DocumentCode
3561389
Title
Automatic Reproduction of a Genius Algorithm: Strassen´s Algorithm Revisited by Genetic Search
Author
Oh, Seunghyun ; Moon, Byung-Ro
Author_Institution
Dept. of Electr. Eng. & Comput. Sci., Univ. of Michigan, Ann Arbor, MI, USA
Volume
14
Issue
2
fYear
2010
fDate
4/1/2010 12:00:00 AM
Firstpage
246
Lastpage
251
Abstract
In 1968, Volker Strassen, a young German mathematician, announced a clever algorithm to reduce the asymptotic complexity of n??n matrix multiplication from the order of n 3 to n 2.81. It soon became one of the most famous scientific discoveries in the 20th century and provoked numerous studies by other mathematicians to improve upon it. Although a number of improvements have been made, Strassen´s algorithm is still optimal in his original framework, the bilinear systems of 2 ?? 2 matrix multiplication, and people are still curious how Strassen developed his algorithm. We examined it to see if we could automatically reproduce Strassen´s discovery using a search algorithm and find other algorithms of the same quality. In total, we found 608 algorithms that have the same quality as Strassen´s, including Strassen´s original algorithm. We partitioned the algorithms into nine different groups based on the way they are constructed. This paper was made possible by the combination of genetic search and linear-algebraic techniques. To the best of our knowledge, this is the first work that automatically reproduced Strassen´s algorithm, and furthermore, discovered new algorithms with equivalent asymptotic complexity using a search algorithm.
Keywords
bilinear systems; genetic algorithms; matrix multiplication; search problems; Strassen algorithm; asymptotic complexity; bilinear systems; genetic search; linear-algebraic techniques; matrix multiplication; search algorithm; Bilinear; Gaussian elimination; Sammon mapping; Strassen´s algorithm; genetic algorithm; matrix multiplication;
fLanguage
English
Journal_Title
Evolutionary Computation, IEEE Transactions on
Publisher
ieee
Conference_Location
10/30/2009 12:00:00 AM
ISSN
1089-778X
Type
jour
DOI
10.1109/TEVC.2009.2029568
Filename
5299113
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