Algorithm for optimal winner determination in combinatorial auctions
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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
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Journal ArticleDOI
The Submodular Welfare Problem with Demand Queries
Uriel Feige,Jan Vondrák +1 more
TL;DR: It is shown that the Submodular Welfare Problem is NP -hard to approximate within a ratio better than some r < 1, and an incentive compatible mechanism based on fair division queries that achieves an optimal solution is presented.
Book ChapterDOI
Auctions with budget constraints
Nir Andelman,Yishay Mansour +1 more
TL;DR: This paper presents exact and approximate algorithms for auctions with budget constraints, and presents a randomized algorithm with an approximation ratio of \(\frac{e}{e-1}\cong\) 1.582, which can be derandomized.
Book ChapterDOI
Coalition structure generation utilizing compact characteristic function representations
TL;DR: This paper presents a new way of formalizing the Coalition Structure Generation problem (CSG), so that it can be applied constraint optimization techniques to it, and develops mixed integer programming formulations and shows that an off-the-shelf optimization package can perform reasonably well.
Improved Exact Solver for the Weighted MAX-SAT Problem.
TL;DR: A propagation algorithm is proposed which improves the detection of disjoint inconsistent subformulas compared to algorithms previously used in Max-SAT solvers and a lazy deletion data structure is developed for the solver which speeds up lower bound calculation on instances with a high clauses-to-variables ratio.
Proceedings Article
Preference elicitation and query learning
TL;DR: In this work, a number of similarities and differences between preference elicitation and query learning are proved, giving both separation results and proving some connections between these problems.
References
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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.
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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.
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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.
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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.