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Approximating nash social welfare under rado valuations

Approximating nash social welfare under rado valuations The Nash social welfare problem asks for an allocation of indivisible items to agents in order to maximize the geometric mean of agents' valuations. We give an overview of the constant-factor approximation algorithm for the problem when agents have Rado valuations [Garg et al. 2021]. Rado valuations are a common generalization of the assignment (OXS) valuations and weighted matroid rank functions. Our approach also gives the first constant-factor approximation algorithm for the asymmetric Nash social welfare problem under the same valuations, provided that the maximum ratio between the weights is bounded by a constant. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGecom Exchanges Association for Computing Machinery

Approximating nash social welfare under rado valuations

ACM SIGecom Exchanges , Volume 19 (1): 7 – Jul 16, 2021

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2021 Copyright is held by the owner/author(s)
ISSN
1551-9031
eISSN
1551-9031
DOI
10.1145/3476436.3476444
Publisher site
See Article on Publisher Site

Abstract

The Nash social welfare problem asks for an allocation of indivisible items to agents in order to maximize the geometric mean of agents' valuations. We give an overview of the constant-factor approximation algorithm for the problem when agents have Rado valuations [Garg et al. 2021]. Rado valuations are a common generalization of the assignment (OXS) valuations and weighted matroid rank functions. Our approach also gives the first constant-factor approximation algorithm for the asymmetric Nash social welfare problem under the same valuations, provided that the maximum ratio between the weights is bounded by a constant.

Journal

ACM SIGecom ExchangesAssociation for Computing Machinery

Published: Jul 16, 2021

Keywords: approximation algorithm

References