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minimize(method=’Powell’)

# minimize(method=’Nelder-Mead’)

`scipy.optimize.``minimize`(fun, x0, args=(), method='Nelder-Mead', tol=None, callback=None, options={'disp': False, 'initial_simplex': None, 'maxiter': None, 'xatol': 0.0001, 'return_all': False, 'fatol': 0.0001, 'func': None, 'maxfev': None})

  Minimization of scalar function of one or more variables using the Nelder-Mead algorithm.

  See also

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

  Options:

  disp : bool

  > Set to True to print convergence messages.

  maxiter, maxfev : int

  > Maximum allowed number of iterations and function evaluations. Will default to `N*200`, where `N` is the number of variables, if neither maxiter or maxfev is set. If both maxiter and maxfev are set, minimization will stop at the first reached.

  initial_simplex : array_like of shape (N + 1, N)

  > Initial simplex. If given, overrides x0. `initial_simplex[j,:]` should contain the coordinates of the j-th vertex of the `N+1` vertices in the simplex, where `N` is the dimension.

  xatol : float, optional

  > Absolute error in xopt between iterations that is acceptable for convergence.

  fatol : number, optional

  > Absolute error in func(xopt) between iterations that is acceptable for convergence.