`scipy.optimize._minpack:_hybrj`

Signature

built-in_hybrj(fun, Dfun, x0, args, full_output, col_deriv, xtol, maxfev, factor, diag)

Hybrid method with user-supplied Jacobian for finding roots.

Solves fun(x) = 0 using a modification of the Powell hybrid method with analytical Jacobian.

fun : callable: Function to minimize. Takes x as first argument.
Dfun : callable: Jacobian of fun. Returns matrix of partial derivatives.
x0 : ndarray, shape (n,): Initial guess, float64.
args : tuple: Extra arguments passed to fun and Dfun.
full_output : int: Non-zero to return optional outputs.
col_deriv : int: Non-zero if Dfun returns column derivatives. Default: 1.
xtol : float: Relative tolerance for solution. Default: 1.49012e-8.
maxfev : int: Maximum number of function evaluations. Default: 100*(n+1).
factor : float: Initial step bound. Default: 100.0.
diag : ndarray, shape (n,) or None: Scaling factors for variables, float64.

x : ndarray, shape (n,): Solution vector.
infodict : dict: Dictionary with keys: fvec (ndarray), nfev (int), njev (int), fjac (ndarray), r (ndarray), qtf (ndarray). Only returned if full_output != 0.
info : int: Exit status. 1-4 indicate success.

If full_output is 0: returns (x, info) If full_output != 0: returns (x, infodict, info)