A Randomized O(logn)-Competitive Algorithm for the Online Connected Facility Location Problem

The Connected Facility Location (CFL) is a network design problem that arises from a combination of the Uncapacitated Facility Location (FL) and the Steiner Tree (ST) problems. The Online Connected Facility Location problem (OCFL) is an online version of the CFL. San Felice et al. ( 2014 ) presented...

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Published inAlgorithmica Vol. 76; no. 4; pp. 1139 - 1157
Main Authors San Felice, Mário César, Williamson, David P., Lee, Orlando
Format Journal Article
LanguageEnglish
Published New York Springer US 01.12.2016
Springer Nature B.V
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ISSN0178-4617
1432-0541
DOI10.1007/s00453-016-0115-1

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Abstract The Connected Facility Location (CFL) is a network design problem that arises from a combination of the Uncapacitated Facility Location (FL) and the Steiner Tree (ST) problems. The Online Connected Facility Location problem (OCFL) is an online version of the CFL. San Felice et al. ( 2014 ) presented a randomized algorithm for the OCFL and proved that it is O ( log 2 n ) -competitive, where n is the number of clients. That algorithm combines the sample-and-augment framework of Gupta, Kumar, Pál, and Roughgarden with previous algorithms for the Online Facility Location (OFL) and the Online Steiner Tree (OST) problems. In this paper we use a more precise analysis to show that the same algorithm is O ( log n ) -competitive. Since there is a lower bound of Ω ( log n ) for this problem, our result achieves the best possible competitive ratio, asymptotically.
AbstractList The Connected Facility Location (CFL) is a network design problem that arises from a combination of the Uncapacitated Facility Location (FL) and the Steiner Tree (ST) problems. The Online Connected Facility Location problem (OCFL) is an online version of the CFL. San Felice et al. (2014) presented a randomized algorithm for the OCFL and proved that it is O ( log 2 n ) -competitive, where n is the number of clients. That algorithm combines the sample-and-augment framework of Gupta, Kumar, Pál, and Roughgarden with previous algorithms for the Online Facility Location (OFL) and the Online Steiner Tree (OST) problems. In this paper we use a more precise analysis to show that the same algorithm is O ( log n ) -competitive. Since there is a lower bound of Ω ( log n ) for this problem, our result achieves the best possible competitive ratio, asymptotically.
The Connected Facility Location (CFL) is a network design problem that arises from a combination of the Uncapacitated Facility Location (FL) and the Steiner Tree (ST) problems. The Online Connected Facility Location problem (OCFL) is an online version of the CFL. San Felice et al. ( 2014 ) presented a randomized algorithm for the OCFL and proved that it is O ( log 2 n ) -competitive, where n is the number of clients. That algorithm combines the sample-and-augment framework of Gupta, Kumar, Pál, and Roughgarden with previous algorithms for the Online Facility Location (OFL) and the Online Steiner Tree (OST) problems. In this paper we use a more precise analysis to show that the same algorithm is O ( log n ) -competitive. Since there is a lower bound of Ω ( log n ) for this problem, our result achieves the best possible competitive ratio, asymptotically.
Author San Felice, Mário César
Williamson, David P.
