})^2) \n\
or the chained Rosenbrock function:\n\
   sum_{i=0}^{n-1} alpha*(x_{i+1} - x_i^2)^2 + (1 - x_i)^2\n";

/*T
   Concepts: TAO^Solving an unconstrained minimization problem
   Routines: TaoCreate();
   Routines: TaoSetType(); TaoSetObjectiveAndGradient();
   Routines: TaoSetHessian();
   Routines: TaoSetSolution();
   Routines: TaoSetFromOptions();
   Routines: TaoSolve();
   Routines: TaoDestroy();
   Processors: 1
T*/

/*
   User-defined application context - contains data needed by the
   application-provided call-back routines that evaluate the function,
   gradient, and hessian.
*/
typedef struct {
  PetscInt  n;          /* dimension */
  PetscReal alpha;   /* condition parameter */
  PetscBool chained;
} AppCtx;

/* -------------- User-defined routines ---------- */
PetscErrorCode FormFunctionGradient(Tao,Vec,PetscReal*,Vec,void*);
PetscErrorCode FormHessian(Tao,Vec,Mat,Mat,void*);

int main(int argc,char **argv)
{
  PetscReal          zero=0.0;
  Vec                x;                     /* solution vector */
  Mat                H;
  Tao                tao;                   /* Tao solver context */
  PetscBool          flg, test_lmvm = PETSC_FALSE;
  PetscMPIInt        size;                  /* number of processes running */
  AppCtx             user;                  /* user-defined application context */
  KSP                ksp;
  PC                 pc;
  Mat                M;
  Vec                in, out, out2;
  PetscReal          mult_solve_dist;

  /* Initialize TAO and PETSc */
  CHKERRQ(PetscInitialize(&argc,&argv,(char*)0,help));
  CHKERRMPI(MPI_Comm_size(PETSC_COMM_WORLD,&size));
  PetscCheck(size == 1,PETSC_COMM_WORLD,PETSC_ERR_WRONG_MPI_SIZE,"Incorrect number of processors");

  /* Initialize problem parameters */
  user.n = 2; user.alpha = 99.0; user.chained = PETSC_FALSE;
  /* Check for command line arguments to override defaults */
  CHKERRQ(PetscOptionsGetInt(NULL,NULL,"-n",&user.n,&flg));
  CHKERRQ(PetscOptionsGetReal(NULL,NULL,"-alpha",&user.alpha,&flg));
  CHKERRQ(PetscOptionsGetBool(NULL,NULL,"-chained",&user.chained,&flg));
  CHKERRQ(PetscOptionsGetBool(NULL,NULL,"-test_lmvm",&test_lmvm,&flg));

  /* Allocate vectors for the solution and gradient */
  CHKERRQ(VecCreateSeq(PETSC_COMM_SELF,user.n,&x));
  CHKERRQ(MatCreateSeqBAIJ(PETSC_COMM_SELF,2,user.n,user.n,1,NULL,&H));

  /* The TAO code begins here */

  /* Create TAO solver with desired solution method */
  CHKERRQ(TaoCreate(PETSC_COMM_SELF,&tao));
  CHKERRQ(TaoSetType(tao,TAOLMVM));

  /* Set solution vec and an initial guess */
  CHKERRQ(VecSet(x, zero));
  CHKERRQ(TaoSetSolution(tao,x));

  /* Set routines for function, gradient, hessian evaluation */
  CHKERRQ(TaoSetObjectiveAndGradient(tao,NULL,FormFunctionGradient,&user));
  CHKERRQ(TaoSetHessian(tao,H,H,FormHessian,&user));

  /* Test the LMVM matrix */
  if (test_lmvm) {
    CHKERRQ(PetscOptionsSetValue(NULL, "-tao_type", "bqnktr"));
  }

  /* Check for TAO command line options */
  CHKERRQ(TaoSetFromOptions(tao));

  /* SOLVE THE APPLICATION */
  CHKERRQ(TaoSolve(tao));

