[Numpy-discussion] numerical gradient, Jacobian, and Hessian

Eelco Hoogendoorn hoogendoorn.eelco at gmail.com
Mon Apr 21 03:13:33 EDT 2014

I was going to suggest numdifftools; its a very capable package in my
experience. Indeed it would be nice to have it integrated into scipy.

Also, in case trying to calculate a numerical gradient is a case of 'the
math getting too bothersome' rather than no closed form gradient actually
existing: Theano may be your best bet; I have very good experiences with it
as well. As far as I can tell, it is actually the only tensor/ndarray aware
differentiator out there (maple and mathematica don't appear to support

On Sun, Apr 20, 2014 at 4:55 PM, Alan G Isaac <alan.isaac at gmail.com> wrote:

> Awhile back there were good signs that SciPy
> would end up with a `diff` module:
> https://github.com/scipy/scipy/issues/2035
> Is this still moving forward?
> It would certainly be nice for SciPy to have intuitive
> numerical gradients, Jacobians, and Hessians.  The last
> two are I think missing altogether.  The first exists
> as scipy.optimize.approx_fprime.
> `approx_fprime` seems to work fine, but I suggest it
> has the following drawbacks:
> - it is hard to find (e.g., try doing a Google search
>    on "scipy gradient" or "scipy numerical gradient"
> - related, it is in the wrong location (scipy.optimize)
> - the signature is odd: (x,f,dx) instead of (f,x,dx)
>    (This matters for ease of recall and for teaching.)
> In any case, as I understand it, the author's of numdifftools
> http://code.google.com/p/numdifftools/
> expressed willingness to have their code moved into SciPy.
> This seems like an excellent way forward.
> There was talk of making this a summer of code project,
> but that seems to have sputtered.
> Alan Isaac
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