root(method=’krylov’)¶
- scipy.optimize.root(fun, x0, args=(), method='krylov', 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.
- rdiff : float, optional
Relative step size to use in numerical differentiation.
- method : {‘lgmres’, ‘gmres’, ‘bicgstab’, ‘cgs’, ‘minres’} or function
Krylov method to use to approximate the Jacobian. Can be a string, or a function implementing the same interface as the iterative solvers in scipy.sparse.linalg.
The default is scipy.sparse.linalg.lgmres.
- inner_M : LinearOperator or InverseJacobian
Preconditioner for the inner Krylov iteration. Note that you can use also inverse Jacobians as (adaptive) preconditioners. For example,
>>> jac = BroydenFirst() >>> kjac = KrylovJacobian(inner_M=jac.inverse).
If the preconditioner has a method named ‘update’, it will be called as update(x, f) after each nonlinear step, with x giving the current point, and f the current function value.
- inner_tol, inner_maxiter, ...
Parameters to pass on to the “inner” Krylov solver. See scipy.sparse.linalg.gmres for details.
- outer_k : int, optional
Size of the subspace kept across LGMRES nonlinear iterations.
See scipy.sparse.linalg.lgmres for details.