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#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 2009-2016 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
# MPI config
#nnodes = 4 # = npop
nnodes = '4:core4:ppn=1' # = npop
# QUEUE config
queue = 'weekdayQ'
timelimit = '00:10'
def func_pickle(func, suffix='.pik', dir='.'):
""" standard pickle.dump of function to a NamedTemporaryFile """
from dill.temp import dump
return dump(func, suffix=suffix, dir=dir)
#######################################################################
# the steepest descent optimizer
#######################################################################
def local_optimize(cost,x0,lb,ub):
from mystic.solvers import PowellDirectionalSolver
from mystic.termination import NormalizedChangeOverGeneration as NCOG
from mystic.monitors import VerboseMonitor, Monitor
maxiter = 1000
maxfun = 1e+6
convergence_tol = 1e-4
#def func_unpickle(filename):
# """ standard pickle.load of function from a File """
# import dill as pickle
# return pickle.load(open(filename,'r'))
#stepmon = VerboseMonitor(100)
stepmon = Monitor()
evalmon = Monitor()
ndim = len(lb)
solver = PowellDirectionalSolver(ndim)
solver.SetInitialPoints(x0)
solver.SetStrictRanges(min=lb,max=ub)
solver.SetEvaluationLimits(maxiter,maxfun)
solver.SetEvaluationMonitor(evalmon)
solver.SetGenerationMonitor(stepmon)
tol = convergence_tol
#cost = func_unpickle(cost) #XXX: regenerate cost function from file
solver.Solve(cost, termination=NCOG(tol))
solved_params = solver.bestSolution
solved_energy = solver.bestEnergy
func_evals = solver.evaluations
return solved_params, solved_energy, func_evals
# EOF
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