root(method=’diagbroyden’)¶
- scipy.optimize.root(fun, x0, args=(), method='diagbroyden', tol=None, callback=None, options={})
See also
For documentation for the rest of the parameters, see scipy.optimize.root
Options: nit : int, optional
Number of iterations to make. If omitted (default), make as many as required to meet tolerances.
disp : bool, optional
Print status to stdout on every iteration.
maxiter : int, optional
Maximum number of iterations to make. If more are needed to meet convergence, NoConvergence is raised.
ftol : float, optional
Relative tolerance for the residual. If omitted, not used.
fatol : float, optional
Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6.
xtol : float, optional
Relative minimum step size. If omitted, not used.
xatol : float, optional
Absolute minimum step size, as determined from the Jacobian approximation. If the step size is smaller than this, optimization is terminated as successful. If omitted, not used.
tol_norm : function(vector) -> scalar, optional
Norm to use in convergence check. Default is the maximum norm.
line_search : {None, ‘armijo’ (default), ‘wolfe’}, optional
Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to ‘armijo’.
jac_options : dict, optional
Options for the respective Jacobian approximation.
- alpha : float, optional
initial guess for the jacobian is (-1/alpha).