A deterministic fully polynomial time approximation scheme for counting integer knapsack solutions made easy
Given n elements with nonnegative integer weights w=(w1,…,wn), an integer capacity C and positive integer ranges u=(u1,…,un), we consider the counting version of the classic integer knapsack problem: find the number of distinct multisets whose weights add up to at most C. We give a deterministic alg...
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| Vydáno v: | Theoretical computer science Ročník 645; s. 41 - 47 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
13.09.2016
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| ISSN: | 0304-3975, 1879-2294 |
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| Abstract | Given n elements with nonnegative integer weights w=(w1,…,wn), an integer capacity C and positive integer ranges u=(u1,…,un), we consider the counting version of the classic integer knapsack problem: find the number of distinct multisets whose weights add up to at most C. We give a deterministic algorithm that estimates the number of solutions to within relative error ϵ in time polynomial in n, logU and 1/ϵ, where U=maxiui. More precisely, our algorithm runs in O(n3log2UϵlognlogUϵ) time. This is an improvement of n2 and 1/ϵ (up to log terms) over the best known deterministic algorithm by Gopalan et al. (2011) [5]. Our algorithm is relatively simple, and its analysis is rather elementary. Our results are achieved by means of a careful formulation of the problem as a dynamic program, using the notion of binding constraints. |
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| AbstractList | Given n elements with nonnegative integer weights , an integer capacity C and positive integer ranges , we consider the counting version of the classic integer knapsack problem: find the number of distinct multisets whose weights add up to at most C. We give a deterministic algorithm that estimates the number of solutions to within relative error in time polynomial in n, and , where . More precisely, our algorithm runs in time. This is an improvement of and (up to log terms) over the best known deterministic algorithm by Gopalan et al. (2011) [5]. Our algorithm is relatively simple, and its analysis is rather elementary. Our results are achieved by means of a careful formulation of the problem as a dynamic program, using the notion of binding constraints. Given n elements with nonnegative integer weights w=(w1,…,wn), an integer capacity C and positive integer ranges u=(u1,…,un), we consider the counting version of the classic integer knapsack problem: find the number of distinct multisets whose weights add up to at most C. We give a deterministic algorithm that estimates the number of solutions to within relative error ϵ in time polynomial in n, logU and 1/ϵ, where U=maxiui. More precisely, our algorithm runs in O(n3log2UϵlognlogUϵ) time. This is an improvement of n2 and 1/ϵ (up to log terms) over the best known deterministic algorithm by Gopalan et al. (2011) [5]. Our algorithm is relatively simple, and its analysis is rather elementary. Our results are achieved by means of a careful formulation of the problem as a dynamic program, using the notion of binding constraints. |
| Author | Halman, Nir |
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| Cites_doi | 10.1137/130925153 10.1287/moor.1090.0391 10.1137/11083976X |
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| Keywords | Integer knapsack Dynamic programming Binding constraints K-approximating sets and functions Approximate counting |
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| References | Rizzi, Tomescu (br0080) 2014 Megiddo (br0040) 1989; vol. 12 Halman, Klabjan, Li, Orlin, Simchi-Levi (br0100) 2014; 28 Dyer (br0030) 2003 Jerrum, Sinclair (br0010) 1996 Meka, Zuckerman (br0070) 2010 Štefankovič, Vempala, Vigoda (br0020) 2012; 41 Gopalan, Klivans, Meka (br0060) 2010 Gopalan, Klivans, Meka, Štefankovič, Vempala, Vigoda (br0050) 2011 Halman, Klabjan, Mostagir, Orlin, Simchi-Levi (br0090) 2009; 34 Gopalan (10.1016/j.tcs.2016.06.015_br0050) 2011 Štefankovič (10.1016/j.tcs.2016.06.015_br0020) 2012; 41 Dyer (10.1016/j.tcs.2016.06.015_br0030) 2003 Meka (10.1016/j.tcs.2016.06.015_br0070) 2010 Rizzi (10.1016/j.tcs.2016.06.015_br0080) 2014 Halman (10.1016/j.tcs.2016.06.015_br0090) 2009; 34 Halman (10.1016/j.tcs.2016.06.015_br0100) 2014; 28 Jerrum (10.1016/j.tcs.2016.06.015_br0010) 1996 Megiddo (10.1016/j.tcs.2016.06.015_br0040) 1989; vol. 12 Gopalan (10.1016/j.tcs.2016.06.015_br0060) |
