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.

• Type: Journal
• Year: 2011
• Where: SIAMCOMP
• Authors: Nir Ailon, Bernard Chazelle, Kenneth L. Clarkson, Ding Liu, Wolfgang Mulzer, C. Seshadhri
• Keywords: Algorithm, Input Distribution, SIAMCOMP 2011, Software Engineering, Unknown Input Distribution