A Polynomial-Time Solution Scheme for Quadratic Stochastic Programs

Paula Rocha () and Daniel Kuhn ()
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Paula Rocha: Imperial College London
Daniel Kuhn: Imperial College London

Journal of Optimization Theory and Applications, 2013, vol. 158, issue 2, No 14, 576-589

Abstract: Abstract We consider quadratic stochastic programs with random recourse—a class of problems which is perceived to be computationally demanding. Instead of using mainstream scenario tree-based techniques, we reduce computational complexity by restricting the space of recourse decisions to those linear and quadratic in the observations, thereby obtaining an upper bound on the original problem. To estimate the loss of accuracy of this approach, we further derive a lower bound by dualizing the original problem and solving it in linear and quadratic recourse decisions. By employing robust optimization techniques, we show that both bounding problems may be approximated by tractable conic programs.

Keywords: Decision rule approximation; Robust optimization; Quadratic stochastic programming; Conic programming (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s10957-012-0264-6

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