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# 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(numpy.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