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# root(method=’excitingmixing’)

`scipy.optimize.``root`(fun, x0, args=(), method='excitingmixing', 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.

  > 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]`.