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.te...@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 > > > >
