root(method=’lm’)¶

scipy.optimize.root(fun, x0, args=(), method='lm', jac=None, tol=None, callback=None, options={'func': None, '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:

Options:	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. epsfcn* : float 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. 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.

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.
epsfcn : float: 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.
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.

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