# 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_derivbool

Specify whether the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation).

xtolfloat

The calculation will terminate if the relative error between two consecutive iterates is at most xtol.

maxfevint

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.

bandtuple

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`).

epsfloat

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.

factorfloat

A parameter determining the initial step bound (`factor * || diag * x||`). Should be in the interval `(0.1, 100)`.

diagsequence

N positive entries that serve as a scale factors for the variables.