`scipy.optimize._root:_root_leastsq`

source: build-install/usr/lib/python3.14/site-packages/scipy/optimize/_root.py:280

Signature

def _root_leastsq(fun, x0, args = (), jac = None, col_deriv = 0, xtol = 1.49012e-08, ftol = 1.49012e-08, gtol = 0.0, maxiter = 0, eps = 0.0, factor = 100, diag = None, **unknown_options)

Summary

Solve for least squares with Levenberg-Marquardt

Parameters

col_deriv : bool: non-zero to specify that the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation).
ftol : float: Relative error desired in the sum of squares.
xtol : float: Relative error desired in the approximate solution.
gtol : float: Orthogonality desired between the function vector and the columns of the Jacobian.
maxiter : 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.
eps : float: A suitable step length for the forward-difference approximation of the Jacobian (for Dfun=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 interval (0.1, 100).
diag : sequence: N positive entries that serve as a scale factors for the variables.

Aliases

scipy.optimize._root._root_leastsq