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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) 2016-2024 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/mystic/blob/master/LICENSE
"""
Example:
- Minimize Rosenbrock's Function with Powell's method.
Demonstrates:
- standard models
- minimal solver interface
- parameter constraints solver and constraints factory
- statistical parameter constraints
- customized monitors
"""
# Powell's Directonal solver
from mystic.solvers import fmin_powell
# Rosenbrock function
from mystic.models import rosen
# tools
from mystic.monitors import VerboseMonitor
from mystic.math.measures import mean, impose_mean
from mystic.math import almostEqual
if __name__ == '__main__':
print("Powell's Method")
print("===============")
# initial guess
x0 = [0.8,1.2,0.7]
# define constraints factory function
def constraints_factory(target):
# define constraints function
def constraints(x):
# constrain the last x_i to be the same value as the first x_i
x[-1] = x[0]
# constrain x such that mean(x) == target
if not almostEqual(mean(x), target):
x = impose_mean(target, x)
return x
return constraints
# configure constraints function
constraints = constraints_factory(1.0)
# configure monitor
stepmon = VerboseMonitor(1)
# use Powell's method to minimize the Rosenbrock function
solution = fmin_powell(rosen,x0,constraints=constraints,itermon=stepmon)
print(solution)
# end of file
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