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Constrained Submodular Maximization: Beyond 1/e

2016/08/11 by Ene, Alina, Nguyen, Huy L.
#Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.1608.03611

Abstract

In this work, we present a new algorithm for maximizing a non-monotone submodular function subject to a general constraint. Our algorithm finds an approximate fractional solution for maximizing the multilinear extension of the function over a down-closed polytope. The approximation guarantee is 0.372 and it is the first improvement over the 1/e approximation achieved by the unified Continuous Greedy algorithm [Feldman et al., FOCS 2011].

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