root(method=’lm’)¶
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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.