root(method=’linearmixing’)

scipy.optimize.root(fun, x0, args=(), method='linearmixing', tol=None, callback=None, options={})

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. If more are needed to meet convergence, NoConvergence is raised.

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).