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Re: Algorithmic Differentiation in Octave

From: Olaf Till
Subject: Re: Algorithmic Differentiation in Octave
Date: Tue, 24 Jan 2017 19:03:39 +0100
User-agent: Mutt/1.5.23 (2014-03-12)

On Tue, Jan 24, 2017 at 08:35:09AM -0700, Brad Bell wrote:
> I have created a link from Cppad
> to Octave (and some other languages) through Swig. See
> Also see the getting started example for Octave
> Feel free to contact me if you have any questions about it.
> Brad.

I thought of implementing AD myself for Octave some time... The
natural concept seems to me to have classes whose objects
(representing vectors) you can give as arguments to a general scripted
function (restriction: function must only use operations which are
overloaded for the object) making this function return the
derivatives. Your concept seems to be different, and more complicated
for application -- it seems to involve creation of a separate
representation of the function code ('ay(0) = ax_0 * ax_1 * ax_2; af =
m_cppad.a_fun(ax, ay);' in your example)...?

For an important application (optimizing dynamic models which are
solved by providing sensitivity equations) we'd also need second
derivatives... And for efficiency, it would be good to be able to mark
some second derivatives which are not needed...


public key id EAFE0591, e.g. on x-hkp://

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