Vahab S. Mirrokni

Maximizing Non-monotone Submodular Functions

Submodular maximization generalizes many important problems including Max Cut in directed and undirected graphs and hypergraphs, certain constraint satisfaction problems, and maximum facility location problems. Unlike the problem of minimizing submodular functions, the problem of maximizing submodular functions is NP-hard. In this paper, we design the first constant-factor approximation algorithms for maximizing nonnegative (non-monotone) submodular functions. In particular, we give a deterministic local-search

Trust-based recommendation systems: an axiomatic approach

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Tight approximation algorithms for maximum general assignment problems

-approximation and a randomized

Sink equilibria and convergence

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Fault-tolerant and 3-dimensional distributed topology control algorithms in wireless multi-hop networks

-approximation algorithm for maximizing nonnegative submodular functions. We also show that a uniformly random set gives a

Maximizing Non-Monotone Submodular Functions

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Online Stochastic Matching: Beating 1-1/e

-approximation. For symmetric submodular functions, we show that a random set gives a

Market sharing games applied to content distribution in ad hoc networks

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Non-monotone submodular maximization under matroid and knapsack constraints

-approximation, which can also be achieved by deterministic local search. These algorithms work in the value oracle model, where the submodular function is accessible through a black box returning

Power optimization in fault-tolerant topology control algorithms for wireless multi-hop networks

f(S)