Papers for

machine learning systems developers

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New limits found for fast matrix multiplication methods

Multiplying matrices via polynomials: exposition and barriers

Abstract: We give an exposition of OpenAI's recent proof that the exponent $ω_\mathbb{C}$ of matrix multiplication over the complex numbers satisfies $ω_\mathbb{C}\leq9/4$. Our version of the proof gives $ω_\mathbb{F}\leq9/4$ over every field $\mathbb{F}$. We also treat the rectangular matrix multiplication exponent $ω_\mathbb{F}(a,b,c)$. Here we have $ω_\mathbb{F}(1,k,1)=2$ for $0\leq k\leq1/2$ and $ω_\mathbb{F}(1,k,1)\leq1+k+1/(4k)$ for $k\geq1/2$, and, for real numbers $a\geq b\geq c\geq0$, $ω_\mathbb{F}(a,b,c)=a+b$ if $c(a+b)\leq ab$ and $ω_\mathbb{F}(a,b,c)\leq(a+b+c)/2+(ab/c+ac/b+bc/a)/4$ otherwise. Our exposition is in the language of degeneration inequalities among polynomial multiplication tensors and Strassen's asymptotic spectrum duality. We moreover prove an operational barrier (which can be thought of as coming from an operational asymptotic spectrum) showing that the current analysis of all these upper bounds on $ω_\mathbb{F}$ and $ω_\mathbb{F}(a,b,c)$ is optimal and that in fact the current degeneration ingredients cannot improve the bounds.

Wed 21 OctComputational Complexity
The gist
Multiplying big grids of numbers, called matrices, takes time. This paper explains a recent way to multiply matrices faster using polynomials, which are math expressions with variables. The authors show the fastest possible speed for these methods and also prove that improving them with current techniques is impossible. They use advanced math ideas about how these multiplications relate to each other in a deeper way.
Open → 2610.30054v1