Local Approximability of Max-Min and Min-Max Linear Programs

In a max-min LP , the objective is to maximise ω subject to A x ≤ 1 , C x ≥ ω 1 , and x ≥ 0 . In a min-max LP , the objective is to minimise ρ subject to A x ≤ ρ 1 , C x ≥ 1 , and x ≥ 0 . The matrices A and C are nonnegative and sparse: each row a i of A has at most Δ I positive elements, and each r...

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Veröffentlicht in:Theory of computing systems Jg. 49; H. 4; S. 672 - 697
Hauptverfasser: Floréen, Patrik, Hassinen, Marja, Kaasinen, Joel, Kaski, Petteri, Musto, Topi, Suomela, Jukka
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer-Verlag 01.11.2011
Springer Nature B.V
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ISSN:1432-4350, 1433-0490
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Abstract In a max-min LP , the objective is to maximise ω subject to A x ≤ 1 , C x ≥ ω 1 , and x ≥ 0 . In a min-max LP , the objective is to minimise ρ subject to A x ≤ ρ 1 , C x ≥ 1 , and x ≥ 0 . The matrices A and C are nonnegative and sparse: each row a i of A has at most Δ I positive elements, and each row c k of C has at most Δ K positive elements. We study the approximability of max-min LPs and min-max LPs in a distributed setting; in particular, we focus on local algorithms (constant-time distributed algorithms). We show that for any Δ I ≥2, Δ K ≥2, and ε >0 there exists a local algorithm that achieves the approximation ratio Δ I (1−1/Δ K )+ ε . We also show that this result is the best possible: no local algorithm can achieve the approximation ratio Δ I (1−1/Δ K ) for any Δ I ≥2 and Δ K ≥2.
AbstractList In a max-min LP , the objective is to maximise ω subject to A x ≤ 1 , C x ≥ ω 1 , and x ≥ 0 . In a min-max LP , the objective is to minimise ρ subject to A x ≤ ρ 1 , C x ≥ 1 , and x ≥ 0 . The matrices A and C are nonnegative and sparse: each row a i of A has at most Δ I positive elements, and each row c k of C has at most Δ K positive elements. We study the approximability of max-min LPs and min-max LPs in a distributed setting; in particular, we focus on local algorithms (constant-time distributed algorithms). We show that for any Δ I ≥2, Δ K ≥2, and ε >0 there exists a local algorithm that achieves the approximation ratio Δ I (1−1/Δ K )+ ε . We also show that this result is the best possible: no local algorithm can achieve the approximation ratio Δ I (1−1/Δ K ) for any Δ I ≥2 and Δ K ≥2.
In a max-min LP, the objective is to maximise omega subject to A x less than or equal to 1, C x greater than or equal to omega 1, and x greater than or equal to 0. In a min-max LP, the objective is to minimise rho subject to A x less than or equal to rho 1, C x greater than or equal to 1, and x greater than or equal to 0. The matrices A and C are nonnegative and sparse: each row a sub( )iof A has at most Delta sub( )Ipositive elements, and each row c sub( )kof C has at most Delta sub( )Kpositive elements. We study the approximability of max-min LPs and min-max LPs in a distributed setting; in particular, we focus on local algorithms (constant-time distributed algorithms). We show that for any Delta sub( )I.2, Delta sub( )K.2, and epsilon >0 there exists a local algorithm that achieves the approximation ratio Delta sub( )I1-1/ Delta sub( )K+ epsilon . We also show that this result is the best possible: no local algorithm can achieve the approximation ratio Delta sub( )I1-1/ Delta sub( )K for any Delta sub( )I.2 and Delta sub( )K.2.
Issue Title: Special Issue: Parallelism in Algorithms and Architectures In a max-min LP, the objective is to maximise ... subject to ... , and ... . In a min-max LP, the objective is to minimise ρ subject to ... , ... , and ... . The matrices A and C are nonnegative and sparse: each row ... of A has at most ... positive elements, and each row ... of C has at most ... positive elements. We study the approximability of max-min LPs and min-max LPs in a distributed setting; in particular, we focus on local algorithms (constant-time distributed algorithms). We show that for any ... , ... , and ... there exists a local algorithm that achieves the approximation ratio ... . We also show that this result is the best possible: no local algorithm can achieve the approximation ratio ... for any ... and ... . (ProQuest: ... denotes formulae and non-USASCII text omitted)
Author Kaski, Petteri
Floréen, Patrik
Musto, Topi
Suomela, Jukka
Kaasinen, Joel
Hassinen, Marja
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CitedBy_id crossref_primary_10_1016_j_tcs_2020_12_017
crossref_primary_10_1145_2431211_2431223
crossref_primary_10_1007_s00446_013_0202_3
crossref_primary_10_1007_s00446_015_0245_8
crossref_primary_10_1007_s10514_019_09856_1
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Approximation algorithms
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Linear programs
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Snippet In a max-min LP , the objective is to maximise ω subject to A x ≤ 1 , C x ≥ ω 1 , and x ≥ 0 . In a min-max LP , the objective is to minimise ρ subject to A x ≤...
Issue Title: Special Issue: Parallelism in Algorithms and Architectures In a max-min LP, the objective is to maximise ... subject to ... , and ... . In a...
In a max-min LP, the objective is to maximise omega subject to A x less than or equal to 1, C x greater than or equal to omega 1, and x greater than or equal...
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SubjectTerms Algorithms
Approximation
Bandwidths
Communication
Computation
Computer Science
Hierarchies
Internet access
Internet service providers
Linear programming
Mathematical analysis
Matrices
Matrix methods
Sensors
Studies
Theory of Computation
Wireless networks
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