Scenario Submodular Cover

  • Nathaniel Grammel
  • Lisa Hellerstein
  • Devorah Kletenik
  • Patrick Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10138)

Abstract

We introduce the Scenario Submodular Cover problem. In this problem, the goal is to produce a cover with minimum expected cost, with respect to an empirical joint probability distribution, given as input by a weighted sample of realizations. The problem is a counterpart to the Stochastic Submodular Cover problem studied by Golovin and Krause [6], which assumes independent variables. We give two approximation algorithms for Scenario Submodular Cover. Assuming an integer-valued utility function and integer weights, the first achieves an approximation factor of \(O(\log Qm)\), where m is the sample size and Q is the goal utility. The second, simpler algorithm achieves an approximation factor of \(O(\log QW)\), where W is the sum of the weights. We achieve our bounds by building on previous related work (in [4, 6, 15]) and by exploiting a technique we call the Scenario-OR modification. We apply these algorithms to a new problem, Scenario Boolean Function Evaluation. Our results have applciations to other problems involving distributions that are explicitly specified by their support.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nathaniel Grammel
    • 1
  • Lisa Hellerstein
    • 1
  • Devorah Kletenik
    • 2
  • Patrick Lin
    • 3
  1. 1.Department of Computer Science and EngineeringNYU Tandon School of EngineeringBrooklynUSA
  2. 2.Department of Computer and Information Science, Brooklyn CollegeCity University of New YorkNew YorkUSA
  3. 3.Department of Computer ScienceUniversity of Illinois at Urbana-ChampaignChampaignUSA

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