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# root(method=’krylov’)

scipy.optimize.root(fun, x0, args=(), method='hybr', jac=None, tol=None, callback=None, options=None)

  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.

    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.

      rdifffloat, optional

        Relative step size to use in numerical differentiation.

      methodstr or callable, optional

        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`. If a string, needs to be one of: `'lgmres'`, `'gmres'`, `'bicgstab'`, `'cgs'`, `'minres'`, `'tfqmr'`.

        The default is `scipy.sparse.linalg.lgmres`.

      inner_MLinearOperator 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_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_kint, optional

        Size of the subspace kept across LGMRES nonlinear iterations.

        See `scipy.sparse.linalg.lgmres` for details.