xref: /petsc/src/tao/leastsquares/tutorials/chwirut1f.F90 (revision 9b88ac225e01f016352a5f4cd90e158abe5f5675)

!  Program usage: mpiexec -n 1 chwirut1f [-help] [all TAO options]
!
!  Description:  This example demonstrates use of the TAO package to solve a
!  nonlinear least-squares problem on a single processor.  We minimize the
!  Chwirut function:
!       sum_{i=0}^{n/2-1} ( alpha*(x_{2i+1}-x_{2i}^2)^2 + (1-x_{2i})^2)
!
!  The C version of this code is test_chwirut1.c
!

!
! ----------------------------------------------------------------------
!
#include <petsc/finclude/petsctao.h>
module chwirut1fmodule
  use petsctao
  implicit none

  PetscReal t(0:213)
  PetscReal y(0:213)
  PetscInt m, n

contains

! --------------------------------------------------------------------
!  FormFunction - Evaluates the function f(X) and gradient G(X)
!
!  Input Parameters:
!  tao - the Tao context
!  X   - input vector
!  dummy - not used
!
!  Output Parameters:
!  f - function vector
  subroutine FormFunction(ta, x, f, dummy, ierr)

    Tao ta
    Vec x, f
    PetscErrorCode ierr
    PetscInt dummy

    PetscInt i
    PetscScalar, pointer, dimension(:)  :: x_v, f_v

    ierr = 0

!     Get pointers to vector data
    PetscCall(VecGetArray(x, x_v, ierr))
    PetscCall(VecGetArray(f, f_v, ierr))

!     Compute F(X)
    do i = 0, m - 1
      f_v(i + 1) = y(i) - exp(-x_v(1)*t(i))/(x_v(2) + x_v(3)*t(i))
    end do

!     Restore vectors
    PetscCall(VecRestoreArray(X, x_v, ierr))
    PetscCall(VecRestoreArray(F, f_v, ierr))

  end

  subroutine FormStartingPoint(x)
    Vec x
    PetscScalar, pointer, dimension(:)  :: x_v
    PetscErrorCode ierr

    PetscCall(VecGetArray(x, x_v, ierr))
    x_v(1) = 0.15
    x_v(2) = 0.008
    x_v(3) = 0.01
    PetscCall(VecRestoreArray(x, x_v, ierr))
  end

