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# 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: | 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