Minimum latency submodular cover

  • Authors:
  • Sungjin Im;Viswanath Nagarajan;Ruben van der Zwaan

  • Affiliations:
  • Department of Computer Science, University of Illinois;IBM T. J. Watson Research Center;Maastricht University, The Netherlands

  • Venue:
  • ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
  • Year:
  • 2012

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Abstract

We study the submodular ranking problem in the presence of metric costs. The input to the minimum latency submodular cover (MLSC) problem consists of a metric (V,d) with source r∈V and m monotone submodular functions f1, f2, ..., fm: 2V→[0,1]. The goal is to find a path originating at r that minimizes the total cover time of all functions; the cover time of function fi is the smallest value t such that fi has value one for the vertices visited within distance t along the path. This generalizes many previously studied problems, such as submodular ranking [1] when the metric is uniform, and group Steiner tree [14] when m=1 and f1 is a coverage function. We give a polynomial time $O(\log \frac{1}{\epsilon } \cdot \log^{2+\delta} |V|)$-approximation algorithm for MLSC, where ε0 is the smallest non-zero marginal increase of any $\{f_i\}_{i=1}^m$ and δ0 is any constant. This result is enabled by a simpler analysis of the submodular ranking algorithm from [1]. We also consider the stochastic submodular ranking problem where elements V have random instantiations, and obtain an adaptive algorithm with an O(log1/ ε) approximation ratio, which is best possible. This result also generalizes several previously studied stochastic problems, eg. adaptive set cover [15] and shared filter evaluation [24,23].