  subroutine InitializeData()
    integer i
    i = 0
    y(i) = 92.9000; t(i) = 0.5000; i = i + 1
    y(i) = 78.7000; t(i) = 0.6250; i = i + 1
    y(i) = 64.2000; t(i) = 0.7500; i = i + 1
    y(i) = 64.9000; t(i) = 0.8750; i = i + 1
    y(i) = 57.1000; t(i) = 1.0000; i = i + 1
    y(i) = 43.3000; t(i) = 1.2500; i = i + 1
    y(i) = 31.1000; t(i) = 1.7500; i = i + 1
    y(i) = 23.6000; t(i) = 2.2500; i = i + 1
    y(i) = 31.0500; t(i) = 1.7500; i = i + 1
    y(i) = 23.7750; t(i) = 2.2500; i = i + 1
    y(i) = 17.7375; t(i) = 2.7500; i = i + 1
    y(i) = 13.8000; t(i) = 3.2500; i = i + 1
    y(i) = 11.5875; t(i) = 3.7500; i = i + 1
    y(i) = 9.4125; t(i) = 4.2500; i = i + 1
    y(i) = 7.7250; t(i) = 4.7500; i = i + 1
    y(i) = 7.3500; t(i) = 5.2500; i = i + 1
    y(i) = 8.0250; t(i) = 5.7500; i = i + 1
    y(i) = 90.6000; t(i) = 0.5000; i = i + 1
    y(i) = 76.9000; t(i) = 0.6250; i = i + 1
    y(i) = 71.6000; t(i) = 0.7500; i = i + 1
    y(i) = 63.6000; t(i) = 0.8750; i = i + 1
    y(i) = 54.0000; t(i) = 1.0000; i = i + 1
    y(i) = 39.2000; t(i) = 1.2500; i = i + 1
    y(i) = 29.3000; t(i) = 1.7500; i = i + 1
    y(i) = 21.4000; t(i) = 2.2500; i = i + 1
    y(i) = 29.1750; t(i) = 1.7500; i = i + 1
    y(i) = 22.1250; t(i) = 2.2500; i = i + 1
    y(i) = 17.5125; t(i) = 2.7500; i = i + 1
    y(i) = 14.2500; t(i) = 3.2500; i = i + 1
    y(i) = 9.4500; t(i) = 3.7500; i = i + 1
    y(i) = 9.1500; t(i) = 4.2500; i = i + 1
    y(i) = 7.9125; t(i) = 4.7500; i = i + 1
    y(i) = 8.4750; t(i) = 5.2500; i = i + 1
    y(i) = 6.1125; t(i) = 5.7500; i = i + 1
    y(i) = 80.0000; t(i) = 0.5000; i = i + 1
    y(i) = 79.0000; t(i) = 0.6250; i = i + 1
    y(i) = 63.8000; t(i) = 0.7500; i = i + 1
    y(i) = 57.2000; t(i) = 0.8750; i = i + 1
    y(i) = 53.2000; t(i) = 1.0000; i = i + 1
    y(i) = 42.5000; t(i) = 1.2500; i = i + 1
    y(i) = 26.8000; t(i) = 1.7500; i = i + 1
    y(i) = 20.4000; t(i) = 2.2500; i = i + 1
    y(i) = 26.8500; t(i) = 1.7500; i = i + 1
    y(i) = 21.0000; t(i) = 2.2500; i = i + 1
    y(i) = 16.4625; t(i) = 2.7500; i = i + 1
    y(i) = 12.5250; t(i) = 3.2500; i = i + 1
    y(i) = 10.5375; t(i) = 3.7500; i = i + 1
    y(i) = 8.5875; t(i) = 4.2500; i = i + 1
    y(i) = 7.1250; t(i) = 4.7500; i = i + 1
    y(i) = 6.1125; t(i) = 5.2500; i = i + 1
    y(i) = 5.9625; t(i) = 5.7500; i = i + 1
    y(i) = 74.1000; t(i) = 0.5000; i = i + 1
    y(i) = 67.3000; t(i) = 0.6250; i = i + 1
    y(i) = 60.8000; t(i) = 0.7500; i = i + 1
    y(i) = 55.5000; t(i) = 0.8750; i = i + 1
    y(i) = 50.3000; t(i) = 1.0000; i = i + 1
    y(i) = 41.0000; t(i) = 1.2500; i = i + 1
    y(i) = 29.4000; t(i) = 1.7500; i = i + 1
    y(i) = 20.4000; t(i) = 2.2500; i = i + 1
    y(i) = 29.3625; t(i) = 1.7500; i = i + 1
    y(i) = 21.1500; t(i) = 2.2500; i = i + 1
    y(i) = 16.7625; t(i) = 2.7500; i = i + 1
    y(i) = 13.2000; t(i) = 3.2500; i = i + 1
    y(i) = 10.8750; t(i) = 3.7500; i = i + 1
    y(i) = 8.1750; t(i) = 4.2500; i = i + 1
    y(i) = 7.3500; t(i) = 4.7500; i = i + 1
    y(i) = 5.9625; t(i) = 5.2500; i = i + 1
    y(i) = 5.6250; t(i) = 5.7500; i = i + 1
    y(i) = 81.5000; t(i) = .5000; i = i + 1
    y(i) = 62.4000; t(i) = .7500; i = i + 1
