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Statistical approximations for stochastic linear programming problems
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DOI：10.1023/A:1018917710373.png)
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摘要
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Sampling and decomposition constitute two of the most successful approaches for addressing large-scale problems arising in statistics and optimization, respectively. In recent years, these two approaches have been combined for the solution of large-scale stochastic linear programming problems. This paper presents the algorithmic motivation for such methods, as well as a broad overview of issues in algorithm design. We discuss both basic schemes as well as computational enhancements and stopping rules. We also introduce a generalization of current algorithms to handle problems with random recourse.
Keyword:
cutting plane algorithms
stochastic programming
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