# scipy.optimize.check_grad

scipy.optimize.check_grad(func, grad, x0, *args, **kwargs)

  Check the correctness of a gradient function by comparing it against a (forward) finite-difference approximation of the gradient.

  Parameters
    func callable `func(x0, *args)`

      Function whose derivative is to be checked.

    grad callable `grad(x0, *args)`

      Gradient of func.

    x0 ndarray

      Points to check grad against forward difference approximation of grad using func.

    args*args, optional

      Extra arguments passed to func and grad.

    epsilon float, optional

      Step size used for the finite difference approximation. It defaults to `sqrt(np.finfo(float).eps)`, which is approximately 1.49e-08.

  Returns
    err float

      The square root of the sum of squares (i.e., the 2-norm) of the difference between `grad(x0, *args)` and the finite difference approximation of grad using func at the points x0.

  See also

  `approx_fprime`

  Examples

      >>> def func(x):
      ...     return x[0]**2 - 0.5 * x[1]**3
      >>> def grad(x):
      ...     return [2 * x[0], -1.5 * x[1]**2]
      >>> from scipy.optimize import check_grad
      >>> check_grad(func, grad, [1.5, -1.5])
      2.9802322387695312e-08

scipy.optimize.approx_fprime scipy.optimize.bracket