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).
See also
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.