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Statistical approximations forstochastic linear programming problems

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Abstract

Sampling and decomposition constitute two of the most successful approaches foraddressing large‐scale problems arising in statistics and optimization, respectively. In recentyears, these two approaches have been combined for the solution of large‐scale stochasticlinear programming problems. This paper presents the algorithmic motivation for suchmethods, as well as a broad overview of issues in algorithm design. We discuss both basicschemes as well as computational enhancements and stopping rules. We also introduce ageneralization of current algorithms to handle problems with random recourse.

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Authors
  1. Julia L. Higle
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  2. Suvrajeet Sen
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Higle, J.L., Sen, S. Statistical approximations forstochastic linear programming problems. Annals of Operations Research 85, 173–193 (1999). https://doi.org/10.1023/A:1018917710373

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