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A Systematic Approach to Bound Factor Revealing LPs and Its Application to the Metric and Squared Metric Facility Location Problems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7408))

Abstract

A systematic technique to bound factor-revealing linear programs is presented. We show how to derive a family of upper bound factor-revealing programs (UPFRP), and that each such program can be solved by a computer to bound the approximation factor. Obtaining an UPFRP is straightforward, and can be used as an alternative to analytical proofs, that are usually very long and tedious. We apply this technique to the Metric Facility Location Problem (MFLP) and to a generalization where the distance function is a squared metric. We call this generalization the Squared Metric Facility Location Problem (SMFLP) and prove that there is no approximation factor better than 2.04, assuming \(\mbox{\rm P} \neq\mbox{\rm NP} \). Then, we analyze the best known algorithms for the MFLP based on primal-dual and LP-rounding techniques when they are applied to the SMFLP. We prove very tight bounds for these algorithms, and show that the LP-rounding algorithm achieves a ratio of 2.04, and therefore has the best factor for the SMFLP. We use UPFRPs in the dual-fitting analysis of the primal-dual algorithms for both the SMFLP and the MFLP, improving some of the previous analysis for the MFLP.

This research was partially supported by CNPq (grant numbers 306860/2010-4, 473867/2010-9, and 309657/2009-1) and FAPESP (grant number 2010/20710-4).

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© 2012 Springer-Verlag Berlin Heidelberg

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Fernandes, C.G., Meira, L.A.A., Miyazawa, F.K., Pedrosa, L.L.C. (2012). A Systematic Approach to Bound Factor Revealing LPs and Its Application to the Metric and Squared Metric Facility Location Problems. In: Gupta, A., Jansen, K., Rolim, J., Servedio, R. (eds) Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. APPROX RANDOM 2012 2012. Lecture Notes in Computer Science, vol 7408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32512-0_13

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  • DOI: https://doi.org/10.1007/978-3-642-32512-0_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32511-3

  • Online ISBN: 978-3-642-32512-0

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