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Stochastic Submodular Maximization

  • Arash Asadpour
  • Hamid Nazerzadeh
  • Amin Saberi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5385)

Abstract

We study stochastic submodular maximization problem with respect to a cardinality constraint. Our model can capture the effect of uncertainty in different problems, such as cascade effects in social networks, capital budgeting, sensor placement, etc. We study non-adaptive and adaptive policies and give optimal constant approximation algorithms for both cases. We also bound the adaptivity gap of the problem between 1.21 and 1.59.

Keywords

Approximation Ratio Cardinality Constraint Submodular Function Greedy Policy Adaptive Policy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Arash Asadpour
    • 1
  • Hamid Nazerzadeh
    • 1
  • Amin Saberi
    • 1
  1. 1.Stanford UniversityStanfordUSA

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