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SIAMCOMP
2011

Self-Improving Algorithms

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Self-Improving Algorithms
We investigate ways in which an algorithm can improve its expected performance by fine-tuning itself automatically with respect to an arbitrary, unknown input distribution. We give such self-improving algorithms for sorting and clustering. The highlights of this work: (i) a sorting algorithm with optimal expected limiting running time; and (ii) a k-median algorithm over the Hamming cube with linear expected limiting running time. In all cases, the algorithm begins with a learning phase during which it adjusts itself to the input distribution (typically in a logarithmic number of rounds), followed by a stationary regime in which the algorithm settles to its optimized incarnation.
Nir Ailon, Bernard Chazelle, Kenneth L. Clarkson,
Added 15 May 2011
Updated 15 May 2011
Type Journal
Year 2011
Where SIAMCOMP
Authors Nir Ailon, Bernard Chazelle, Kenneth L. Clarkson, Ding Liu, Wolfgang Mulzer, C. Seshadhri
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