Source code for naginterfaces.library.examples.opt.handle_solve_bounds_foas_ex

#!/usr/bin/env python3
"``naginterfaces.library.opt.handle_solve_bounds_foas`` Python Example."

# NAG Copyright 2018-2020.

# pylint: disable=invalid-name,line-too-long

from naginterfaces.base import utils
from naginterfaces.library import opt

def main():
    """
    Example for :func:`naginterfaces.library.opt.handle_solve_bounds_foas`.

    Large-scale first order active set bound-constrained nonlinear programming.

    Demonstrates using the ``FileObjManager`` class.

    >>> main()
    naginterfaces.library.opt.handle_solve_bounds_foas Python Example Results.
    Minimizing a bound-constrained Rosenbrock problem.
     E04KF, First order method for bound-constrained problems
     Begin of Options
    ...
     End of Options
    <BLANKLINE>
    <BLANKLINE>
     Status: converged, an optimal solution was found
     Value of the objective             4.00000E-02
    ...
     """

    print(
        'naginterfaces.library.opt.handle_solve_bounds_foas '
        'Python Example Results.'
    )
    print('Minimizing a bound-constrained Rosenbrock problem.')

    # The initial guess:
    x = [-1.5, 1.9]

    # The Rosenbrock objective:
    objfun = lambda x, inform: (
        (1. - x[0])**2 + 100.*(x[1] - x[0]**2)**2, inform,
    )

    def objgrd(x, fdx, inform):
        """The objective's gradient."""
        fdx[:] = [
            2.*x[0] - 400.*x[0]*(x[1]-x[0]**2) - 2.,
            200.*(x[1]-x[0]**2),
        ]
        return inform

    # Create a handle for the problem:
    nvar = len(x)
    handle = opt.handle_init(nvar)

    # Define the bounds:
    opt.handle_set_simplebounds(
        handle,
        bl=[-1., -2.],
        bu=[0.8, 2.],
    )

    # Define the nonlinear objective:
    opt.handle_set_nlnobj(handle, idxfd=list(range(1, nvar+1)))

    # Set some algorithmic options.
    for option in [
            'FOAS Print Frequency = 5',
            'Print Solution = yes',
            'Print Level = 1',
            'Monitoring Level = 3',
    ]:
        opt.handle_opt_set(handle, option)

    # Use an explicit I/O manager for abbreviated iteration output:
    iom = utils.FileObjManager(locus_in_output=False)
    # Output from handle_solve_bounds_foas is sent
    # to the NAG Library Engine advisory unit, which is associated with
    # standard output by default in the I/O manager instance.
    # To register a different file object to the advisory unit,
    # use the I/O manager's register_to_advunit method.

    # Solve the problem:
    opt.handle_solve_bounds_foas(
        handle, x,
        objfun=objfun, objgrd=objgrd, io_manager=iom,
    )

    # Destroy the handle:
    opt.handle_free(handle)

if __name__ == '__main__':
    import doctest
    import sys
    sys.exit(
        doctest.testmod(
            None, verbose=True, report=False,
            optionflags=doctest.ELLIPSIS | doctest.REPORT_NDIFF,
        ).failed
    )