Towards Better Approximation of Winner Determination for Combinatorial Auctions with Large Number of Bids

We propose new approximate algorithms for combinatorial auctions with massively large number of (more than 100,000) bids. In this paper, we focus on a more practical approximated algorithm in the context of revenue maximization. We propose a hill-climbing greedy algorithm, a SA-like random search al...

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Bibliographic Details
Published in:2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology : proceedings : 18-22 December, 2006, Hong Kong, China pp. 618 - 621
Main Authors: Fukuta, Naoki, Ito, Takayuki
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 18.12.2006
IEEE
Series:ACM Conferences
Subjects:
Applied computing > Operations research > Decision analysis
Approximation algorithms
Computational complexity
Computer science
Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Cooperation and coordination
Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Intelligent agents
Consumer electronics
Greedy algorithms
Indium tin oxide
Informatics
Information systems > Information systems applications > Decision support systems
Information systems > World Wide Web > Online advertising
Information systems > World Wide Web > Web applications > Electronic commerce
Mathematics of computing > Discrete mathematics > Combinatorics > Combinatorial algorithms
Simulated annealing
Sorting
Temperature
ISBN:9780769527482, 0769527485
Online Access:Get full text
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Summary:We propose new approximate algorithms for combinatorial auctions with massively large number of (more than 100,000) bids. In this paper, we focus on a more practical approximated algorithm in the context of revenue maximization. We propose a hill-climbing greedy algorithm, a SA-like random search algorithm, and their enhancement for searching multiple key parameter values. The experimental results demonstrate that our algorithms perform approximately 0.997 optimality compared with the optimal solutions and better than previously presented approximated algorithms. We also demonstrate that our algorithms are a kind of anytime algorithmthat bring better results in shorter computational time that can be applied to large and dynamic electronic markets.
ISBN:9780769527482
0769527485
DOI:10.1109/IAT.2006.123