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.
  
          alpha : float, optional
              Initial Jacobian approximation is (-1/alpha).
          alphamax : float, optional
              The entries of the diagonal Jacobian are kept in the range
              ``[alpha, alphamax]``.
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_excitingmixing_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.
  
          alpha : float, optional
              Initial Jacobian approximation is (-1/alpha).
          alphamax : float, optional
              The entries of the diagonal Jacobian are kept in the range
              ``[alpha, alphamax]``.
      """
      pass
function_tokens: def _root_excitingmixing_doc ( ) : pass