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31The Subset Sum Problem Reducing Time Complexity of NP-Completeness with Quantum Search Abstract The Subset Sum Problem is a member of the NPcomplete class, so no known polynomial time algorithm exists for it. Although th

The Subset Sum Problem Reducing Time Complexity of NP-Completeness with Quantum Search Abstract The Subset Sum Problem is a member of the NPcomplete class, so no known polynomial time algorithm exists for it. Although th

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Source URL: ciim.usf.edu

- Date: 1980-01-01 00:00:00
    32A self-applicable partial evaluator for a subset of Haskell Silvano Dal-Zilio

    A self-applicable partial evaluator for a subset of Haskell Silvano Dal-Zilio

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    Source URL: repository.readscheme.org

    - Date: 2010-10-22 08:06:07
      33OWL3 Cheat Sheet This

      OWL3 Cheat Sheet This "cheat sheet" is a subset of the new words added to OTCWL2014 (aka OWL3), effective 10 April 2015 for club and tournament play. New Twos (4) DA

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      Source URL: www.seattlescrabble.org

      - Date: 2015-02-16 20:46:29
        34Two-dimensional Subset Selection for Hypervolume and Epsilon-Indicator Karl Bringmann Tobias Friedrich

        Two-dimensional Subset Selection for Hypervolume and Epsilon-Indicator Karl Bringmann Tobias Friedrich

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        Source URL: people.mpi-inf.mpg.de

        - Date: 2016-01-03 06:46:36
          351. Metrically approximate subgroups Let G be a group with a metric d invariant under left and right translations. A (K, r)-approximate subgroup is a subset X of G containing 1, such that the product set XX is covered by

          1. Metrically approximate subgroups Let G be a group with a metric d invariant under left and right translations. A (K, r)-approximate subgroup is a subset X of G containing 1, such that the product set XX is covered by

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          Source URL: www.ma.huji.ac.il

          - Date: 2014-08-22 07:10:31
            36We connect high-dimensional subset selection and submodular maximization. Our results extend the work of Das and Kempefrom the setting of linear regression to arbitrary objective functions. This connection allows

            We connect high-dimensional subset selection and submodular maximization. Our results extend the work of Das and Kempefrom the setting of linear regression to arbitrary objective functions. This connection allows

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            Source URL: mmds-data.org

            - Date: 2016-06-23 15:50:48
              37Worksheet on Inclusion-Exclusion October 11, 2015 This is a long worksheet and it will probably span two days. Might I suggest that you refrain from working on it between the classes so you can enjoy the discovery collab

              Worksheet on Inclusion-Exclusion October 11, 2015 This is a long worksheet and it will probably span two days. Might I suggest that you refrain from working on it between the classes so you can enjoy the discovery collab

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              Source URL: math.colorado.edu

              Language: English - Date: 2015-12-04 19:21:55
              38SETS: A Basic Set Theory Package Francis J. Wright School of Mathematical Sciences Queen Mary and Westfield College University of London Mile End Road, London E1 4NS, UK.

              SETS: A Basic Set Theory Package Francis J. Wright School of Mathematical Sciences Queen Mary and Westfield College University of London Mile End Road, London E1 4NS, UK.

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              Source URL: reduce-algebra.com

              Language: English - Date: 2008-12-30 11:47:48
              39In this talk, I will discuss Ligra, a shared-memory graph processing framework that has two very simple routines, one for mapping over edges and one for mapping over vertices. The routines can be applied to any subset of

              In this talk, I will discuss Ligra, a shared-memory graph processing framework that has two very simple routines, one for mapping over edges and one for mapping over vertices. The routines can be applied to any subset of

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              Source URL: mmds-data.org

              - Date: 2016-06-23 15:50:48
                40Space-Efficient Randomized Algorithms for K-SUM Joshua R. Wang Stanford University, Stanford CA 94305, USA

                Space-Efficient Randomized Algorithms for K-SUM Joshua R. Wang Stanford University, Stanford CA 94305, USA

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                Source URL: web.stanford.edu

                Language: English - Date: 2014-04-18 04:16:48