NIPS*95 workshop on Optimization

[Jagota Arun Kumar]

Dear Connectionists:

Attached find a description of the NIPS*95 workshop on optimization.
For up-to-date information, including abstracts of talks, see the URL.

We might be able to fit in one or two more talks. Send me a title and
abstract by e-mail if you'd like to give a talk. 

Arun Jagota

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             OPTIMIZATION PROBLEM SOLVING WITH NEURAL NETS

                NIPS95 Workshop, Organizer: Arun Jagota

           Friday Dec 1 1995, 7:30--9:30 AM and 4:30--6:30 PM

                       E-mail: jagota at cs.unt.edu

      Workshop URL: http://www.msci.memphis.edu/~jagota/NIPS95

Ever since the work of Hopfield and Tank, neural nets have found increasing use 
in the approximate solution of difficult optimization problems, arising in many
applications. Such neural nets are well-suited in principle to these problems, 
because they minimize, in parallel form, an energy function into which an 
optimization problem's objective and constraints can be mapped. Unfortunately, 
often they haven't worked well in practice, for two reasons. First, mapping 
the objective and constraints of a problem onto a single good energy function 
has turned out difficult for certain problems, for example for the Travelling 
Salesman Problem. The ease or difficulty of mapping has turned out moreover 
to be problem-dependent, making it difficult to find a good general mapping 
methodology. Second, the dynamical algorithms have often been limited to some 
form of local search or gradient-descent. In recent years, there have been 
significant advances on both fronts. Provably good mappings of several 
optimization problems have been found. Powerful dynamical algorithms that go 
beyond gradient-descent have also been developed, with ideas borrowed from 
different fields. Examples are Mean Field Annealing, Simulated Annealing, 
Projection Methods, and Randomized Multi-Start Algorithms. This workshop aims 
to take stock of the state of the art on this topic, and to study directions
for future research and applications.

Target Audience

Both the topics---neural nets and optimization---are of relevance to a
wide range of disciplines and we hope that several of these will be 
represented at this workshop. These include Cognitive Science, Computer 
Science, Engineering, Mathematics, Neurobiology, Physics, Chemistry, and 
Psychology. 

Format

6-8 30 minute talks, each including 5 minutes for discussion. 30 minutes for
discussion at the end.

Talks

The Complexity of Stability in Hopfield Networks
Ian Parberry, University of North Texas 

Title to be announced
Anand Rangarajan, Yale University

Performance of Neural Network Algorithms for 
Maximum Clique on Highly Compressible Graphs
Arun Jagota, University of North Texas

Population-based Incremental Learning
Shumeet Baluja, Carnegie-Mellon University

How Good are Neural Networks Algorithms for the Travelling Salesman Problem?
Marco Budinich, Dipartimento di Fisica, Via Valerio 2, 34127 Trieste  ITALY               
Relaxation Labeling Networks for the Maximum Clique Problem
Marcello Pelillo, University of Venice, Italy
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