  /* Test the LMVM matrix */
  if (test_lmvm) {
    CHKERRQ(TaoGetKSP(tao, &ksp));
    CHKERRQ(KSPGetPC(ksp, &pc));
    CHKERRQ(PCLMVMGetMatLMVM(pc, &M));
    CHKERRQ(VecDuplicate(x, &in));
    CHKERRQ(VecDuplicate(x, &out));
    CHKERRQ(VecDuplicate(x, &out2));
    CHKERRQ(VecSet(in, 1.0));
    CHKERRQ(MatMult(M, in, out));
    CHKERRQ(MatSolve(M, out, out2));
    CHKERRQ(VecAXPY(out2, -1.0, in));
    CHKERRQ(VecNorm(out2, NORM_2, &mult_solve_dist));
    if (mult_solve_dist < 1.e-11) {
      CHKERRQ(PetscPrintf(PetscObjectComm((PetscObject)tao), "error between LMVM MatMult and MatSolve: < 1.e-11\n"));
    } else if (mult_solve_dist < 1.e-6) {
      CHKERRQ(PetscPrintf(PetscObjectComm((PetscObject)tao), "error between LMVM MatMult and MatSolve: < 1.e-6\n"));
    } else {
      CHKERRQ(PetscPrintf(PetscObjectComm((PetscObject)tao), "error between LMVM MatMult and MatSolve: %e\n", (double)mult_solve_dist));
    }
    CHKERRQ(VecDestroy(&in));
    CHKERRQ(VecDestroy(&out));
    CHKERRQ(VecDestroy(&out2));
  }

  CHKERRQ(TaoDestroy(&tao));
  CHKERRQ(VecDestroy(&x));
  CHKERRQ(MatDestroy(&H));

  CHKERRQ(PetscFinalize());
  return 0;
}

/* -------------------------------------------------------------------- */
/*
    FormFunctionGradient - Evaluates the function, f(X), and gradient, G(X).

    Input Parameters:
.   tao  - the Tao context
.   X    - input vector
.   ptr  - optional user-defined context, as set by TaoSetFunctionGradient()

    Output Parameters:
.   G - vector containing the newly evaluated gradient
.   f - function value

    Note:
    Some optimization methods ask for the function and the gradient evaluation
    at the same time.  Evaluating both at once may be more efficient that
    evaluating each separately.
*/
PetscErrorCode FormFunctionGradient(Tao tao,Vec X,PetscReal *f, Vec G,void *ptr)
{
  AppCtx            *user = (AppCtx *) ptr;
  PetscInt          i,nn=user->n/2;
  PetscReal         ff=0,t1,t2,alpha=user->alpha;
  PetscScalar       *g;
  const PetscScalar *x;

  PetscFunctionBeginUser;
  /* Get pointers to vector data */
  CHKERRQ(VecGetArrayRead(X,&x));
  CHKERRQ(VecGetArray(G,&g));

  /* Compute G(X) */
  if (user->chained) {
    g[0] = 0;
    for (i=0; i<user->n-1; i++) {
      t1 = x[i+1] - x[i]*x[i];
      ff += PetscSqr(1 - x[i]) + alpha*t1*t1;
      g[i] += -2*(1 - x[i]) + 2*alpha*t1*(-2*x[i]);
      g[i+1] = 2*alpha*t1;
    }
  } else {
    for (i=0; i<nn; i++) {
      t1 = x[2*i+1]-x[2*i]*x[2*i]; t2= 1-x[2*i];
      ff += alpha*t1*t1 + t2*t2;
      g[2*i] = -4*alpha*t1*x[2*i]-2.0*t2;
      g[2*i+1] = 2*alpha*t1;
    }
  }

  /* Restore vectors */
  CHKERRQ(VecRestoreArrayRead(X,&x));
  CHKERRQ(VecRestoreArray(G,&g));
  *f   = ff;

  CHKERRQ(PetscLogFlops(15.0*nn));
  PetscFunctionReturn(0);
}

/* ------------------------------------------------------------------- */
/*
   FormHessian - Evaluates Hessian matrix.

   Input Parameters:
.  tao   - the Tao context
.  x     - input vector
.  ptr   - optional user-defined context, as set by TaoSetHessian()

   Output Parameters:
.  H     - Hessian matrix

   Note:  Providing the Hessian may not be necessary.  Only some solvers
   require this matrix.
*/
PetscErrorCode FormHessian(Tao tao,Vec X,Mat H, Mat Hpre, void *ptr)
{
  AppCtx            *user = (AppCtx*)ptr;
  PetscInt          i, ind[2];
  PetscReal         alpha=user->alpha;
  PetscReal         v[2][2];
  const PetscScalar *x;
  PetscBool         assembled;

  PetscFunctionBeginUser;
  /* Zero existing matrix entries */
  CHKERRQ(MatAssembled(H,&assembled));
  if (assembled) CHKERRQ(MatZeroEntries(H));

  /* Get a pointer to vector data */
  CHKERRQ(VecGetArrayRead(X,&x));

