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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/

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
# +++ 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,



Etienne Grossmann ------

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