Challenge: Automatic differentiation in D

[Bill Baxter]

I actually was reminded about AD by someone's recent post on a SciPy
discussion list.
Here's another interesting link that appeared there questioning why AD
doesn't get more use in the machine learning community:
http://justindomke.wordpress.com/2009/02/17/automatic-differentiation-the-most-criminally-underused-tool-in-the-potential-machine-learning-toolbox/

And a followup posted by the same guy explaining the basics of reverse-mode AD:
http://justindomke.wordpress.com/2009/03/24/a-simple-explanation-of-reverse-mode-automatic-differentiation/

--bb

On Tue, May 12, 2009 at 11:31 AM, Steve Teale
<steve.teale at britseyeview.com> wrote:
> Bill Baxter Wrote:
>
>> > Bill,
>> >
>> > D4 maybe. In the present mood I think you are spitting in the wind!
>>
>> I'm just proposing it as a fun project if anyone is interested.
>> Shouldn't require any compiler changes.  Unless roadblocks are found
>> that require some compiler changes, in which case it's better to know
>> about those now than later on.   I see AD as a category of interesting
>> numerical techniques that a sufficiently advanced compiler can make
>> much less painful to use.  Like expression templates.  I think D2 has
>> most of what would be needed already.  The basic idea of AD (forward
>> AD, anyway) is pretty simple and quite elegant IMO, and worth learning
>> about anyway, for anyone interested in numerical computing,
>> computational physics, etc.
>>
>> --bb
>
> Bill,
>
> Has there been a discussion about expression templates - I have never been able to understand why they only apply to declarations. That sounds interesting.
>
> Steve
>
>
>
>