# root(method=’hybr’)

scipy.optimize.root(fun, x0, args=(), method='hybr', jac=None, tol=None, callback=None, options=None)

  Find the roots of a multivariate function using MINPACK’s hybrd and hybrj routines (modified Powell method).

  See also

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

  Options:
    ——-
    col_deriv bool

      Specify whether the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation).

    xtol float

      The calculation will terminate if the relative error between two consecutive iterates is at most xtol.

    maxfev 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.

    band tuple

      If set to a two-sequence containing the number of sub- and super-diagonals within the band of the Jacobi matrix, the Jacobi matrix is considered banded (only for `fprime=None`).

    eps float

      A suitable step length for the forward-difference approximation of the Jacobian (for `fprime=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.

    factor float

      A parameter determining the initial step bound (`factor * || diag * x||`). Should be in the interval `(0.1, 100)`.

    diag sequence

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