Self-improving algorithms

Nir Ailon, Bernard Chazelle, Seshadhri Comandur, Ding Liu

Research output: Contribution to conferencePaper

9 Scopus citations


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.

Original languageEnglish (US)
Number of pages10
StatePublished - Feb 28 2006
EventSeventeenth Annual ACM-SIAM Symposium on Discrete Algorithms - Miami, FL, United States
Duration: Jan 22 2006Jan 24 2006


OtherSeventeenth Annual ACM-SIAM Symposium on Discrete Algorithms
CountryUnited States
CityMiami, FL

All Science Journal Classification (ASJC) codes

  • Software
  • Mathematics(all)

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    Ailon, N., Chazelle, B., Comandur, S., & Liu, D. (2006). Self-improving algorithms. 261-270. Paper presented at Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithms, Miami, FL, United States.