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address@hidden: Re: automatic differentiation]
From: |
Etienne Grossmann |
Subject: |
address@hidden: Re: automatic differentiation] |
Date: |
Wed, 25 Jan 2006 11:54:05 -0500 |
User-agent: |
Mutt/1.4.2.1i |
Was on address@hidden
On Wed, Jan 25, 2006 at 12:39:26AM +0100, Thomas Kasper wrote:
# Hi,
#
# am I right to assume that currently there is no support for automatic
# differentiation in Octave? Well then here is what could be a start...
#
# I wrote it - you guess - 'in the hope that it will be useful'.
# If you tell me it might in fact be, I'd be glad to contribute. If not, never
# mind.
#
# Included you find a brief example of how to use it.
#
# Regards,
# Thomas
#
# --
# 10 GB Mailbox, 100 FreeSMS/Monat http://www.gmx.net/de/go/topmail
# +++ GMX - die erste Adresse f??r Mail, Message, More +++
Hi Thomas,
thanks for this contribution, it seems interesting and I'm looking
into it. But, w/ my octave 2.9.3, ad_example croaks during 'newton'.
======================================================================
octave:2> help newton
help: sorry, `newton'is not documented
octave:3> which newton
newton is a user-defined function
octave:4> [x, steps] = newton ('F', x0, 1e-9)
*** glibc detected *** free(): invalid pointer: 0x08c33da8 ***
panic: Aborted -- stopping myself...
attempting to save variables to òctave-core'...
save to òctave-core'complete
GNU Octave, version 2.9.3 (i686-pc-linux-gnu).
======================================================================
I am unsure about what your functions do. What kind of object does
gradinit return? Does your code do algorithmic differentiation and
fast hessian-vector products, as in
Nicol N. Schraudolph. Fast Curvature Matrix-Vector Products for
Second-Order Gradient Descent. Neural Computation, 14(7):17231738,
2002. http://nic.schraudolph.org/bib2html/index.html
Cheers,
Etienne
--
Etienne Grossmann ------ http://www.cs.uky.edu/~etienne
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- address@hidden: Re: automatic differentiation],
Etienne Grossmann <=