xref: /petsc/src/tao/unconstrained/tutorials/rosenbrock1.c (revision 48a46eb9bd028bec07ec0f396b1a3abb43f14558)

/* 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";

/*
   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 */
  PetscFunctionBeginUser;
  PetscCall(PetscInitialize(&argc, &argv, (char *)0, help));
  PetscCallMPI(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 */
  PetscCall(PetscOptionsGetInt(NULL, NULL, "-n", &user.n, &flg));
  PetscCall(PetscOptionsGetReal(NULL, NULL, "-alpha", &user.alpha, &flg));
  PetscCall(PetscOptionsGetBool(NULL, NULL, "-chained", &user.chained, &flg));
  PetscCall(PetscOptionsGetBool(NULL, NULL, "-test_lmvm", &test_lmvm, &flg));

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

  /* The TAO code begins here */

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

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

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

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

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

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

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

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

  PetscCall(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 */
  PetscCall(VecGetArrayRead(X, &x));
  PetscCall(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 */
  PetscCall(VecRestoreArrayRead(X, &x));
  PetscCall(VecRestoreArray(G, &g));
  *f = ff;

  PetscCall(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 */
  PetscCall(MatAssembled(H, &assembled));
  if (assembled) PetscCall(MatZeroEntries(H));

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

  /* Compute H(X) entries */
  if (user->chained) {
    PetscCall(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;
      PetscCall(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;
      PetscCall(MatSetValues(H, 2, ind, 2, ind, v[0], INSERT_VALUES));
    }
  }
  PetscCall(VecRestoreArrayRead(X, &x));

  /* Assemble matrix */
  PetscCall(MatAssemblyBegin(H, MAT_FINAL_ASSEMBLY));
  PetscCall(MatAssemblyEnd(H, MAT_FINAL_ASSEMBLY));
  PetscCall(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*/