    y(i) = 32.5000; t(i) = 1.5000; i = i + 1
    y(i) = 12.4100; t(i) = 3.0000; i = i + 1
    y(i) = 13.1200; t(i) = 3.0000; i = i + 1
    y(i) = 15.5600; t(i) = 3.0000; i = i + 1
    y(i) = 5.6300; t(i) = 6.0000; i = i + 1
    y(i) = 78.0000; t(i) = .5000; i = i + 1
    y(i) = 59.9000; t(i) = .7500; i = i + 1
    y(i) = 33.2000; t(i) = 1.5000; i = i + 1
    y(i) = 13.8400; t(i) = 3.0000; i = i + 1
    y(i) = 12.7500; t(i) = 3.0000; i = i + 1
    y(i) = 14.6200; t(i) = 3.0000; i = i + 1
    y(i) = 3.9400; t(i) = 6.0000; i = i + 1
    y(i) = 76.8000; t(i) = .5000; i = i + 1
    y(i) = 61.0000; t(i) = .7500; i = i + 1
    y(i) = 32.9000; t(i) = 1.5000; i = i + 1
    y(i) = 13.8700; t(i) = 3.0000; i = i + 1
    y(i) = 11.8100; t(i) = 3.0000; i = i + 1
    y(i) = 13.3100; t(i) = 3.0000; i = i + 1
    y(i) = 5.4400; t(i) = 6.0000; i = i + 1
    y(i) = 78.0000; t(i) = .5000; i = i + 1
    y(i) = 63.5000; t(i) = .7500; i = i + 1
    y(i) = 33.8000; t(i) = 1.5000; i = i + 1
    y(i) = 12.5600; t(i) = 3.0000; i = i + 1
    y(i) = 5.6300; t(i) = 6.0000; i = i + 1
    y(i) = 12.7500; t(i) = 3.0000; i = i + 1
    y(i) = 13.1200; t(i) = 3.0000; i = i + 1
    y(i) = 5.4400; t(i) = 6.0000; i = i + 1
    y(i) = 76.8000; t(i) = .5000; i = i + 1
    y(i) = 60.0000; t(i) = .7500; i = i + 1
    y(i) = 47.8000; t(i) = 1.0000; i = i + 1
    y(i) = 32.0000; t(i) = 1.5000; i = i + 1
    y(i) = 22.2000; t(i) = 2.0000; i = i + 1
    y(i) = 22.5700; t(i) = 2.0000; i = i + 1
    y(i) = 18.8200; t(i) = 2.5000; i = i + 1
    y(i) = 13.9500; t(i) = 3.0000; i = i + 1
    y(i) = 11.2500; t(i) = 4.0000; i = i + 1
    y(i) = 9.0000; t(i) = 5.0000; i = i + 1
    y(i) = 6.6700; t(i) = 6.0000; i = i + 1
    y(i) = 75.8000; t(i) = .5000; i = i + 1
    y(i) = 62.0000; t(i) = .7500; i = i + 1
    y(i) = 48.8000; t(i) = 1.0000; i = i + 1
    y(i) = 35.2000; t(i) = 1.5000; i = i + 1
    y(i) = 20.0000; t(i) = 2.0000; i = i + 1
    y(i) = 20.3200; t(i) = 2.0000; i = i + 1
    y(i) = 19.3100; t(i) = 2.5000; i = i + 1
    y(i) = 12.7500; t(i) = 3.0000; i = i + 1
    y(i) = 10.4200; t(i) = 4.0000; i = i + 1
    y(i) = 7.3100; t(i) = 5.0000; i = i + 1
    y(i) = 7.4200; t(i) = 6.0000; i = i + 1
    y(i) = 70.5000; t(i) = .5000; i = i + 1
    y(i) = 59.5000; t(i) = .7500; i = i + 1
    y(i) = 48.5000; t(i) = 1.0000; i = i + 1
    y(i) = 35.8000; t(i) = 1.5000; i = i + 1
    y(i) = 21.0000; t(i) = 2.0000; i = i + 1
    y(i) = 21.6700; t(i) = 2.0000; i = i + 1
    y(i) = 21.0000; t(i) = 2.5000; i = i + 1
    y(i) = 15.6400; t(i) = 3.0000; i = i + 1
    y(i) = 8.1700; t(i) = 4.0000; i = i + 1
    y(i) = 8.5500; t(i) = 5.0000; i = i + 1
    y(i) = 10.1200; t(i) = 6.0000; i = i + 1
    y(i) = 78.0000; t(i) = .5000; i = i + 1
    y(i) = 66.0000; t(i) = .6250; i = i + 1
    y(i) = 62.0000; t(i) = .7500; i = i + 1
    y(i) = 58.0000; t(i) = .8750; i = i + 1
    y(i) = 47.7000; t(i) = 1.0000; i = i + 1
    y(i) = 37.8000; t(i) = 1.2500; i = i + 1
    y(i) = 20.2000; t(i) = 2.2500; i = i + 1
    y(i) = 21.0700; t(i) = 2.2500; i = i + 1
    y(i) = 13.8700; t(i) = 2.7500; i = i + 1
    y(i) = 9.6700; t(i) = 3.2500; i = i + 1
    y(i) = 7.7600; t(i) = 3.7500; i = i + 1
    y(i) = 5.4400; t(i) = 4.2500; i = i + 1
