Submodular Maximization over Multiple Matroids via Generalized Exchange Properties

  • Authors:
  • Jon Lee;Maxim Sviridenko;Jan Vondrák

  • Affiliations:
  • IBM T. J. Watson Research Center, Yorktown Heights, New York 10598;IBM T. J. Watson Research Center, Yorktown Heights, New York 10598;IBM Almaden Research Center, San Jose, California 95120

  • Venue:
  • Mathematics of Operations Research
  • Year:
  • 2010

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Abstract

Submodular function maximization is a central problem in combinatorial optimization, generalizing many important NP-hard problems including max cut in digraphs, graphs, and hypergraphs; certain constraint satisfaction problems; maximum entropy sampling; and maximum facility location problems. Our main result is that for any k ≥ 2 and any ε 0, there is a natural local search algorithm that has approximation guarantee of 1/(k + ε) for the problem of maximizing a monotone submodular function subject to k matroid constraints. This improves upon the 1/(k + 1)-approximation of Fisher, Nemhauser, and Wolsey obtained in 1978 [Fisher, M., G. Nemhauser, L. Wolsey. 1978. An analysis of approximations for maximizing submodular set functions---II. Math. Programming Stud.8 73--87]. Also, our analysis can be applied to the problem of maximizing a linear objective function and even a general nonmonotone submodular function subject to k matroid constraints. We show that, in these cases, the approximation guarantees of our algorithms are 1/(k-1 + ε) and 1/(k + 1 + 1/(k-1) + ε), respectively. Our analyses are based on two new exchange properties for matroids. One is a generalization of the classical Rota exchange property for matroid bases, and another is an exchange property for two matroids based on the structure of matroid intersection.