/usr/local/sage/sage-6.2/src/bin/sage-ipython:281: OptimizeWarning: Unknown solver options: verbose

p2 = scipy.optimize.root(FEXP2, guess,(p0,p1),method='anderson',options={'verbose':True})

>>> from scipy.optimize import *
>>> show_options('root', 'anderson')

Anderson 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 guess for the Jacobian is (-1/alpha).
                M : float, optional
                    Number of previous vectors to retain. Defaults to 5.
                w0 : float, optional
                    Regularization parameter for numerical stability.
                    Compared to unity, good values of the order of 0.01.