    y(i) = 4.8700; t(i) = 4.7500; i = i + 1
    y(i) = 4.0100; t(i) = 5.2500; i = i + 1
    y(i) = 3.7500; t(i) = 5.7500; i = i + 1
    y(i) = 24.1900; t(i) = 3.0000; i = i + 1
    y(i) = 25.7600; t(i) = 3.0000; i = i + 1
    y(i) = 18.0700; t(i) = 3.0000; i = i + 1
    y(i) = 11.8100; t(i) = 3.0000; i = i + 1
    y(i) = 12.0700; t(i) = 3.0000; i = i + 1
    y(i) = 16.1200; t(i) = 3.0000; i = i + 1
    y(i) = 70.8000; t(i) = .5000; i = i + 1
    y(i) = 54.7000; t(i) = .7500; i = i + 1
    y(i) = 48.0000; t(i) = 1.0000; i = i + 1
    y(i) = 39.8000; t(i) = 1.5000; i = i + 1
    y(i) = 29.8000; t(i) = 2.0000; i = i + 1
    y(i) = 23.7000; t(i) = 2.5000; i = i + 1
    y(i) = 29.6200; t(i) = 2.0000; i = i + 1
    y(i) = 23.8100; t(i) = 2.5000; i = i + 1
    y(i) = 17.7000; t(i) = 3.0000; i = i + 1
    y(i) = 11.5500; t(i) = 4.0000; i = i + 1
    y(i) = 12.0700; t(i) = 5.0000; i = i + 1
    y(i) = 8.7400; t(i) = 6.0000; i = i + 1
    y(i) = 80.7000; t(i) = .5000; i = i + 1
    y(i) = 61.3000; t(i) = .7500; i = i + 1
    y(i) = 47.5000; t(i) = 1.0000; i = i + 1
    y(i) = 29.0000; t(i) = 1.5000; i = i + 1
    y(i) = 24.0000; t(i) = 2.0000; i = i + 1
    y(i) = 17.7000; t(i) = 2.5000; i = i + 1
    y(i) = 24.5600; t(i) = 2.0000; i = i + 1
    y(i) = 18.6700; t(i) = 2.5000; i = i + 1
    y(i) = 16.2400; t(i) = 3.0000; i = i + 1
    y(i) = 8.7400; t(i) = 4.0000; i = i + 1
    y(i) = 7.8700; t(i) = 5.0000; i = i + 1
    y(i) = 8.5100; t(i) = 6.0000; i = i + 1
    y(i) = 66.7000; t(i) = .5000; i = i + 1
    y(i) = 59.2000; t(i) = .7500; i = i + 1
    y(i) = 40.8000; t(i) = 1.0000; i = i + 1
    y(i) = 30.7000; t(i) = 1.5000; i = i + 1
    y(i) = 25.7000; t(i) = 2.0000; i = i + 1
    y(i) = 16.3000; t(i) = 2.5000; i = i + 1
    y(i) = 25.9900; t(i) = 2.0000; i = i + 1
    y(i) = 16.9500; t(i) = 2.5000; i = i + 1
    y(i) = 13.3500; t(i) = 3.0000; i = i + 1
    y(i) = 8.6200; t(i) = 4.0000; i = i + 1
    y(i) = 7.2000; t(i) = 5.0000; i = i + 1
    y(i) = 6.6400; t(i) = 6.0000; i = i + 1
    y(i) = 13.6900; t(i) = 3.0000; i = i + 1
    y(i) = 81.0000; t(i) = .5000; i = i + 1
    y(i) = 64.5000; t(i) = .7500; i = i + 1
    y(i) = 35.5000; t(i) = 1.5000; i = i + 1
    y(i) = 13.3100; t(i) = 3.0000; i = i + 1
    y(i) = 4.8700; t(i) = 6.0000; i = i + 1
    y(i) = 12.9400; t(i) = 3.0000; i = i + 1
    y(i) = 5.0600; t(i) = 6.0000; i = i + 1
    y(i) = 15.1900; t(i) = 3.0000; i = i + 1
    y(i) = 14.6200; t(i) = 3.0000; i = i + 1
    y(i) = 15.6400; t(i) = 3.0000; i = i + 1
    y(i) = 25.5000; t(i) = 1.7500; i = i + 1
    y(i) = 25.9500; t(i) = 1.7500; i = i + 1
    y(i) = 81.7000; t(i) = .5000; i = i + 1
    y(i) = 61.6000; t(i) = .7500; i = i + 1
    y(i) = 29.8000; t(i) = 1.7500; i = i + 1
    y(i) = 29.8100; t(i) = 1.7500; i = i + 1
    y(i) = 17.1700; t(i) = 2.7500; i = i + 1
    y(i) = 10.3900; t(i) = 3.7500; i = i + 1
    y(i) = 28.4000; t(i) = 1.7500; i = i + 1
    y(i) = 28.6900; t(i) = 1.7500; i = i + 1
    y(i) = 81.3000; t(i) = .5000; i = i + 1
    y(i) = 60.9000; t(i) = .7500; i = i + 1
    y(i) = 16.6500; t(i) = 2.7500; i = i + 1
    y(i) = 10.0500; t(i) = 3.7500; i = i + 1
    y(i) = 28.9000; t(i) = 1.7500; i = i + 1
    y(i) = 28.9500; t(i) = 1.7500; i = i + 1

