minimize(method=’COBYLA’)¶

scipy.optimize.minimize(fun, x0, args=(), method='COBYLA', constraints=(), tol=None, callback=None, options={'iprint': 1, 'disp': False, 'maxiter': 1000, 'catol': 0.0002, 'rhobeg': 1.0})

Minimize a scalar function of one or more variables using the Constrained Optimization BY Linear Approximation (COBYLA) algorithm.

See also

For documentation for the rest of the parameters, see scipy.optimize.minimize

Options:

Options:	rhobeg : float Reasonable initial changes to the variables. tol : float Final accuracy in the optimization (not precisely guaranteed). This is a lower bound on the size of the trust region. disp : bool Set to True to print convergence messages. If False, verbosity is ignored as set to 0. maxiter : int Maximum number of function evaluations. catol : float Tolerance (absolute) for constraint violations

rhobeg : float

Reasonable initial changes to the variables.

tol : float

Final accuracy in the optimization (not precisely guaranteed). This is a lower bound on the size of the trust region.

disp : bool

Set to True to print convergence messages. If False, verbosity is ignored as set to 0.

maxiter : int

Maximum number of function evaluations.

catol : float

Tolerance (absolute) for constraint violations

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