root(method=’hybr’)¶
- scipy.optimize.root(fun, x0, args=(), method='hybr', jac=None, tol=None, callback=None, options={'col_deriv': 0, 'diag': None, 'factor': 100, 'eps': None, 'band': None, 'func': None, 'maxfev': 0, 'xtol': 1.49012e-08})
- Find the roots of a multivariate function using MINPACK’s hybrd and hybrj routines (modified Powell method). - See also - 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. 
