Dual-bounded generating problems: Efficient and inefficient points for discrete probability distributions and sparse boxes for multidimensional data

  • Authors:

  • Leonid Khachiyan;Endre Boros;Khaled Elbassioni;Vladimir Gurvich;Kazuhisa Makino

  • Affiliations:

  • Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854-8003, United States;RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway, NJ 08854-8003, United States;Max-Planck-Institut für Informatik, Saarbrücken, Germany;RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway, NJ 08854-8003, United States;Department of Mathematical Informatics, Graduate School of Information and Technology, University of Tokyo, Tokyo, 113-8656, Japan

  • Venue:
  • Theoretical Computer Science
  • Year:
  • 2007

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Abstract

We show that |X|@?n|Y| must hold for two finite sets X,Y@?R^n whenever they can be separated by a nonnegative linear function such that X is above Y and the componentwise minimum of any two distinct points in X is dominated by some point in Y. As a consequence, we obtain an incremental quasi-polynomial time algorithm for generating all maximal integer feasible solutions for a given monotone system of separable inequalities, for generating all p-inefficient points of a given discrete probability distribution, and for generating all maximal hyper-rectangles which contain a specified fraction of points of a given set in R^n. This provides a substantial improvement over previously known exponential time algorithms for these generation problems related to Integer and Stochastic Programming, and Data Mining. Furthermore, we give an incremental polynomial time generation algorithm for monotone systems with fixed number of separable inequalities, implying that for discrete probability distributions with independent coordinates, both p-efficient and p-inefficient points can be separately generated in incremental polynomial time.