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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 Nelder-Mead.
- Plot of parameter convergence to function minimum.
Demonstrates:
- standard models
- minimal solver interface
- parameter trajectories using retall
"""
# Nelder-Mead solver
from mystic.solvers import fmin
# Rosenbrock function
from mystic.models import rosen
# tools
import matplotlib.pyplot as plt
if __name__ == '__main__':
# initial guess
x0 = [0.8,1.2,0.7]
# use Nelder-Mead to minimize the Rosenbrock function
solution = fmin(rosen,x0,disp=0,retall=1)
allvecs = solution[-1]
# plot the parameter trajectories
plt.plot([i[0] for i in allvecs])
plt.plot([i[1] for i in allvecs])
plt.plot([i[2] for i in allvecs])
# draw the plot
plt.title("Rosenbrock parameter convergence")
plt.xlabel("Nelder-Mead solver iterations")
plt.ylabel("parameter value")
plt.legend(["x", "y", "z"])
plt.show()
# end of file
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