Internet and Network Economics

Volume 6484 of the series Lecture Notes in Computer Science pp 246-257

Constrained Non-monotone Submodular Maximization: Offline and Secretary Algorithms

  • Anupam GuptaAffiliated withCarnegie Mellon University
  • , Aaron RothAffiliated withMicrosoft Research New England
  • , Grant SchoenebeckAffiliated withPrinceton University
  • , Kunal TalwarAffiliated withMicrosoft Research

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Constrained submodular maximization problems have long been studied, most recently in the context of auctions and computational advertising, with near-optimal results known under a variety of constraints when the submodular function is monotone. In this paper, we give constant approximation algorithms for the non-monotone case that work for p-independence systems (which generalize constraints given by the intersection of p matroids that had been studied previously), where the running time is \(\text{poly}(n,p)\). Our algorithms and analyses are simple, and essentially reduce non-monotone maximization to multiple runs of the greedy algorithm previously used in the monotone case.

We extend these ideas to give a simple greedy-based constant factor algorithms for non-monotone submodular maximization subject to a knapsack constraint, and for (online) secretary setting (where elements arrive one at a time in random order and the algorithm must make irrevocable decisions) subject to uniform matroid or a partition matroid constraint. Finally, we give an O(logk) approximation in the secretary setting subject to a general matroid constraint of rank k.