Lee, Orlando
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Keywords Online algorithms
Approximation algorithms
Randomized algorithms
Connected facility location
Competitive analysis
Steiner tree
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References Swamy, C., Kumar, A.: Primal-dual algorithms for connected facility location problems. Algorithmica 40(4), 245–269 (2004)
LiSA 1.488 approximation algorithm for the uncapacitated facility location problemInf. Comput.20132224558300096110.1016/j.ic.2012.01.0071281.68236
FotakisDOn the competitive ratio for online facility locationAlgorithmica200850157236615310.1007/s00453-007-9049-y1154.68573
San Felice: M.C., Williamson, D.P., Lee, O.: The online connected facility location problem. In: Latin American Theoretical INformatics, 11th Symposium, LATIN 2014, no. 8392 in Lecture Notes in Computer Science, pp. 574–585. Springer, Berlin, Heidelberg (2014)
VaziraniVApproximation Algorithms2003GermanySpringer-Verlag Berlin Heidelberg10.1007/978-3-662-04565-7
FotakisDOnline and incremental algorithms for facility locationSIGACT News20114219713110.1145/1959045.1959065
Gupta, A., Kumar, A., Pál, M., Roughgarden, T.: Approximation via cost sharing: Simpler and better approximation algorithms for network design. J. ACM 54(3) (2007). http://portal.acm.org/citation.cfm?doid=1236457.1236458. Article 11
HasanMKJungHChwaKYApproximation algorithms for connected facility locationJ. Comb. Optim.200816155172241527710.1007/s10878-007-9130-01159.90022
Shmoys, D.B.: Approximation algorithms for facility location problems. In: Approximation Algorithms for Combinatorial Optimization, 3rd International Workshop, APPROX 2000, no. 1913 in Lecture Notes in Computer Science, pp. 27–33. Springer, Berlin, Germany (2000). http://dl.acm.org/citation.cfm?id=646688.703104
WilliamsonDPShmoysDBThe Design of Approximation Algorithms2011New York, NYCambridge University Press10.1017/CBO97805119217351219.90004
MahdianMYeYZhangJApproximation algorithms for metric facility location problemsSIAM J. Comput.200636411432224773410.1137/S00975397034357161151.90590
ImaseMWaxmanBMDynamic Steiner tree problemSIAM J. Discrete Math.199143369384110594310.1137/04040330739.05030
FotakisDA primal-dual algorithm for online non-uniform facility locationJ. Discrete Algorithms200751141148228705310.1016/j.jda.2006.03.0011134.90021
Bartal, Y., Fiat, A., Rabani, Y.: : Competitive Algorithms for Distributed Data Management. J. Comput. Syst. Sci. 51(3), 341–358. doi:10.1006/jcss.1995.1073
ByrkaJAardalKAn optimal bifactor approximation algorithm for the metric facility location problemSIAM J. Comput.20103922122231260789910.1137/0707089011205.90173
Nagarajan, C., Williamson, D.P.: Offline and online facility leasing. Discrete Optim. 10(4), 361–370 (2013). doi:10.1016/j.disopt.2013.10.001. http://www.sciencedirect.com/science/article
Umboh, S.: Online network design algorithms via hierarchical decompositions. In: Proceedings of the 26th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2015). To appear. CoRR, abs/1410.4240
Eisenbrand, F., Grandoni, F., Rothvoß, T., Schäfer, G.: Connected facility location via random facility sampling and core detouring. J. Comput. Syst. Sci. 76(8), 709–726 (2010). http://linkinghub.elsevier.com/retrieve
Meyerson, A.: Online facility location. In: Foundations of Computer Science, 42nd IEEE Symposium, FOCS 2001, pp. 426–431 (2001). http://dl.acm.org/citation.cfm?id=874063.875567
JungHHasanMKChwaKYA 6.55 factor primal-dual approximation algorithm for the connected facility location problemJ. Comb. Optim.200918258271255116710.1007/s10878-009-9227-81180.90349
BorodinAEl-YanivROnline Computation and Competitive Analysis1998New York, NYCambridge University Press0931.68015
BuchbinderNNaorJSThe design of competitive online algorithms via a primal-dual approachFound. Trends Theor. Comput. Sci.2009393263250649610.1561/04000000241190.68083
GuptaASrinivasanATardosÉCost-sharing mechanisms for network designAlgorithmica20085098119236615610.1007/s00453-007-9065-y1169.68314
References_xml – reference: WilliamsonDPShmoysDBThe Design of Approximation Algorithms2011New York, NYCambridge University Press10.1017/CBO97805119217351219.90004
– reference: FotakisDOnline and incremental algorithms for facility locationSIGACT News20114219713110.1145/1959045.1959065
