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Book ChapterDOI

An Optimal Online Algorithm for Weighted Bipartite Matching and Extensions to Combinatorial Auctions

TLDR
This work studies online variants of weighted bipartite matching on graphs and hypergraphs where the right-hand side vertices correspond to items and the left-handside vertices to bidders in order to develop a model for online matching.
Abstract
We study online variants of weighted bipartite matching on graphs and hypergraphs. In our model for online matching, the vertices on the right-hand side of a bipartite graph are given in advance and the vertices on the left-hand side arrive online in random order. Whenever a vertex arrives, its adjacent edges with the corresponding weights are revealed and the online algorithm has to decide which of these edges should be included in the matching. The studied matching problems have applications, e.g., in online ad auctions and combinatorial auctions where the right-hand side vertices correspond to items and the left-hand side vertices to bidders.

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Citations
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Book

Online Matching and Ad Allocation

TL;DR: This monograph surveys the key problems, models and algorithms from online matchings, as well as their implication in the practice of ad allocation, to provide a classification of the problems, techniques and algorithms in this area.
Proceedings ArticleDOI

Online mobile Micro-Task Allocation in spatial crowdsourcing

TL;DR: This paper identifies a more practical micro-task allocation problem, called the Global Online Micro-task Allocation in spatial crowdsourcing (GOMA) problem, and proposes a two-phase-based framework, based on which the TGOA algorithm with 1 over 4 -competitive ratio under the online random order model is presented.
Journal ArticleDOI

Spatial crowdsourcing: a survey

TL;DR: A comprehensive and systematic review of existing research on four core algorithmic issues in spatial crowdsourcing: (1) task assignment, (2) quality control, (3) incentive mechanism design, and (4) privacy protection.
Book ChapterDOI

Prophet secretary for combinatorial auctions and matroids

TL;DR: In this article, the prophet inequalities were improved to (1− 1/e)-approximation for both matroids and combinatorial auctions for the adversarial arrival order of random variables.
Posted Content

Primal Beats Dual on Online Packing LPs in the Random-Order Model

TL;DR: In this paper, the authors considered the problem of packing LP in an online model where the columns are presented to the algorithm in random order, and they obtained an incentive compatible (1 − ϵ)-competitive algorithm with competitive ratio O(1) for any (randomized) online algorithm.
References
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Book

Algorithmic Game Theory

TL;DR: A new era of theoretical computer science addresses fundamental problems about auctions, networks, and human behavior in a bid to solve the challenges of 21st Century finance.
Proceedings ArticleDOI

An optimal algorithm for on-line bipartite matching

TL;DR: This work applies the general approach to data structures, bin packing, graph coloring, and graph coloring to bipartite matching and shows that a simple randomized on-line algorithm achieves the best possible performance.
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