parameters: ()
docstring: 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.
docstring_summary: 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.
docstring_tokens: 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 .
function: def _root_krylov_doc():
      """
      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.
      """
      pass
function_tokens: def _root_krylov_doc ( ) : pass