root(method=’lm’)#
- scipy.optimize.root(fun, x0, args=(), method='hybr', jac=None, tol=None, callback=None, options=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. 
- epsfloat
- 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. 
- 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.