The minimum expectation selection problem

  • Authors:

  • David Eppstein;George S. Lueker

  • Affiliations:

  • Department of Information and Computer Science, University of California, Irvine, Irvine, California;Department of Information and Computer Science, University of California, Irvine, Irvine, California

  • Venue:
  • Random Structures & Algorithms - Special issue: Proceedings of the tenth international conference "Random structures and algorithms"
  • Year:
  • 2002

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Abstract

We define the min-min expectation selection problem (resp. max-min expectation selection problem) to be that of selecting k out of n given discrete probability distributions, to minimize (resp. maximize) the expectation of the minimum value resulting when independent random variables are drawn from the selected distributions. We assume each distribution has finitely many atoms. Let d be the number of distinct values in the support of the distributions. We show that if d is a constant greater than 2, the min-min expectation problem is NP-complete but admits a fully polynomial time approximation scheme. For d an arbitrary integer, it is NP-hard to approximate the min-min expectation problem within any constant approximation factor. The max-min expectation problem is polynomially solvable for constant d; we leave open its complexity for variable d. We also show similar results for binary selection problems in which we must choose one distribution from each of n pairs of distributions.