Sunday, October 23, 2005

Algorithms and Technologies, and ESA 2006

I recently attended a talk on convex programming by Stephen Boyd. He made the interesting point that least-squares minimization, linear programming, and even semidefinite programming are now "technologies", in that they have a well defined theory and polynomial time solutions, efficient black box code that works for very large sized inputs, and overall, a very well worked out understanding of what variants to use for what kinds of problem instances.

What this means is that tomorrow if I have to suggest to someone that their problem can be formulated as a convex program, this not only gives them a solution "in theory", but gives them a solution "in practice", that they can actually implement. Such happenings are surprisingly rare when applying theoretical methods to real-world problems; it is not often that one can invoke a sophisticated algorithm that has known worst-case bounds, and find an implementation for it.

Today is the FOCS panel discussion on 'Exciting the public about (theoretical) computer science'. One way (though certainly not the only way) of exciting people about theory is to demonstrate the power of mathematical rigor and worst-case analysis by showing how algorithms that have good theoretical guarantees actually work well in practice: in other words, showing that algorithms can become "technologies".

This is a really long and roundabout way of mentioning a call for papers: ESA 2006 will be held in Zurich from Sep 11-15, 2006. ESA has a both a theoretical and applied track (disclaimer: I am on the PC for the applied track), and this year the applied track is trying a new approach to submissions:
Authors of papers that include an experimental aspect can make supporting source code or data sets available on a webpage (referenced in the submitted paper) or send them by e-mail to; such supporting files will be considered by the programcommittee members at their discretion.
So get your source code ready ! And remember this is not about theory making itself "useful", but about demonstrating that the formal approach to algorithm design is far more effective than any other approach.

1 comment:

  1. " is not often that one can invoke a sophisticated algorithm that has known worst-case bounds, and find an implementation for it."

    I find it intriguing that the sophisticated implementations (at least for linear programming) don't actually achieve the worst-case bounds. For efficiency in practice they make parameter choices which result in non-polynomial worst-case bounds (or at least, in versions for which a polynomial bound isn't known). Of course, you can always combine the two by running for a long time with the "efficient" parameter choices, and then if you haven't terminated yet, switching to the worst-case parameter choices.


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