Approximating the online set multicover problems via randomized winnowing

Piotr Berman, Bhaskar DasGupta

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    In this paper, we consider the weighted online set k-multicover problem. In this problem, we have a universe V of elements, a family S of subsets of V with a positive real cost for every S ∈ S, and a "coverage factor" (positive integer) k. A subset {i0, i1, ...} ⊆ V of elements are presented online in an arbitrary order. When each element ip is presented, we are also told the collection of all (at least k) sets Sip ⊆ S and their costs to which ip belongs and we need to select additional sets from Sip if necessary such that our collection of selected sets contains at leastk sets that contain the element ip. The goal is to minimize the total cost of the selected sets.11Our algorithm and competitive ratio bounds can be extended to the case when a set can be selected at most a prespecified number of times instead of just once; we do not report these extensions for simplicity and also because they have no relevance to the biological applications that motivated our work. In this paper, we describe a new randomized algorithm for the online multicover problem based on a randomized version of the winnowing approach of [N. Littlestone, Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm, Machine Learning 2 (1988) 285-318]. This algorithm generalizes and improves some earlier results in [N. Alon, B. Awerbuch, Y. Azar, N. Buchbinder, J. Naor, A general approach to online network optimization problems, in: Proceedings of the 15th ACM-SIAM Symposium on Discrete Algorithms, 2004, pp. 570-579; N. Alon, B. Awerbuch, Y. Azar, N. Buchbinder, J. Naor, The online set cover problem, in: Proceedings of the 35th Annual ACM Symposium on the Theory of Computing, 2003, pp. 100-105]. We also discuss lower bounds on competitive ratios for deterministic algorithms for general k based on the approaches in [N. Alon, B. Awerbuch, Y. Azar, N. Buchbinder, J. Naor, The online set cover problem, in: Proceedings of the 35th Annual ACM Symposium on the Theory of Computing, 2003, pp. 100-105].

    Original languageEnglish (US)
    Pages (from-to)54-71
    Number of pages18
    JournalTheoretical Computer Science
    Volume393
    Issue number1-3
    DOIs
    StatePublished - Mar 20 2008

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    Set Cover
    Competitive Ratio
    Annual
    Costs
    Online Optimization
    Network Optimization
    Subset
    Computing
    Deterministic Algorithm
    Randomized Algorithms
    Learning systems
    Simplicity
    Machine Learning
    Coverage
    Attribute
    Lower bound
    Optimization Problem
    Minimise
    Generalise
    Integer

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)

    Cite this

    Berman, Piotr ; DasGupta, Bhaskar. / Approximating the online set multicover problems via randomized winnowing. In: Theoretical Computer Science. 2008 ; Vol. 393, No. 1-3. pp. 54-71.
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    abstract = "In this paper, we consider the weighted online set k-multicover problem. In this problem, we have a universe V of elements, a family S of subsets of V with a positive real cost for every S ∈ S, and a {"}coverage factor{"} (positive integer) k. A subset {i0, i1, ...} ⊆ V of elements are presented online in an arbitrary order. When each element ip is presented, we are also told the collection of all (at least k) sets Sip ⊆ S and their costs to which ip belongs and we need to select additional sets from Sip if necessary such that our collection of selected sets contains at leastk sets that contain the element ip. The goal is to minimize the total cost of the selected sets.11Our algorithm and competitive ratio bounds can be extended to the case when a set can be selected at most a prespecified number of times instead of just once; we do not report these extensions for simplicity and also because they have no relevance to the biological applications that motivated our work. In this paper, we describe a new randomized algorithm for the online multicover problem based on a randomized version of the winnowing approach of [N. Littlestone, Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm, Machine Learning 2 (1988) 285-318]. This algorithm generalizes and improves some earlier results in [N. Alon, B. Awerbuch, Y. Azar, N. Buchbinder, J. Naor, A general approach to online network optimization problems, in: Proceedings of the 15th ACM-SIAM Symposium on Discrete Algorithms, 2004, pp. 570-579; N. Alon, B. Awerbuch, Y. Azar, N. Buchbinder, J. Naor, The online set cover problem, in: Proceedings of the 35th Annual ACM Symposium on the Theory of Computing, 2003, pp. 100-105]. We also discuss lower bounds on competitive ratios for deterministic algorithms for general k based on the approaches in [N. Alon, B. Awerbuch, Y. Azar, N. Buchbinder, J. Naor, The online set cover problem, in: Proceedings of the 35th Annual ACM Symposium on the Theory of Computing, 2003, pp. 100-105].",
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    Approximating the online set multicover problems via randomized winnowing. / Berman, Piotr; DasGupta, Bhaskar.

    In: Theoretical Computer Science, Vol. 393, No. 1-3, 20.03.2008, p. 54-71.

    Research output: Contribution to journalArticle

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    N2 - In this paper, we consider the weighted online set k-multicover problem. In this problem, we have a universe V of elements, a family S of subsets of V with a positive real cost for every S ∈ S, and a "coverage factor" (positive integer) k. A subset {i0, i1, ...} ⊆ V of elements are presented online in an arbitrary order. When each element ip is presented, we are also told the collection of all (at least k) sets Sip ⊆ S and their costs to which ip belongs and we need to select additional sets from Sip if necessary such that our collection of selected sets contains at leastk sets that contain the element ip. The goal is to minimize the total cost of the selected sets.11Our algorithm and competitive ratio bounds can be extended to the case when a set can be selected at most a prespecified number of times instead of just once; we do not report these extensions for simplicity and also because they have no relevance to the biological applications that motivated our work. In this paper, we describe a new randomized algorithm for the online multicover problem based on a randomized version of the winnowing approach of [N. Littlestone, Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm, Machine Learning 2 (1988) 285-318]. This algorithm generalizes and improves some earlier results in [N. Alon, B. Awerbuch, Y. Azar, N. Buchbinder, J. Naor, A general approach to online network optimization problems, in: Proceedings of the 15th ACM-SIAM Symposium on Discrete Algorithms, 2004, pp. 570-579; N. Alon, B. Awerbuch, Y. Azar, N. Buchbinder, J. Naor, The online set cover problem, in: Proceedings of the 35th Annual ACM Symposium on the Theory of Computing, 2003, pp. 100-105]. We also discuss lower bounds on competitive ratios for deterministic algorithms for general k based on the approaches in [N. Alon, B. Awerbuch, Y. Azar, N. Buchbinder, J. Naor, The online set cover problem, in: Proceedings of the 35th Annual ACM Symposium on the Theory of Computing, 2003, pp. 100-105].

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