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# root(method=’linearmixing’)

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:
    ——-
    nitint, optional

      Number of iterations to make. If omitted (default), make as many as required to meet tolerances.

    dispbool, optional

      Print status to stdout on every iteration.

    maxiterint, optional

      Maximum number of iterations to make.

    ftolfloat, optional

      Relative tolerance for the residual. If omitted, not used.

    fatolfloat, optional

      Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6.

    xtolfloat, optional

      Relative minimum step size. If omitted, not used.

    xatolfloat, 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_normfunction(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_optionsdict, optional

      Options for the respective Jacobian approximation.

      alphafloat, optional

        initial guess for the jacobian is (-1/alpha).