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"""Interface to Constrained Optimization By Linear Approximation
Functions:
fmin_coblya(func, x0, cons, args=(), consargs=None, rhobeg=1.0, rhoend=1e-4,
iprint=1, maxfun=1000)
Minimize a function using the Constrained Optimization BY Linear
Approximation (COBYLA) method
"""
import _cobyla
from numpy import copy
def fmin_cobyla(func, x0, cons, args=(), consargs=None, rhobeg=1.0, rhoend=1e-4,
iprint=1, maxfun=1000):
"""
Minimize a function using the Constrained Optimization BY Linear
Approximation (COBYLA) method
Arguments:
func -- function to minimize. Called as func(x, *args)
x0 -- initial guess to minimum
cons -- a sequence 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
See also:
scikits.openopt, which offers a unified syntax to call this and other solvers
fmin, fmin_powell, fmin_cg,
fmin_bfgs, fmin_ncg -- multivariate local optimizers
leastsq -- nonlinear least squares minimizer
fmin_l_bfgs_b, fmin_tnc,
fmin_cobyla -- constrained multivariate optimizers
anneal, brute -- global optimizers
fminbound, brent, golden, bracket -- local scalar minimizers
fsolve -- n-dimenstional root-finding
brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding
fixed_point -- scalar fixed-point finder
"""
err = "cons must be a sequence of callable functions or a single"\
" callable function."
try:
m = len(cons)
except TypeError:
if callable(cons):
m = 1
cons = [cons]
else:
raise TypeError(err)
else:
for thisfunc in cons:
if not callable(thisfunc):
raise TypeError(err)
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=copy(x0), rhobeg=rhobeg, rhoend=rhoend,
iprint=iprint, maxfun=maxfun)
return xopt
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