Adaptive Local Ratio

Mestre, Julián

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Adaptive Local Ratio

Mestre, J. (2008). Adaptive Local Ratio. In Proceedings of the 19th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2008 (pp. 152-160). New York, NY: ACM.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-1AC6-0 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-1AC7-E

Genre: Conference Paper

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Creators:
Mestre, Julián¹, Author

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1Max Planck Society, ou_persistent13

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Abstract: Local Ratio is a well-known paradigm for designing approximation algorithms for combinatorial optimization problems. At a very high level, a local ratio algorithm first decomposes the input weight function $w$ into a positive linear combination of simpler weight functions or \emph{models}. Guided by this process a solution $S$ is constructed such that $S$ is $\alpha$-approximate with respect to each model used in the decomposition. As a result, $S$ is $\alpha$-approximate under $w$ as well. These models usually have a very simple structure that remains ``unchanged'' throughout the execution of the algorithm. In this work we show that adaptively choosing a model from a richer spectrum of functions can lead to a better local ratio. Indeed, by turning the search for a good model into an optimization problem of its own, we get improved approximations for a data migration problem.

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Language(s): eng - English

Dates: Modified: 2009-03-26Date issued: 2008

Publication Status: Issued

Pages: -

Publishing info: New York, NY : ACM

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Identifiers: eDoc: 428165
URI: http://portal.acm.org/ft_gateway.cfm?id=1347099&type=pd
Other: Local-ID: C125756E0038A185-50F3F35FC21C5964C125745E00459C0A-conf/soda/Mestre2008

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Title: Untitled Event

Place of Event: San Francisco, USA

Start-/End Date: 2008-01-20 - 2008-01-22

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Title: Proceedings of the 19th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2008

Source Genre: Proceedings

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Affiliations:

Publ. Info: New York, NY : ACM

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 152 - 160 Identifier: ISBN: 978-0-89871-647-4