Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques

Volume 3624 of the series Lecture Notes in Computer Science pp 86-98

What About Wednesday? Approximation Algorithms for Multistage Stochastic Optimization

  • Anupam GuptaAffiliated withCarnegie Mellon UniversityDept. of Computer Science, Carnegie Mellon University
  • , Martin PálAffiliated withCarnegie Mellon UniversityDIMACS Center, Rutgers University
  • , Ramamoorthi RaviAffiliated withCarnegie Mellon UniversityTepper School of Business, Carnegie Mellon University
  • , Amitabh SinhaAffiliated withCarnegie Mellon UniversityRoss School of Business, University of Michigan

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We study the problem of multi-stage stochastic optimization with recourse, and provide approximation algorithms using cost-sharing functions for such problems. Our algorithms use and extend the Boosted Sampling framework of [6]. We also show how the framework can be adapted to give approximation algorithms even when the inflation parameters are correlated with the scenarios.