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Electronic Colloquium on Computational Complexity

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Reports tagged with matrix multiplication:
TR00-060 | 17th August 2000
Uri Zwick

All Pairs Shortest Paths using Bridging Sets and Rectangular Matrix Multiplication

We present two new algorithms for solving the {\em All
Pairs Shortest Paths\/} (APSP) problem for weighted directed
graphs. Both algorithms use fast matrix multiplication algorithms.

The first algorithm
solves the APSP problem for weighted directed graphs in which the edge
weights are integers of small absolute value in ... more >>>

TR04-032 | 5th February 2004
Ryan Williams

A new algorithm for optimal constraint satisfaction and its implications

We present a novel method for exactly solving (in fact, counting solutions to) general constraint satisfaction optimization with at most two variables per constraint (e.g. MAX-2-CSP and MIN-2-CSP), which gives the first exponential improvement over the trivial algorithm; more precisely, it is a constant factor improvement in the base of ... more >>>

TR06-115 | 26th July 2006
Artur Czumaj, Andrzej Lingas

Finding a Heaviest Triangle is not Harder than Matrix Multiplication

We show that for any $\epsilon > 0$, a maximum-weight triangle in an
undirected graph with $n$ vertices and real weights assigned to
vertices can be found in time $\O(n^{\omega} + n^{2 + \epsilon})$,
where $\omega $ is the exponent of fastest matrix multiplication
algorithm. By the currently best bound ... more >>>

TR11-067 | 25th April 2011
Noga Alon, Amir Shpilka, Chris Umans

On Sunflowers and Matrix Multiplication

Comments: 1

We present several variants of the sunflower conjecture of Erd\H{o}s and Rado and discuss the relations among them.

We then show that two of these conjectures (if true) imply negative answers to questions of Coppersmith and Winograd and Cohn et al. regarding possible approaches for obtaining fast matrix multiplication algorithms. ... more >>>

TR12-004 | 10th January 2012
Marcos Villagra, Masaki Nakanishi, Shigeru Yamashita, Yasuhiko Nakashima

Tensor Rank and Strong Quantum Nondeterminism in Multiparty Communication

Revisions: 3

In this paper we study quantum nondeterminism in multiparty communication. There are three (possibly) different types of nondeterminism in quantum computation: i) strong, ii) weak with classical proofs, and iii) weak with quantum proofs. Here we focus on the first one. A strong quantum nondeterministic protocol accepts a correct input ... more >>>

TR14-117 | 18th August 2014
Shiva Manne, Manjish Pal

Fast Approximate Matrix Multiplication by Solving Linear Systems

Comments: 1

In this paper, we present novel deterministic algorithms for multiplying two $n \times n$ matrices approximately. Given two matrices $A,B$ we return a matrix $C'$ which is an \emph{approximation} to $C = AB$. We consider the notion of approximate matrix multiplication in which the objective is to make the Frobenius ... more >>>

TR14-154 | 20th November 2014
Andris Ambainis, Yuval Filmus, Francois Le Gall

Fast Matrix Multiplication: Limitations of the Laser Method

Until a few years ago, the fastest known matrix multiplication algorithm, due to Coppersmith and Winograd (1990), ran in time $O(n^{2.3755})$. Recently, a surge of activity by Stothers, Vassilevska-Williams, and Le Gall has led to an improved algorithm running in time $O(n^{2.3729})$. These algorithms are obtained by analyzing higher ... more >>>

TR15-127 | 7th August 2015
Stasys Jukna, Georg Schnitger

On the Optimality of Bellman--Ford--Moore Shortest Path Algorithm

Revisions: 1

We prove a general lower bound on the size of branching programs over any semiring of zero characteristic, including the (min,+) semiring. Using it, we show that the classical dynamic programming algorithm of Bellman, Ford and Moore for the shortest s-t path problem is optimal, if only Min and Sum ... more >>>

TR17-131 | 1st September 2017
Joshua Grochow, Cris Moore

Designing Strassen's algorithm

In 1969, Strassen shocked the world by showing that two n x n matrices could be multiplied in time asymptotically less than $O(n^3)$. While the recursive construction in his algorithm is very clear, the key gain was made by showing that 2 x 2 matrix multiplication could be performed with ... more >>>

TR20-029 | 6th March 2020
Swastik Kopparty, Guy Moshkovitz, Jeroen Zuiddam

Geometric Rank of Tensors and Subrank of Matrix Multiplication

Motivated by problems in algebraic complexity theory (e.g., matrix multiplication) and extremal combinatorics (e.g., the cap set problem and the sunflower problem), we introduce the geometric rank as a new tool in the study of tensors and hypergraphs. We prove that the geometric rank is an upper bound on the ... more >>>

TR20-030 | 9th March 2020
Matthias Christandl, Fran├žois Le Gall, Vladimir Lysikov, Jeroen Zuiddam

Barriers for Rectangular Matrix Multiplication

We study the algorithmic problem of multiplying large matrices that are rectangular. We prove that the method that has been used to construct the fastest algorithms for rectangular matrix multiplication cannot give optimal algorithms. In fact, we prove a precise numerical barrier for this method. Our barrier improves the previously ... more >>>

TR22-020 | 18th February 2022
Vahid Reza Asadi, Alexander Golovnev, Tom Gur, Igor Shinkar

Worst-Case to Average-Case Reductions via Additive Combinatorics

We present a new framework for designing worst-case to average-case reductions. For a large class of problems, it provides an explicit transformation of algorithms running in time $T$ that are only correct on a small (subconstant) fraction of their inputs into algorithms running in time $\widetilde{O}(T)$ that are correct on ... more >>>

TR22-111 | 1st August 2022
Robert Andrews

On Matrix Multiplication and Polynomial Identity Testing

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 $n \times n \times n$ matrix multiplication, we construct a hitting set generator with seed length $O(\sqrt{n} \cdot ... more >>>

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