minimize(method=’COBYLA’)#

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

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:
——-
rhobegfloat

Reasonable initial changes to the variables.

tolfloat

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

dispbool

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

maxiterint

Maximum number of function evaluations.

catolfloat

Tolerance (absolute) for constraint violations