Importance Sampling for a Monte Carlo Matrix Multiplication Algorithm, with Application to Information Retrieval

We perform importance sampling for a randomized matrix multiplication algorithm by Drineas, Kannan, and Mahoney and derive probabilities that minimize the expected value (with regard to the distributions of the matrix elements) of the variance. We compare these optimized probabilities with uniform p...

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Vydané v:	SIAM journal on scientific computing Ročník 33; číslo 4; s. 1689 - 1706
Hlavní autori:	Eriksson-Bique, Sylvester, Solbrig, Mary, Stefanelli, Michael, Warkentin, Sarah, Abbey, Ralph, Ipsen, Ilse C. F.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Philadelphia, PA Society for Industrial and Applied Mathematics 01.01.2011
Predmet:	Accuracy Algorithms Approximation Documents Exact sciences and technology Expected values Experiments Information retrieval Mathematics Methods of scientific computing (including symbolic computation, algebraic computation) Multiplication & division Numerical analysis Numerical analysis. Scientific computation Numerical linear algebra Numerical methods in probability and statistics Probability Random variables Sciences and techniques of general use 68W20 Monte Carlo method 62P30 Multiplication Numerical linear algebra query term-by-document matrix Information retrieval 15B48 Probability distribution randomized algorithm Stochastic method Algorithm Variance matching Numerical analysis Scientific computation 15A99 Distribution function Matrix method Sampling expected value 68Q87
ISSN:	1064-8275, 1095-7197
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