English

Limits on All Known (and Some Unknown) Approaches to Matrix Multiplication

Computational Complexity 2018-10-23 v1 Data Structures and Algorithms

Abstract

We study the known techniques for designing Matrix Multiplication algorithms. The two main approaches are the Laser method of Strassen, and the Group theoretic approach of Cohn and Umans. We define a generalization based on zeroing outs which subsumes these two approaches, which we call the Solar method, and an even more general method based on monomial degenerations, which we call the Galactic method. We then design a suite of techniques for proving lower bounds on the value of ω\omega, the exponent of matrix multiplication, which can be achieved by algorithms using many tensors TT and the Galactic method. Some of our techniques exploit `local' properties of TT, like finding a sub-tensor of TT which is so `weak' that TT itself couldn't be used to achieve a good bound on ω\omega, while others exploit `global' properties, like TT being a monomial degeneration of the structural tensor of a group algebra. Our main result is that there is a universal constant >2\ell>2 such that a large class of tensors generalizing the Coppersmith-Winograd tensor CWqCW_q cannot be used within the Galactic method to show a bound on ω\omega better than \ell, for any qq. We give evidence that previous lower-bounding techniques were not strong enough to show this. We also prove a number of complementary results along the way, including that for any group GG, the structural tensor of C[G]\mathbb{C}[G] can be used to recover the best bound on ω\omega which the Coppersmith-Winograd approach gets using CWG2CW_{|G|-2} as long as the asymptotic rank of the structural tensor is not too large.

Keywords

Cite

@article{arxiv.1810.08671,
  title  = {Limits on All Known (and Some Unknown) Approaches to Matrix Multiplication},
  author = {Josh Alman and Virginia Vassilevska Williams},
  journal= {arXiv preprint arXiv:1810.08671},
  year   = {2018}
}

Comments

32 pages. A preliminary version appeared in the 59th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2018)

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