  end
end module chwirut1fmodule

program main
  use chwirut1fmodule
  implicit none
! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
!                   Variable declarations
! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
!
!  See additional variable declarations in the file chwirut1f.h

  PetscErrorCode ierr    ! used to check for functions returning nonzeros
  Vec x       ! solution vector
  Vec f       ! vector of functions
  Tao ta     ! Tao context
  PetscInt nhist
  PetscMPIInt size, rank    ! number of processes running
  PetscReal hist(100) ! objective value history
  PetscReal resid(100)! residual history
  PetscReal cnorm(100)! cnorm history
  PetscInt lits(100)   ! #ksp history
  PetscInt oh
  TaoConvergedReason reason

!  Initialize TAO and PETSc
  PetscCallA(PetscInitialize(ierr))

  PetscCallMPIA(MPI_Comm_size(PETSC_COMM_WORLD, size, ierr))
  PetscCallMPIA(MPI_Comm_rank(PETSC_COMM_WORLD, rank, ierr))
  PetscCheckA(size == 1, PETSC_COMM_SELF, PETSC_ERR_WRONG_MPI_SIZE, 'This is a uniprocessor example only')

!  Initialize problem parameters
  m = 214
  n = 3

!  Allocate vectors for the solution and gradient
  PetscCallA(VecCreateSeq(PETSC_COMM_SELF, n, x, ierr))
  PetscCallA(VecCreateSeq(PETSC_COMM_SELF, m, f, ierr))

!  The TAO code begins here

!  Create TAO solver
  PetscCallA(TaoCreate(PETSC_COMM_SELF, ta, ierr))
  PetscCallA(TaoSetType(ta, TAOPOUNDERS, ierr))
!  Set routines for function, gradient, and hessian evaluation

  PetscCallA(TaoSetResidualRoutine(ta, f, FormFunction, 0, ierr))

!  Optional: Set initial guess
  call InitializeData()
  call FormStartingPoint(x)
  PetscCallA(TaoSetSolution(ta, x, ierr))

!  Check for TAO command line options
  PetscCallA(TaoSetFromOptions(ta, ierr))
  oh = 100
  PetscCallA(TaoSetConvergenceHistory(ta, hist, resid, cnorm, lits, oh, PETSC_TRUE, ierr))
!  SOLVE THE APPLICATION
  PetscCallA(TaoSolve(ta, ierr))
  PetscCallA(TaoGetConvergenceHistory(ta, nhist, ierr))
  PetscCallA(TaoGetConvergedReason(ta, reason, ierr))
  if (reason%v <= 0) then
    print *, 'Tao failed.'
    print *, 'Try a different TAO method, adjust some parameters,'
    print *, 'or check the function evaluation routines.'
  end if

!  Free TAO data structures
  PetscCallA(TaoDestroy(ta, ierr))

!  Free PETSc data structures
  PetscCallA(VecDestroy(x, ierr))
  PetscCallA(VecDestroy(f, ierr))

  PetscCallA(PetscFinalize(ierr))

end

!/*TEST
!
!   build:
!      requires: !complex
!
!   test:
!      args: -tao_monitor_short -tao_max_it 100 -tao_type pounders -tao_gatol 1.e-5
!      requires: !single
!
!TEST*/