  /* Compute H(X) entries */
  if (user->chained) {
    CHKERRQ(MatZeroEntries(H));
    for (i=0; i<user->n-1; i++) {
      PetscScalar t1 = x[i+1] - x[i]*x[i];
      v[0][0] = 2 + 2*alpha*(t1*(-2) - 2*x[i]);
      v[0][1] = 2*alpha*(-2*x[i]);
      v[1][0] = 2*alpha*(-2*x[i]);
      v[1][1] = 2*alpha*t1;
      ind[0] = i; ind[1] = i+1;
      CHKERRQ(MatSetValues(H,2,ind,2,ind,v[0],ADD_VALUES));
    }
  } else {
    for (i=0; i<user->n/2; i++) {
      v[1][1] = 2*alpha;
      v[0][0] = -4*alpha*(x[2*i+1]-3*x[2*i]*x[2*i]) + 2;
      v[1][0] = v[0][1] = -4.0*alpha*x[2*i];
      ind[0]=2*i; ind[1]=2*i+1;
      CHKERRQ(MatSetValues(H,2,ind,2,ind,v[0],INSERT_VALUES));
    }
  }
  CHKERRQ(VecRestoreArrayRead(X,&x));

  /* Assemble matrix */
  CHKERRQ(MatAssemblyBegin(H,MAT_FINAL_ASSEMBLY));
  CHKERRQ(MatAssemblyEnd(H,MAT_FINAL_ASSEMBLY));
  CHKERRQ(PetscLogFlops(9.0*user->n/2.0));
  PetscFunctionReturn(0);
}

/*TEST

   build:
      requires: !complex

   test:
      args: -tao_smonitor -tao_type nls -tao_gatol 1.e-4
      requires: !single

   test:
      suffix: 2
      args: -tao_smonitor -tao_type lmvm -tao_gatol 1.e-3

   test:
      suffix: 3
      args: -tao_smonitor -tao_type ntr -tao_gatol 1.e-4
      requires: !single

   test:
      suffix: 4
      args: -tao_smonitor -tao_type ntr -tao_mf_hessian -tao_ntr_pc_type none -tao_gatol 1.e-4

   test:
      suffix: 5
      args: -tao_smonitor -tao_type bntr -tao_gatol 1.e-4

   test:
      suffix: 6
      args: -tao_smonitor -tao_type bntl -tao_gatol 1.e-4

   test:
      suffix: 7
      args: -tao_smonitor -tao_type bnls -tao_gatol 1.e-4

   test:
      suffix: 8
      args: -tao_smonitor -tao_type bntr -tao_bnk_max_cg_its 3 -tao_gatol 1.e-4

   test:
      suffix: 9
      args: -tao_smonitor -tao_type bntl -tao_bnk_max_cg_its 3 -tao_gatol 1.e-4

   test:
      suffix: 10
      args: -tao_smonitor -tao_type bnls -tao_bnk_max_cg_its 3 -tao_gatol 1.e-4

   test:
      suffix: 11
      args: -test_lmvm -tao_max_it 10 -tao_bqnk_mat_type lmvmbroyden

   test:
      suffix: 12
      args: -test_lmvm -tao_max_it 10 -tao_bqnk_mat_type lmvmbadbroyden

   test:
     suffix: 13
     args: -test_lmvm -tao_max_it 10 -tao_bqnk_mat_type lmvmsymbroyden

   test:
     suffix: 14
     args: -test_lmvm -tao_max_it 10 -tao_bqnk_mat_type lmvmbfgs

   test:
     suffix: 15
     args: -test_lmvm -tao_max_it 10 -tao_bqnk_mat_type lmvmdfp

   test:
     suffix: 16
     args: -test_lmvm -tao_max_it 10 -tao_bqnk_mat_type lmvmsr1

   test:
     suffix: 17
     args: -tao_smonitor -tao_gatol 1e-4 -tao_type bqnls

   test:
     suffix: 18
     args: -tao_smonitor -tao_gatol 1e-4 -tao_type blmvm

   test:
     suffix: 19
     args: -tao_smonitor -tao_gatol 1e-4 -tao_type bqnktr -tao_bqnk_mat_type lmvmsr1

   test:
     suffix: 20
     args: -tao_monitor -tao_gatol 1e-4 -tao_type blmvm -tao_ls_monitor

   test:
     suffix: 21
     args: -test_lmvm -tao_max_it 10 -tao_bqnk_mat_type lmvmsymbadbroyden

   test:
     suffix: 22
     args: -tao_max_it 1 -tao_converged_reason

   test:
     suffix: 23
     args: -tao_max_funcs 0 -tao_converged_reason

   test:
     suffix: 24
     args: -tao_gatol 10 -tao_converged_reason

   test:
     suffix: 25
     args: -tao_grtol 10 -tao_converged_reason

   test:
     suffix: 26
     args: -tao_gttol 10 -tao_converged_reason

   test:
     suffix: 27
     args: -tao_steptol 10 -tao_converged_reason

   test:
     suffix: 28
     args: -tao_fmin 10 -tao_converged_reason

TEST*/