scipy.optimize._root:_root_krylov_doc

Parameters

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.

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.

rdiff

rdiff

method

method

inner_M

inner_M

inner_rtol, inner_atol, inner_callback, ...

Parameters to pass on to the "inner" Krylov solver.

For a full list of options, see the documentation for the solver you are using. By default this is scipy.sparse.linalg.lgmres. If the solver has been overridden through method, see the documentation for that solver instead. To use an option for that solver, prepend inner_ to it. For example, to control the rtol argument to the solver, set the inner_rtol option here.

outer_k

outer_k