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statsmodels.tools.numdiff.approx_fprime_cs

statsmodels.tools.numdiff.approx_fprime_cs(x, f, epsilon=None, args=(), kwargs={})[source]

Calculate gradient or Jacobian with complex step derivative approximation

Parameters:

x : array

parameters at which the derivative is evaluated

f : function

f(*((x,)+args), **kwargs) returning either one value or 1d array

epsilon : float, optional

Stepsize, if None, optimal stepsize is used. Optimal step-size is EPS*x. See note.

args : tuple

Tuple of additional arguments for function f.

kwargs : dict

Dictionary of additional keyword arguments for function f.

Returns:

partials : ndarray

array of partial derivatives, Gradient or Jacobian

Notes

The complex-step derivative has truncation error O(epsilon**2), so truncation error can be eliminated by choosing epsilon to be very small. The complex-step derivative avoids the problem of round-off error with small epsilon because there is no subtraction.

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