| References_xml | – start-page: 482 year: 1996 end-page: 520 ident: br0010 article-title: The Markov chain Monte Carlo method: an approach to approximate counting and integration publication-title: Approximation Algorithms for NP-Hard Problems – year: 2010 ident: br0060 article-title: Polynomial-time approximation schemes for Knapsack and related counting problems using branching programs – start-page: 762 year: 2014 end-page: 773 ident: br0080 article-title: Faster FPTASes for counting and random generation of knapsack solutions publication-title: Proceedings of the 22nd Annual European Symposium on Algorithms (ESA) – volume: 41 start-page: 356 year: 2012 end-page: 366 ident: br0020 article-title: A deterministic polynomial-time approximation scheme for counting knapsack solutions publication-title: SIAM J. Sci. Comput. – start-page: 693 year: 2003 end-page: 699 ident: br0030 article-title: Approximate counting by dynamic programming publication-title: Proceedings of the 35th Annual ACM Symposium on Theory of Computing (STOC) – volume: vol. 12 start-page: 225 year: 1989 end-page: 268 ident: br0040 article-title: On the complexity of linear programming publication-title: Advances in Economic Theory – start-page: 817 year: 2011 end-page: 826 ident: br0050 article-title: An FPTAS for #Knapsack and related counting problems publication-title: IEEE 52nd Annual Symposium on Foundations of Computer Science (FOCS) – volume: 28 start-page: 1725 year: 2014 end-page: 1796 ident: br0100 article-title: Fully polynomial time approximation schemes for stochastic dynamic programs publication-title: SIAM J. Discrete Math. – start-page: 427 year: 2010 end-page: 436 ident: br0070 article-title: Pseudorandom generators for polynomial threshold functions publication-title: Proceedings of the 42nd ACM Symposium on Theory of Computing (STOC) – volume: 34 start-page: 674 year: 2009 end-page: 685 ident: br0090 article-title: A fully polynomial time approximation scheme for single-item stochastic inventory control with discrete demand publication-title: Math. Oper. Res. – volume: 28 start-page: 1725 year: 2014 ident: 10.1016/j.tcs.2016.06.015_br0100 article-title: Fully polynomial time approximation schemes for stochastic dynamic programs publication-title: SIAM J. Discrete Math. doi: 10.1137/130925153 – ident: 10.1016/j.tcs.2016.06.015_br0060 – start-page: 693 year: 2003 ident: 10.1016/j.tcs.2016.06.015_br0030 article-title: Approximate counting by dynamic programming – start-page: 817 year: 2011 ident: 10.1016/j.tcs.2016.06.015_br0050 article-title: An FPTAS for #Knapsack and related counting problems – start-page: 762 year: 2014 ident: 10.1016/j.tcs.2016.06.015_br0080 article-title: Faster FPTASes for counting and random generation of knapsack solutions – volume: vol. 12 start-page: 225 year: 1989 ident: 10.1016/j.tcs.2016.06.015_br0040 article-title: On the complexity of linear programming – volume: 34 start-page: 674 year: 2009 ident: 10.1016/j.tcs.2016.06.015_br0090 article-title: A fully polynomial time approximation scheme for single-item stochastic inventory control with discrete demand publication-title: Math. Oper. Res. doi: 10.1287/moor.1090.0391 – volume: 41 start-page: 356 year: 2012 ident: 10.1016/j.tcs.2016.06.015_br0020 article-title: A deterministic polynomial-time approximation scheme for counting knapsack solutions publication-title: SIAM J. Sci. Comput. doi: 10.1137/11083976X – start-page: 482 year: 1996 ident: 10.1016/j.tcs.2016.06.015_br0010 article-title: The Markov chain Monte Carlo method: an approach to approximate counting and integration – start-page: 427 year: 2010 ident: 10.1016/j.tcs.2016.06.015_br0070 article-title: Pseudorandom generators for polynomial threshold functions |
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| Snippet | Given n elements with nonnegative integer weights w=(w1,…,wn), an integer capacity C and positive integer ranges u=(u1,…,un), we consider the counting version... Given n elements with nonnegative integer weights , an integer capacity C and positive integer ranges , we consider the counting version of the classic integer... |
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| SubjectTerms | Algorithms Approximate counting Approximation Binding Binding constraints Dynamic programming Estimates Integer knapsack Integers K-approximating sets and functions Mathematical analysis Mathematical models Polynomials |
| Title | A deterministic fully polynomial time approximation scheme for counting integer knapsack solutions made easy |
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