An efficient approximate allocation algorithm for combinatorial auctions

Edo Zurel*, Noam Nisan

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

102 Scopus citations

Abstract

We propose a heuristic for allocation in combinatorial auctions. We first run an approximation algorithm on the linear programming relaxation of the combinatorial auction. We then run a sequence of greedy algorithms, starting with the order on the bids determined by the approximate linear program and continuing in a hill-climbing fashion using local improvements in the order of bids. We have implemented the algorithm and have tested it on the complete corpus of instances provided by Vohra and de Vries as well as on instances drawn from the distributions of Leyton-Brown, Pearson, and Shoham. Our algorithm typically runs two to three orders of magnitude faster than the reported running times of Vohra and de Vries, while achieving an average approximation error of less than 1%. This algorithm can provide, in less than a minute of CPU time, excellent solutions for problems with over 1000 items and 10,000 bids. We thus believe that combinatorial auctions for most purposes face no practical computational hurdles.

Original languageAmerican English
Pages125-136
Number of pages12
DOIs
StatePublished - 2001
EventEC'01: Proceedings of the 3rd ACM Conference on Electronic Commerce - Tampa, FL, United States
Duration: 14 Oct 200117 Oct 2001

Conference

ConferenceEC'01: Proceedings of the 3rd ACM Conference on Electronic Commerce
Country/TerritoryUnited States
CityTampa, FL
Period14/10/0117/10/01

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