scipy.optimize.fmin_cobyla

scipy.optimize.fmin_cobyla(func, x0, cons, args=(), consargs=None, rhobeg=1.0, rhoend=0.0001, 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