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# Interface to Constrained Optimization By Linear Approximation
from __future__ import nested_scopes
import _cobyla
def fmin_cobyla(func, x0, cons, args=(), consargs=None, rhobeg=1.0, rhoend=1e-4,
iprint=1, maxfun=1000):
"""
Minimize a function using the Contrained Optimization BY Linear Approximation
(COBYLA) method
Arguments:
func -- function to minimize. Called as func(x, *args)
x0 -- initial guess to minimum
cons -- a list of functions that all must be >=0 (a single function
if only 1 constraint)
args -- extra arguments to pass to function
consargs -- extra arguments to pass to constraints (default of None means
use same extra arguments as those passed to func). Use () for no
extra arguments.
rhobeg -- reasonable initial changes to the variables
rhoend -- final accuracy in the optimization (not precisely guaranteed)
iprint -- controls the frequency of output: 0 (no output),1,2,3
maxfun -- maximum number of function evaluations.
Returns:
x -- the minimum
"""
err = "cons must be a list of callable functions or a single"\
" callable function."
n = len(x0)
if isinstance(cons, list):
m = len(cons)
for thisfunc in cons:
if not callable(thisfunc):
raise TypeError, err
elif callable(cons):
m = 1
cons = [cons]
else:
raise TypeError, "cons must be a list of callable functions or a single"\
" callable function."
if consargs is None:
consargs = args
def calcfc(x, con):
f = func(x, *args)
k = 0
for constraints in cons:
con[k] = constraints(x,*consargs)
k += 1
return f
xopt = _cobyla.minimize(calcfc, m=m, x=x0,rhobeg=rhobeg,rhoend=rhoend,iprint=iprint,
maxfun=maxfun)
return xopt
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