[Computational Complexity] GCT Workshop (Guest Post by Josh Grochow)
- Last week, the Center for Computational Intractability hosted a Geometric Complexity Theory Workshop. Geometric complexity theory (GCT) is an approach to P vs NP and related problems proposed by Ketan Mulmuley and Milind Sohoni, in which they were naturally led to using techniques in algebraic geometry and representation theory. This workshop was the most recent attempt to explicate GCT to both mathematicians and computer scientists, and discuss recent related results. Without looking at the attendance list, my impression was that the workshop was approximately 3/8 complexity theorists and 3/4 classical mathematicians (meaning that about 1/8 fell into both camps), and was about 1/3 students and 2/3 postdocs/professors. This turned out to be a pretty good mix.
The first two days were scheduled to be all Ketan all the time. Despite the fact that Ketan is an excellent presenter, talking for two days straight and keeping the audience interested would be a tough task for anyone. Not everyone made it to the end, but a significant fraction of the audience did. Ketan's lectures were interspersed with a lot of discussion and debate, including a couple short impromptu guest lectures from audience members on some background topics. Pointed questions came from both classical mathematicians and complexity theorists, and as often as not were answered by audience members from the other field. It was refreshing to see the humility of the giants of multiple fields: great classical mathematicians asked complexity theory questions that a complexity undergrad could answer, and vice versa. I think this is one of the few times I have ever heard of serious classical mathematicians mingling with complexity theorists, and I think it worked quite well. This type of interaction and more seems necessary for the GCT program, and can only be a Good Thing for both fields.
And now for two technical items from the workshop.
Avi Wigderson and others (sorry to the others, but Avi sticks out most in my memory, and I didn't learn everyone's names!) repeatedly suggested applying the techniques of GCT to easier problems than P vs NP and see if they can be pushed through to fruition for easier lower bounds. At the moment, GCT reduces P vs NP to certain hard conjectures in algebraic geometry and representation theory. The corresponding conjectures arising in easier lower bound problems would hopefully be easier, maybe even easy enough to solve in our lifetimes! Peter Burgisser has been looking at matrix multiplication using these techniques (an idea originally suggested by his adviser, Volker Strassen, long before GCT came along), and his student Christian Ikenmeyer gave a presentation on their results as one of the research talks on the third day.
Christian's talk was probably the most salient for complexity theorists not intimately familiar with GCT, so I'll mention a bit more about it to finish off the post. GCT introduces the idea of a "geometric obstruction" (=some type of representation-theoretic object) to "there are circuits of size nc for SAT up to length n" (NP vs P/poly). If a geometric obstruction exists for all (or infinitely many) input lengths n, then P is not equal to NP. The conjectures arising in GCT imply that such obstructions exist. But even in smaller problems, such as matrix multiplication, no one had ever seen such geometric obstructions! Peter Burgisser and Christian Ikenmeyer did computer calculations and found geometric obstructions to the border-rank of 2x2 matrix multiplication. (The border-rank of 2x2 matrix multiplication roughly captures how many multiplications are needed to approximate 2x2 matrix multiplication, in a specific sense.) The geometric obstructions they found show that the border rank is at least 6 (it is known to be 7, by a significant algebro-geometric result of Joseph Landsberg). Although this only proved a weaker result than what was already known, for a comparatively easy lower bound (compared to P vs NP), this is an important proof-of-concept for the GCT approach of using geometric obstructions to prove complexity-theoretic lower bounds. This work also raises the intriguing possibility (also suggested in the GCT series of papers) that one might be able to "verify P neq NP up to length n" for larger and larger n, the same way people verify the Goldbach Conjecture or Riemann Hypothesis up to a certain numbers of integers/zeroes. Unfortunately, doing this in the GCT setting even up to n=10 seems to take too much time (whereas Goldbach and Riemann have been verified for millions of integers/zeroes).
Finally, I should mention even in the case of 2x2 matrix multiplication, the conjectures that arise seem almost as difficult as the ones arising in the P vs NP problem. Avi and others suggested we look at even easier problems.
Posted By Lance to Computational Complexity at 7/14/2010 07:12:00 AM