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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.

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Authors and Affiliations

  1. ISE Department, The Ohio State University, Columbus, OH, USA

    Julia L. Higle

  2. IE Department, Tsinghua University, Beijing, China

    Lei Zhao

Authors
  1. Julia L. Higle
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  2. Lei Zhao
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Correspondence to Julia L. Higle.

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Higle, J.L., Zhao, L. Adaptive and nonadaptive approaches to statistically based methods for solving stochastic linear programs: a computational investigation. Comput Optim Appl 51, 509–532 (2012). https://doi.org/10.1007/s10589-010-9366-y

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  • DOI: https://doi.org/10.1007/s10589-010-9366-y

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