Approximation Algorithms for 2-Stage Stochastic Optimization Problems

  • Chaitanya Swamy
  • David B. Shmoys
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4337)


Stochastic optimization is a leading approach to model optimization problems in which there is uncertainty in the input data, whether from measurement noise or an inability to know the future. In this survey, we outline some recent progress in the design of polynomial-time algorithms with performance guarantees on the quality of the solutions found for an important class of stochastic programming problems — 2-stage problems with recourse. In particular, we show that for a number of concrete problems, algorithmic approaches that have been applied for their deterministic analogues are also effective in this more challenging domain. More specifically, this work highlights the role of tools from linear programming, rounding techniques, primal-dual algorithms, and the role of randomization more generally.


Approximation Algorithm Facility Location Stochastic Programming Steiner Tree Stochastic Optimization 
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 2006

Authors and Affiliations

  • Chaitanya Swamy
    • 1
  • David B. Shmoys
    • 2
  1. 1.Department of Combinatorics & OptimizationUniversity of WaterlooWaterloo
  2. 2.School of ORIE and Department of Computer ScienceCornell UniversityIthaca

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