Barycentric Bounds in Stochastic Programming: Theory and Application

Karl Frauendorfer (), Daniel Kuhn () and Michael Schürle ()
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Karl Frauendorfer: University of St. Gallen
Daniel Kuhn: Imperial College of Science, Technology and Medicine
Michael Schürle: University of St. Gallen

Chapter Chapter 5 in Stochastic Programming, 2010, pp 67-96 from Springer

Abstract: Abstract The design and analysis of efficient approximation schemes are of fundamental importance in stochastic programming research. Bounding approximations are particularly popular for providing strict error bounds that can be made small by using partitioning techniques. In this chapter we develop a powerful bounding method for linear multistage stochastic programs with a generalized nonconvex dependence on the random parameters. Thereby, we establish bounds on the recourse functions as well as compact bounding sets for the optimal decisions. We further demonstrate that our bounding methods facilitate the reliable solution of important real-life decision problems. To this end, we solve a stochastic optimization model for the management of nonmaturing accounts and compare the bounds on maximum profit obtained with different partitioning strategies.

Keywords: Correction Term; Stochastic Program; Random Parameter; Liquidity Risk; Linear Stochastic Program (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4419-1642-6_5

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DOI: 10.1007/978-1-4419-1642-6_5

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