Computational complexity of matrix multiplication: Difference between revisions

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Updated the article to reflect that the pre-print authored by Williams, Xu, Xu, and Zhou has been officially published in a peer-reviewed conference in 2024.
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}}</ref> The optimal number of field operations needed to multiply two square {{math|''n'' × ''n''}} matrices [[big O notation|up to constant factors]] is still unknown. This is a major open question in [[theoretical computer science]].
 
{{As of|20232024|0701}}, the best announced bound on the [[Time complexity|asymptotic complexity]] of a matrix multiplication algorithm is {{math|O(''n''<sup>2.371552</sup>)}} time, given by Williams, Xu, Xu, and Zhou,.<ref name="wxxz23">{{cite arXivconference |last1=Vassilevska Williams |first1=Virginia Vassilevska |last2=Xu |first2=Yinzhan |last3=Xu |first3=Zixuan |last4=Zhou |first4=Renfei |date=2023 |title=New Bounds for Matrix Multiplication: from Alpha to Omega |classconference=cs.DSProceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) |eprintpages=3792–3835 |arxiv=2307.07970 |doi=10.1137/1.9781611977912.134}}</ref><ref>{{cite announcedweb in|last=Nadis a|first=Steve preprint.|date=March This7, improves2024 on|title=New theBreakthrough boundBrings ofMatrix Multiplication Closer to Ideal {{math|O(''n''<sup>2url=https://www.371866<quantamagazine.org/sup>)new-breakthrough-brings-matrix-multiplication-closer-to-ideal-20240307 |access-date=2024-03-09}}</ref> givenHowever, bythis aand preprintsimilar ofimprovements to Strassen are not used in Duanpractice, Wubecause andthey Zhouare [[galactic algorithm]]s: the constant coefficient hidden by the [[big O notation]] is so large that they are only worthwhile for matrices that are too large to handle on present-day computers.<ref name="dwz22">{{cite journal
{{cite arXiv
| last1=Duan
| first1=Ran
| last2=Wu
| first2=Hongxun
| last3=Zhou
| first3=Renfei
| year = 2022
| eprint=2210.10173
| title = Faster Matrix Multiplication via Asymmetric Hashing
| class=cs.DS
}}</ref> However, this and similar improvements to Strassen are not used in practice, because they are [[galactic algorithm]]s: the constant coefficient hidden by the [[big O notation]] is so large that they are only worthwhile for matrices that are too large to handle on present-day computers.<ref>{{cite journal
| last = Iliopoulos
| first = Costas S.
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| url-status = dead
}}</ref><ref name="robinson">{{cite journal | last=Robinson | first=Sara | title=Toward an Optimal Algorithm for Matrix Multiplication | url=https://archive.siam.org/pdf/news/174.pdf | date=November 2005 | journal=SIAM News | volume=38 | issue=9 | quote=Even if someone manages to prove one of the conjectures—thereby demonstrating that {{math|1=''ω'' = 2}}—the wreath product approach is unlikely to be applicable to the large matrix problems that arise in practice. [...] the input matrices must be astronomically large for the difference in time to be apparent.}}</ref>
The best time-bound confirmed by peer review is {{math|O(''n''<sup>2.3728596</sup>)}}.<ref name="aw20"/>
 
== Simple algorithms ==
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}}</ref><ref>{{Cite web|last=Hartnett|first=Kevin|title=Matrix Multiplication Inches Closer to Mythic Goal|url=https://www.quantamagazine.org/mathematicians-inch-closer-to-matrix-multiplication-goal-20210323/|access-date=2021-04-01|website=Quanta Magazine|date=23 March 2021 |language=en}}</ref>
|-
| 2022 || 2.371866 || Duan, Wu, Zhou<ref name="dwz22"/>
{{cite arXiv |eprint=2210.10173 |class=cs.DS |first1=Ran |last1=Duan |first2=Hongxun |last2=Wu |title=Faster Matrix Multiplication via Asymmetric Hashing |last3=Zhou |first3=Renfei |year=2022}}</ref>
|-
| 20232024 || 2.371552 || [[Virginia Vassilevska Williams|Williams]], Xu, Xu, and Zhou<ref name="wxxz23"/>
|}
 
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Since the output of the matrix multiplication problem is size <math>n^2</math>, we have <math>\omega(k) \geq 2</math> for all values of <math>k</math>. If one can prove for some values of <math>k</math> between 0 and 1 that <math>\omega(k) \leq 2</math>, then such a result shows that <math>\omega(k) = 2</math> for those <math>k</math>. The largest ''k'' such that <math>\omega(k) = 2</math> is known as the ''dual matrix multiplication exponent'', usually denoted ''α''. ''α'' is referred to as the "[[Duality (optimization)|dual]]" because showing that <math>\alpha = 1</math> is equivalent to showing that <math>\omega = 2</math>. Like the matrix multiplication exponent, the dual matrix multiplication exponent sometimes appears in the complexity of algorithms in numerical linear algebra and optimization.<ref>{{Cite journal|last1=Cohen|first1=Michael B.|last2=Lee|first2=Yin Tat|last3=Song|first3=Zhao|date=2021-01-05|title=Solving Linear Programs in the Current Matrix Multiplication Time|url=https://doi.org/10.1145/3424305|journal=Journal of the ACM|volume=68|issue=1|pages=3:1–3:39|doi=10.1145/3424305|issn=0004-5411|arxiv=1810.07896|s2cid=231955576 }}</ref>
 
The first bound on ''α'' is by [[Don Coppersmith|Coppersmith]] in 1982, who showed that <math>\alpha > 0.17227</math>.<ref>{{Cite journal|last=Coppersmith|first=D.|date=1982-08-01|title=Rapid Multiplication of Rectangular Matrices|url=https://epubs.siam.org/doi/10.1137/0211037|journal=SIAM Journal on Computing|volume=11|issue=3|pages=467–471|doi=10.1137/0211037|issn=0097-5397|url-access=subscription}}</ref> The current best peer-reviewed bound on ''α'' is <math>\alpha >\geq 0.31389321334</math>, given by Le Gall and Urrutia.<ref>{{cite book|last1=Le Gall|first1=Francois|title=Improved Rectangular Matrix Multiplication using Powers of the Coppersmith-Winograd Tensor|date=2018-01-01|url=https://epubs.siam.org/doi/10.1137/1.9781611975031.67|work=Proceedings of the 2018 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)|pages=1029–1046|series=Proceedings|publisher=Society for Industrial and Applied Mathematics|doi=10.1137/1.9781611975031.67|access-date=2021-05-23|last2=Urrutia|first2=Florent|arxiv=1708.05622|isbn=978-1-61197-503-1 |s2cid=33396059 }}</ref> This paper also contains bounds on <math>\omega(k)</math>. In July 2023 Williams, Xu, Xu, and Zhou claim to show <math>\alpha \geq 0.321334</math> in a preprint.<ref name="wxxz23"/>
 
==Related problems==