view doc/v2_planning/optimization.txt @ 1036:89e76e6e074f

XG added to optimization team
author Xavier Glorot <glorotxa@iro.umontreal.ca>
date Tue, 07 Sep 2010 12:08:37 -0400
parents a1b6ccd5b6dc
children baf1988db557
line wrap: on
line source

Discussion of Optimization-Related Issues
=========================================

Members: JB, PL, OD, XG

Representative: JB


Previous work - scikits, openopt, scipy  provide function optimization
algorithms.  These are not currently GPU-enabled but may be in the future.


IS PREVIOUS WORK SUFFICIENT?
--------------------------------

In many cases it is (I used it for sparse coding, and it was ok).

These packages provide batch optimization, whereas we typically need online
optimization.

It can be faster (to run) and more convenient (to implement) to have
optimization algorithms as Theano update expressions.


What optimization algorithms do we want/need?
---------------------------------------------

 - sgd 
 - sgd + momentum
 - sgd with annealing schedule
 - TONGA
 - James Marten's Hessian-free
 - Conjugate gradients, batch and (large) mini-batch [that is also what Marten's thing does]

Do we need anything to make batch algos work better with Pylearn things?
 - conjugate methods? yes
 - L-BFGS? maybe, when needed