`scipy.optimize._root:_root_leastsq`

source: /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