root(method=’lm’)#

scipy.optimize.root(fun, x0, args=(), method='lm', jac=None, tol=None, callback=None, options={'col_deriv': 0, 'xtol': 1.49012e-08, 'ftol': 1.49012e-08, 'gtol': 0.0, 'maxiter': 0, 'eps': 0.0, 'factor': 100, 'diag': None})

Solve for least squares with Levenberg-Marquardt

See also

For documentation for the rest of the parameters, see scipy.optimize.root

Options:
——-
col_derivbool

non-zero to specify that the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation).

ftolfloat

Relative error desired in the sum of squares.

xtolfloat

Relative error desired in the approximate solution.

gtolfloat

Orthogonality desired between the function vector and the columns of the Jacobian.

maxiterint

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.

epsfcnfloat

A suitable step length for the forward-difference approximation of the Jacobian (for Dfun=None). If epsfcn 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 interval (0.1, 100).

diagsequence

N positive entries that serve as a scale factors for the variables.