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#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2025 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/pathos/blob/master/LICENSE
"""
Minimize the selected model with Powell's method.
Requires: development version of mystic
http://pypi.python.org/pypi/mystic
"""
def optimize(solver, target='rosen', **kwds):
if target == 'rosen': # 3d-rosenbrock
# Rosenbrock function
from dejong import rosen as the_model
ndim = 3
actual_coeffs = [1.0] * ndim
pprint = list
else: # 4th-order chebyshev
# Chebyshev cost function
from poly import chebyshev4cost as the_model
from poly import chebyshev4coeffs as actual_coeffs
ndim = len(actual_coeffs)
from mystic.math import poly1d as pprint
# number of trials
print("One trial:")
print("===============")
# initial guess
import random
x0 = [random.uniform(-100,100) for i in range(ndim)]
# minimize the function
results = the_solver(the_model, x0, **kwds)
print("===============")
print("Actual params:\n %s" % pprint(actual_coeffs))
print("Solved params:\n %s" % pprint(results[0]))
print("Function value: %s" % results[1])
print("Total function evals: %s" % results[3])
return
# Powell's Directonal solver
from optimize_helper import fmin_powell as the_solver
if __name__ == '__main__':
target = 'rosen'
#target = 'cheby'
print("Function: %s" % target)
print("Solver: %s" % 'fmin_powell')
optimize(the_solver, target=target)
#optimize(the_solver, target=target, monitor=True)
#optimize(the_solver, target=target, monitor=True, disp=False)
# end of file
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