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.