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#!/usr/bin/env python
#
# Problem definition:
# A-R Hedar and M Fukushima, "Derivative-Free Filter Simulated Annealing
# Method for Constrained Continuous Global Optimization", Journal of
# Global Optimization, 35(4), 521-549 (2006).
#
# Original Matlab code written by A. Hedar (Nov. 23, 2005)
# http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/go.htm
# and ported to Python by Mike McKerns (December 2014)
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2024 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/mystic/blob/master/LICENSE
def objective(x):
x0,x1 = x
return x0**2 + (x1 - 1)**2
bounds = [(-1,1)]*2
# with penalty='penalty' applied, solution is:
xs, xs_ = [(0.5)**.5, 0.5], [-(0.5)**.5, 0.5]
ys = 0.75
from mystic.symbolic import generate_constraint, generate_solvers, solve
from mystic.symbolic import generate_penalty, generate_conditions
equations = """
x1 - x0**2 = 0.0
"""
cf = generate_constraint(generate_solvers(solve(equations, target='x1')))
pf = generate_penalty(generate_conditions(equations), k=1e12)
if __name__ == '__main__':
from mystic.solvers import diffev2
from mystic.math import almostEqual
result = diffev2(objective, x0=bounds, bounds=bounds, constraints=cf, npop=40, disp=False, full_output=True)
assert almostEqual(result[0], xs, tol=1e-2) \
or almostEqual(result[0], xs_, tol=1e-2)
assert almostEqual(result[1], ys, rel=1e-2)
# EOF
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