# root(method=’hybr’)¶

scipy.optimize.root(fun, x0, args=(), method='hybr', jac=None, tol=None, callback=None, options={'func': None, 'col_deriv': 0, 'xtol': 1.49012e-08, 'maxfev': 0, 'band': None, 'eps': None, 'factor': 100, 'diag': None})

Find the roots of a multivariate function using MINPACK’s hybrd and hybrj routines (modified Powell method).

For documentation for the rest of the parameters, see scipy.optimize.root
Options: col_deriv : bool Specify whether the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation). xtol : float The calculation will terminate if the relative error between two consecutive iterates is at most xtol. maxfev : int The maximum number of calls to the function. If zero, then 100*(N+1) is the maximum where N is the number of elements in x0. band : tuple If set to a two-sequence containing the number of sub- and super-diagonals within the band of the Jacobi matrix, the Jacobi matrix is considered banded (only for fprime=None). eps : float A suitable step length for the forward-difference approximation of the Jacobian (for fprime=None). If eps is less than the machine precision, it is assumed that the relative errors in the functions are of the order of the machine precision. factor : float A parameter determining the initial step bound (factor * || diag * x||). Should be in the interval (0.1, 100). diag : sequence N positive entries that serve as a scale factors for the variables.