Algorithm for optimal winner determination in combinatorial auctions
Reads0
Chats0
TLDR
The algorithm allows combinatorial auctions to scale up to significantly larger numbers of items and bids than prior approaches to optimal winner determination by capitalizing on the fact that the space of bids is sparsely populated in practice.About:
This article is published in Artificial Intelligence.The article was published on 2002-02-01 and is currently open access. It has received 1045 citations till now. The article focuses on the topics: Combinatorial auction & Common value auction.read more
Citations
More filters
Posted Content
Bundling Equilibrium in Combinatorial auctions
TL;DR: The tradeoff between communication complexity and economic efficiency of bundling equilibrium is analyzed, and the main motivation for studying all these equilibria, and not just the domination equilibrium, is that they afford a reduction of the communication complexity.
Journal ArticleDOI
Trading grid services – a multi-attribute combinatorial approach
TL;DR: This paper proposes an auction mechanism for allocating and scheduling computer resources such as processors or storage space which have multiple quality attributes which are evaluated according to its economic and computational performance as well as its practical applicability by means of a simulation.
Journal ArticleDOI
MINIMAXSAT: an efficient weighted max-SAT solver
TL;DR: A wide set of solving alternatives on a broad set of optimization benchmarks indicates that the performance of MINIMAXSAT is usually close to the best specialized alternative and, in some cases, even better.
Proceedings ArticleDOI
Truthful randomized mechanisms for combinatorial auctions
TL;DR: Two computationally-efficient incentive-compatible mechanisms for combinatorial auctions with general bidder preferences are designed, both of which are randomized, and are incentive- compatible in the universal sense.
Journal ArticleDOI
Empirical hardness models: Methodology and a case study on combinatorial auctions
TL;DR: The use of supervised machine learning is proposed to build models that predict an algorithm's runtime given a problem instance and techniques for interpreting them are described to gain understanding of the characteristics that cause instances to be hard or easy.
References
More filters
Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Book ChapterDOI
Reducibility Among Combinatorial Problems
TL;DR: The work of Dantzig, Fulkerson, Hoffman, Edmonds, Lawler and other pioneers on network flows, matching and matroids acquainted me with the elegant and efficient algorithms that were sometimes possible.
Book
Integer programming
TL;DR: The principles of integer programming are directed toward finding solutions to problems from the fields of economic planning, engineering design, and combinatorial optimization as mentioned in this paper, which is a standard of graduate-level courses since 1972.
Journal ArticleDOI
Incentives in Teams
TL;DR: This paper analyzes the problem of inducing the members of an organization to behave as if they formed a team and exhibits a particular set of compensation rules, an optimal incentive structure, that leads to team behavior.