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We develop a notion of {\em inner rank} as a tool for obtaining lower bounds on the rank of matrix multiplication tensors. We use it to give a short proof that the border rank (and therefore rank) of the tensor associated with $n\times n$…

Computational Complexity · Computer Science 2019-05-15 Joel Friedman

The border rank of the matrix multiplication operator for n by n matrices is a standard measure of its complexity. Using techniques from algebraic geometry and representation theory, we show the border rank is at least 2n^2-n. Our bounds…

Computational Complexity · Computer Science 2013-06-04 J. M. Landsberg , Giorgio Ottaviani

We show that the border support rank of the tensor corresponding to two-by-two matrix multiplication is seven over the complex numbers. We do this by constructing two polynomials that vanish on all complex tensors with format…

Computational Complexity · Computer Science 2018-09-26 Markus Bläser , Matthias Christandl , Jeroen Zuiddam

The rank of the matrix multiplication operator for nxn matrices is one of the most studied quantities in algebraic complexity theory. I prove that the rank is at least n^2-o(n^2). More precisely, for any integer p\leq n -1, the rank is at…

Computational Complexity · Computer Science 2013-10-31 J. M. Landsberg

Let M_n denote the matrix multiplication tensor for nxn matrices. We use the border substitution method combined with Koszul flattenings to prove the border rank lower bound of 2n^2-log(n)-1 for M_n.

Computational Complexity · Computer Science 2016-08-29 J. M. Landsberg , Mateusz Michałek

We establish basic information about border rank algorithms for the matrix multiplication tensor and other tensors with symmetry. We prove that border rank algorithms for tensors with symmetry (such as matrix multiplication and the…

Algebraic Geometry · Mathematics 2016-02-01 J. M. Landsberg , Mateusz Michałek

We present new methods for determining polynomials in the ideal of the variety of bilinear maps of border rank at most r. We apply these methods to several cases including the case r = 6 in the space of bilinear maps C^4 x C^4 -> C^4. This…

Computational Complexity · Computer Science 2013-07-10 Jonathan D. Hauenstein , Christian Ikenmeyer , J. M. Landsberg

Algebraic matrix multiplication algorithms are designed by bounding the rank of matrix multiplication tensors, and then using a recursive method. However, designing algorithms in this way quickly leads to large constant factors: if one…

Computational Complexity · Computer Science 2024-10-29 Josh Alman , Hantao Yu

We present a new explicit formula for the determinant that contains superexponentially fewer terms than the usual Leibniz formula. As an immediate corollary of our formula, we show that the tensor rank of the $n \times n$ determinant tensor…

Combinatorics · Mathematics 2025-01-07 Robin Houston , Adam P. Goucher , Nathaniel Johnston

We prove that the border rank of the Kronecker square of the little Coppersmith-Winograd tensor $T_{cw,q}$ is the square of its border rank for $q > 2$ and that the border rank of its Kronecker cube is the cube of its border rank for $q >…

Computational Complexity · Computer Science 2021-12-28 Austin Conner , Fulvio Gesmundo , Joseph M. Landsberg , Emanuele Ventura

For every $p\leq n$ positive integer we obtain the lower bound $(3-\frac{1}{p+1})n^2-\big(2\binom{2p}{p+1}-\binom{2p-2}{p-1}+2\big)n$ for the rank of the $n\times n$ matrix multiplication. This bound improves the previous one…

Computational Complexity · Computer Science 2013-11-08 Alex Massarenti , Emanuele Raviolo

We analyze rank decompositions of the $3\times 3$ matrix multiplication tensor over $\mathbb{Z}/2\mathbb{Z}$. We restrict our attention to decompositions of rank $\le 21$, as only those decompositions will yield an asymptotically faster…

Computational Complexity · Computer Science 2024-02-05 Jason Yang

An important building block in all current asymptotically fast algorithms for matrix multiplication are tensors with low border rank, that is, tensors whose border rank is equal or very close to their size. To find new asymptotically fast…

Computational Complexity · Computer Science 2016-08-25 Markus Bläser , Vladimir Lysikov

We show that lower bounds on the border rank of matrix multiplication can be used to non-trivially derandomize polynomial identity testing for small algebraic circuits. Letting $\underline{R}(n)$ denote the border rank of $n \times n \times…

Computational Complexity · Computer Science 2024-04-18 Robert Andrews

Determining the asymptotic algebraic complexity of matrix multiplication, succinctly represented by the matrix multiplication exponent $\omega$, is a central problem in algebraic complexity theory. The best upper bounds on $\omega$, leading…

Computational Complexity · Computer Science 2022-03-08 Matthias Christandl , Péter Vrana , Jeroen Zuiddam

We prove border rank bounds for a class of $GL(V)$-invariant tensors in $V^*\otimes U\otimes W$, where $U$ and $W$ are $GL(V)$-modules. These tensors correspond to spaces of matrices of constant rank. In particular we prove lower bounds for…

Algebraic Geometry · Mathematics 2024-05-10 Derek Wu

Fast matrix multiplication can be described as searching for low-rank decompositions of the matrix--multiplication tensor. We design a neural architecture, \textsc{StrassenNet}, which reproduces the Strassen algorithm for $2\times 2$…

We prove the lower bound R(M_m) \geq 3/2 m^2 - 2 on the border rank of m x m matrix multiplication by exhibiting explicit representation theoretic (occurence) obstructions in the sense of the geometric complexity theory (GCT) program. While…

Computational Complexity · Computer Science 2013-03-19 Peter Bürgisser , Christian Ikenmeyer

This is the second in a series of papers on rank decompositions of the matrix multiplication tensor. We present new rank $23$ decompositions for the $3\times 3$ matrix multiplication tensor $M_{\langle 3\rangle}$. All our decompositions…

Computational Complexity · Computer Science 2018-01-04 Grey Ballard , Christian Ikenmeyer , J. M. Landsberg , Nick Ryder

In this paper, we present a new formula of the determinant tensor $det_n$ for $n \times n$ matrices. In \cite{kim2023newdet4}, Kim, Ju, and Kim found a new formula of $4 \times 4$ determinant tensor $det_4$ which is available when the base…

Commutative Algebra · Mathematics 2023-03-15 Jeong-Hoon Ju , Taehyeong Kim , Yeongrak Kim
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