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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Community energy retail tariffs in Singapore: opportunities for peer-to-peer and time-of-use versus vertically integrated tariffs
TL;DR: In this article, a portfolio of stylized retail tariffs for different market organizations is proposed, among the proposed configurations are time-of-use (ToU), default vertical and peer-to-peer (P2P) tariffs, the last of which operates through a blockchain platform.
Posted Content
Learning and Optimization with Submodular Functions.
TL;DR: The formal definition of submodularity is reviewed; the optimization of sub modular functions, both maximization and minimization are reviewed; and some applications in relation to learning and reasoning using submodular functions are discussed.
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Dynamic Programming based Allocation of Virtual Machine Instances in Clouds
TL;DR: This work proposes dynamic programming based mechanism for solving VMPA problem (DP-VMPA), which takes the maximum social welfare as the objective function, and uses the combinatorial auction-based dynamic programming (CA-DP) allocation algorithm to solve the Winner Determination Problem (WDP).
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A strategy for multi-agent resource allocation under incomplete information
TL;DR: This paper tackles a distributed resource allocation among selfish and myopic agents for the redistribution of indivisible resources without side payment by addressing a negotiation strategy allowing an allocation with a good quality measured in term of social welfare.
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Investment Incentives in Near-Optimal Mechanisms
TL;DR: It is shown that if a monotone allocation rule “excludes bossy negative externalities” and guarantees a fraction β in the worst case, then that guarantee persists when investment is possible.
References
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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.
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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.
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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.