Submodular function maximization via the multilinear relaxation and contention resolution schemes

Chandra Chekuri, Jan Vondrák, Rico Zenklusen

Research output: Contribution to journalArticle

Abstract

We consider the problem of maximizing a nonnegative submodular set function f : 2N → ℝ+ over a ground set N subject to a variety of packing-type constraints including (multiple) matroid constraints, knapsack constraints, and their intersections. In this paper we develop a general framework that allows us to derive a number of new results, in particular, when f may be a nonmonotone function. Our algorithms are based on (approximately) maximizing the multilinear extension F of f over a polytope P that represents the constraints, and then effectively rounding the fractional solution. Although this approach has been used quite successfully, it has been limited in some important ways. We overcome these limitations as follows. First, we give constant factor approximation algorithms to maximize F over a downward-closed polytope P described by an efficient separation oracle. Previously this was known only for monotone functions. For nonmonotone functions, a constant factor was known only when the polytope was either the intersection of a fixed number of knapsack constraints or a matroid polytope. Second, we show that contention resolution schemes are an effective way to round a fractional solution, even when f is nonmonotone. In particular, contention resolution schemes for different polytopes can be combined to handle the intersection of different constraints. Via linear programming duality we show that a contention resolution scheme for a constraint is related to the correlation gap of weighted rank functions of the constraint. This leads to an optimal contention resolution scheme for the matroid polytope. Our results provide a broadly applicable framework for maximizing linear and submodular functions subject to independence constraints. We give several illustrative examples. Contention resolution schemes may find other applications.

Original languageEnglish (US)
Pages (from-to)1831-1879
Number of pages49
JournalSIAM Journal on Computing
Volume43
Issue number6
DOIs
StatePublished - Jan 1 2014

Keywords

  • Approximation algorithms
  • Contention resolution
  • Randomized algorithms
  • Submodular function maximization

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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