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:

Options:	disp : bool Set to True to print convergence messages. maxiter, maxfev : int Maximum allowed number of iterations and function evaluations. Will default to `N200`, 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.

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.