Can't use DIRECT method

[fedebenelli]

Hello! Thank you so much for developing this project, is helping me a lot in my research.

Until now I used local optimizers with no problem, but now that I'm trying to use a global optimizer I'm facing some troubles that I can't find solution to.

I'm trying to use the DIRECT method, which also happened with other global optimizers. No matter how many parameters I set up, I always get an NLOPT_INVALID_ARGS code. Here is a minimal script to replicate this, in the script I only set bounds but I've tried every method that starts with set_. Am I doing something wrong?

module myfoo
  use iso_fortran_env, only: wp => real64
  implicit none

contains

  function foo(x, gradient, func_data) result(f)
    real(wp), intent(in) :: x(:)
    real(wp), intent(in out), optional :: gradient(:)
    class(*), intent(in), optional :: func_data
    real(wp) :: f

    f = x(1)**2 + x(2)**2

  end function foo
end module

program main
  use myfoo, only: foo, wp
  use nlopt_wrap, only: create, destroy, nlopt_opt, nlopt_algorithm_enum
  use nlopt_callback, only: nlopt_func, create_nlopt_func
  implicit none

  type(nlopt_opt) :: opt
  type(nlopt_func) :: f

  real(wp) :: x(2), fval
  integer :: stat

  X = [2, 5]

  f = create_nlopt_func(foo)
  ! opt = nlopt_opt(nlopt_algorithm_enum%LN_NELDERMEAD, 2)
  opt = nlopt_opt(nlopt_algorithm_enum%GN_DIRECT, 2)

  call opt%set_min_objective(f)

  call opt%set_lower_bounds([-1._wp, -1._wp])
  call opt%set_upper_bounds([1._wp, 1._wp])

  call opt%optimize(x, fval, stat)
  print *, X
  print *, fval, stat

end program main

[awvwgk]

From the example code posted the main issue I am spotting is that the trial vector X is already above the upper bounds of the optimizer. This could be for this particular example a reason for failure.

I will try to set some time on the weekend to check in more detail.

[awvwgk]

This works fine for me:

module myfoo
  use iso_fortran_env, only: wp => real64
  implicit none

contains

  function foo(x, gradient, func_data) result(f)
    real(wp), intent(in) :: x(:)
    real(wp), intent(in out), optional :: gradient(:)
    class(*), intent(in), optional :: func_data
    real(wp) :: f

    f = x(1)**2 + x(2)**2
    print *, "[", x, "]", f

  end function foo
end module

program main
  use myfoo, only: foo, wp
  use nlopt_wrap, only: create, destroy, nlopt_opt, nlopt_algorithm_enum
  use nlopt_callback, only: nlopt_func, create_nlopt_func
  use nlopt_enum, only: result_to_string
  implicit none

  type(nlopt_opt) :: opt
  type(nlopt_func) :: f

  real(wp) :: x(2), fval
  integer :: stat

  X = [.2, .5]

  f = create_nlopt_func(foo)
  ! opt = nlopt_opt(nlopt_algorithm_enum%LN_NELDERMEAD, 2)
  opt = nlopt_opt(nlopt_algorithm_enum%GN_DIRECT, 2)

  call opt%set_min_objective(f)

  call opt%set_lower_bounds([-1._wp, -1._wp])
  call opt%set_upper_bounds([1._wp, 1._wp])

  call opt%set_maxeval(200)

  call opt%optimize(x, fval, stat)
  print *, X
  print *, fval, result_to_string(stat)

end program main

Closing this as resolved, feel free to reopen.

[fedebenelli]

Thanks for the help! I did not know that detail about the initial vector, makes sense