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root(method=’lm’)

# root(method=’hybr’)

`scipy.optimize.``root`(fun, x0, args=(), method='hybr', jac=None, tol=None, callback=None, options={'col_deriv': 0, 'diag': None, 'factor': 100, 'eps': None, 'band': None, 'func': None, 'maxfev': 0, 'xtol': 1.49012e-08})

  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.