– reference: LiSA 1.488 approximation algorithm for the uncapacitated facility location problemInf. Comput.20132224558300096110.1016/j.ic.2012.01.0071281.68236
– reference: JungHHasanMKChwaKYA 6.55 factor primal-dual approximation algorithm for the connected facility location problemJ. Comb. Optim.200918258271255116710.1007/s10878-009-9227-81180.90349
– reference: Umboh, S.: Online network design algorithms via hierarchical decompositions. In: Proceedings of the 26th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2015). To appear. CoRR, abs/1410.4240
– reference: Swamy, C., Kumar, A.: Primal-dual algorithms for connected facility location problems. Algorithmica 40(4), 245–269 (2004)
– reference: Nagarajan, C., Williamson, D.P.: Offline and online facility leasing. Discrete Optim. 10(4), 361–370 (2013). doi:10.1016/j.disopt.2013.10.001. http://www.sciencedirect.com/science/article/
– reference: ImaseMWaxmanBMDynamic Steiner tree problemSIAM J. Discrete Math.199143369384110594310.1137/04040330739.05030
– reference: Bartal, Y., Fiat, A., Rabani, Y.: : Competitive Algorithms for Distributed Data Management. J. Comput. Syst. Sci. 51(3), 341–358. doi:10.1006/jcss.1995.1073
– reference: HasanMKJungHChwaKYApproximation algorithms for connected facility locationJ. Comb. Optim.200816155172241527710.1007/s10878-007-9130-01159.90022
– reference: San Felice: M.C., Williamson, D.P., Lee, O.: The online connected facility location problem. In: Latin American Theoretical INformatics, 11th Symposium, LATIN 2014, no. 8392 in Lecture Notes in Computer Science, pp. 574–585. Springer, Berlin, Heidelberg (2014)
– reference: ByrkaJAardalKAn optimal bifactor approximation algorithm for the metric facility location problemSIAM J. Comput.20103922122231260789910.1137/0707089011205.90173
– reference: Gupta, A., Kumar, A., Pál, M., Roughgarden, T.: Approximation via cost sharing: Simpler and better approximation algorithms for network design. J. ACM 54(3) (2007). http://portal.acm.org/citation.cfm?doid=1236457.1236458. Article 11
– reference: Eisenbrand, F., Grandoni, F., Rothvoß, T., Schäfer, G.: Connected facility location via random facility sampling and core detouring. J. Comput. Syst. Sci. 76(8), 709–726 (2010). http://linkinghub.elsevier.com/retrieve/
– reference: FotakisDA primal-dual algorithm for online non-uniform facility locationJ. Discrete Algorithms200751141148228705310.1016/j.jda.2006.03.0011134.90021
– reference: Meyerson, A.: Online facility location. In: Foundations of Computer Science, 42nd IEEE Symposium, FOCS 2001, pp. 426–431 (2001). http://dl.acm.org/citation.cfm?id=874063.875567
– reference: FotakisDOn the competitive ratio for online facility locationAlgorithmica200850157236615310.1007/s00453-007-9049-y1154.68573
– reference: GuptaASrinivasanATardosÉCost-sharing mechanisms for network designAlgorithmica20085098119236615610.1007/s00453-007-9065-y1169.68314
– reference: BuchbinderNNaorJSThe design of competitive online algorithms via a primal-dual approachFound. Trends Theor. Comput. Sci.2009393263250649610.1561/04000000241190.68083
– reference: Shmoys, D.B.: Approximation algorithms for facility location problems. In: Approximation Algorithms for Combinatorial Optimization, 3rd International Workshop, APPROX 2000, no. 1913 in Lecture Notes in Computer Science, pp. 27–33. Springer, Berlin, Germany (2000). http://dl.acm.org/citation.cfm?id=646688.703104
– reference: MahdianMYeYZhangJApproximation algorithms for metric facility location problemsSIAM J. Comput.200636411432224773410.1137/S00975397034357161151.90590
– reference: BorodinAEl-YanivROnline Computation and Competitive Analysis1998New York, NYCambridge University Press0931.68015
– reference: VaziraniVApproximation Algorithms2003GermanySpringer-Verlag Berlin Heidelberg10.1007/978-3-662-04565-7
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Snippet The Connected Facility Location (CFL) is a network design problem that arises from a combination of the Uncapacitated Facility Location (FL) and the Steiner...
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SubjectTerms Algorithm Analysis and Problem Complexity
Algorithms
Clients
Competition
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Mathematics of Computing
Operations research
Production planning
Randomization
Site selection
Theory of Computation
Title A Randomized O(logn)-Competitive Algorithm for the Online Connected Facility Location Problem
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