This week, I will present the paper "Regularity lemmas and combinatorial algorithms" by N. Bansal and R. Williams. This introduces a new approach for combinatorial algorithms for fundamental problems exploiting regularity concepts from graph theory and additive combinatorics.

Boolean matrix multiplication (BMM) is a key primitive used in shortest path problems, transitive closures and CFG parsing. For nxn boolean matrices A and B, the problem is to compute the matrix product D over the 'boolean semiring': D[i,j] = OR_{1<=k<=n} (A[i,k] AND B[k,j]). It is easy to see that this problem easily reduces to the "Algebraic matrix multiplication" (AMM) (the standard matrix product over the reals, for instance); in fact, this provides the best known algorithms for BMM! Beginning with Strassen's algorithm (O*(n^{2.8}) time, suppressing a polylog factor), AMM has attracted much theoretical attention, since we now have the additional power of subtraction(cancellation). The state of the art using this approach is a O*(n^{2.38}) algorithm by Coppersmith and Winograd (1987). However, there is an alternative line of attack based on "combinatorial algorithms" treating boolean matrices as graphs; many of them are based on the "Four Russians" algorithm. This approach has yielded only modest results: the best running time for dense matrices is O(n^3/log^2 n).

In this talk, I will present a new combinatorial algorithm that asympototically beats the Four Russians. For this, we will need a strong hammer from graph theory: regularity (and triangle removal) concepts. More precisely, the algorithm will be based on the (algorithmic versions of) Szemeredi's regularity theorem, the triangle removal lemma and Frieze and Kannan's pseudoregularity (weak regularity) theorem. I will first explain these theorems. Then, I will present two algorithms for BMM. The first is essentially a natural reduction to the triangle removal lemma. The second algorithm builds on the first one, but exploits weak regularity instead. We will achieve a running time of nearly O*(n^3/ (log n)^{2.25}) (suppressing a poly(log log n) factor), which is the currently best known for any combinatorial algorithm. Finally, I will present a subcubic algorithm for the related problem of detecting if a given graph has any triangles, again using weak regularity. (All these algorithms will be randomized, and will work w.h.p.) The main open questions in this approach are to derandomize the above algorithms, and to obtain a truly subcubic time algorithm.

This talk will be mostly self-contained. (You may wish to review the details of some basic models of computation (pointer machine model and the word RAM), which are relevant for the problem, but, due to time constraints, I suspect I will mostly gloss over the model-dependent aspects of the algorithm.)

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