## Abstract

Let X = {1, 2, . . . , n} be a ground set of n elements, and let S be a family of subsets of X, |S| = m, with a positive cost c_{S} associated with each S ∈ S. Consider the following online version of the set cover problem, described as a game between an algorithm and an adversary. An adversary gives elements to the algorithm from X one-by-one. Once a new element is given, the algorithm has to cover it by some set of S containing it. We assume that the elements of X and the members of S are known in advance to the algorithm, however, the set X′ ⊆ X of elements given by the adversary is not known in advance to the algorithm. (In general, X′ may be a strict subset of X.) The objective is to minimize the total cost of the sets chosen by the algorithm. Let C denote the family of sets in S that the algorithm chooses. At the end of the game the adversary also produces (off-line) a family of sets C_{OPT} that covers X′. The performance of the algorithm is the ratio between the cost of C and the cost of C_{OPT}. The maximum ratio, taken over all input sequences, is the competitive ratio of the algorithm. We present an O (log m log n) competitive deterministic algorithm for the problem, and establish a nearly matching Ω (log n log m/log log m+log log n) lower bound for all interesting values of m and n. The techniques used are motivated by similar techniques developed in computational learning theory for online prediction (e.g., the WINNOW algorithm) together with a novel way of converting the fractional solution they supply into a deterministic online algorithm.

Original language | English (US) |
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Pages (from-to) | 100-105 |

Number of pages | 6 |

Journal | Conference Proceedings of the Annual ACM Symposium on Theory of Computing |

State | Published - 2003 |

Externally published | Yes |

Event | 35th Annual ACM Symposium on Theory of Computing - San Diego, CA, United States Duration: Jun 9 2003 → Jun 11 2003 |

## All Science Journal Classification (ASJC) codes

- Software

## Keywords

- Competitive Analysis
- Derandomization
- On-line Algorithms
- Randomized Rounding
- Set-Cover