`scipy.optimize._minpack_py:_root_hybr`

source: /scipy/optimize/_minpack_py.py :194

Signature

   def     _root_hybr (    func    ,    x0    ,    args    =  ()   ,    jac    =  None   ,    col_deriv    =  0   ,    xtol    =  1.49012e-08   ,    maxfev    =  0   ,    band    =  None   ,    eps    =  None   ,    factor    =  100   ,    diag    =  None   ,   ** unknown_options      )

Summary

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

Parameters

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 jac=None).
eps : float: A suitable step length for the forward-difference approximation of the Jacobian (for jac=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.

Aliases

scipy.optimize._minpack_py._root_hybr