Source code for naginterfaces.library.examples.glopt.handle_solve_mcs_ex

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

# NAG Copyright 2021.

from math import exp
from naginterfaces.base import utils
from naginterfaces.library import opt
from naginterfaces.library import glopt

def main():
    """
    Example for :func:`naginterfaces.library.glopt.handle_solve_mcs`.

    Global optimization by multi-level coordinate search.

    >>> main()
    naginterfaces.library.glopt.handle_solve_mcs Python Example Results.
    Global optimization of the Peaks objective function.
    ...
    Final objective value is -6.55113
    Global optimum is (0.22828, -1.62553)
    """

    print(
        'naginterfaces.library.glopt.handle_solve_mcs Python Example Results.'
    )
    print('Global optimization of the Peaks objective function.')

    # Define the 'Peaks' functions
    objfun = lambda x, inform: (
        3.*(1. - x[0])**2*exp(-x[0]**2 - (x[1] + 1.)**2)
        - (10.*(x[0]/5. - x[0]**3 - x[1]**5)*exp(-x[0]**2 - x[1]**2))
        - 1./3.0*exp(-(x[0] + 1.)**2 - x[1]**2),
        inform,
    )

    # Create the problem in the NAG Optimization Modelling Suite.
    # Define an empty problem with 2 variables
    nvar = 2
    handle = opt.handle_init(nvar)

    # Define the objective for the problem as being nonlinear
    opt.handle_set_nlnobj(
        handle,
        idxfd=list(range(1, nvar+1)),
    )

    # Declare the simple bounds for the variables (both are in [-3., 3.])
    opt.handle_set_simplebounds(
        handle,
        bl=[-3., -3.],
        bu=[3., 3.],
    )

    # Set some optional parameters
    for option in [
            'MCS Max Objective Calls = 100000',
            'MCS Print Frequency = 10',
            'Print Solution = yes',
    ]:
        opt.handle_opt_set(handle, option)

    # Use an explicit I/O manager for abbreviated iteration output
    iom = utils.FileObjManager(locus_in_output=False)

    # Solve the problem
    soln = glopt.handle_solve_mcs(handle, objfun=objfun, io_manager=iom)

    # Destroy the handle
    opt.handle_free(handle)

    # Extract the solution and print it
    objval = soln.rinfo[0]
    print('Final objective value is {:.5f}'.format(objval))
    print(
        'Global optimum is (' +
        ', '.join(['{:.5f}'.format(xi) for xi in soln.x]) +
        ')'
    )

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