Computational Optimization and Applications

, Volume 51, Issue 2, pp 509–532 | Cite as

Adaptive and nonadaptive approaches to statistically based methods for solving stochastic linear programs: a computational investigation

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Abstract

Large scale stochastic linear programs are typically solved using a combination of mathematical programming techniques and sample-based approximations. Some methods are designed to permit sample sizes to adapt to information obtained during the solution process, while others are not. In this paper, we experimentally examine the relative merits and challenges of approximations based on adaptive samples and those based on non-adaptive samples. We focus our attention on Stochastic Decomposition (SD) as an adaptive technique and Sample Average Approximation (SAA) as a non-adaptive technique. Our results indicate that there can be minimal difference in the quality of the solutions provided by these methods, although comparing their computational requirements would be more challenging.

Keywords

Stochastic programming Sample based optimization Computational experimentation 
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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.ISE DepartmentThe Ohio State UniversityColumbusUSA
  2. 2.IE DepartmentTsinghua UniversityBeijingChina

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