# 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
    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

minimize(method=’TNC’) minimize(method=’SLSQP’)