Multistage stochastic convex programs: Duality and its implications

Julia Higle () and Suvrajeet Sen

Annals of Operations Research, 2006, vol. 142, issue 1, 129-146

Abstract: In this paper, we study alternative primal and dual formulations of multistage stochastic convex programs (SP). The alternative dual problems which can be traced to the alternative primal representations, lead to stochastic analogs of standard deterministic constructs such as conjugate functions and Lagrangians. One of the by-products of this approach is that the development does not depend on dynamic programming (DP) type recursive arguments, and is therefore applicable to problems in which the objective function is non-separable (in the DP sense). Moreover, the treatment allows us to handle both continuous and discrete random variables with equal ease. We also investigate properties of the expected value of perfect information (EVPI) within the context of SP, and the connection between EVPI and nonanticipativity of optimal multipliers. Our study reveals that there exist optimal multipliers that are nonanticipative if, and only if, the EVPI is zero. Finally, we provide interpretations of the retroactive nature of the dual multipliers. Copyright Springer Science + Business Media, Inc. 2006

Keywords: Stochastic Programming; Duality; EVPI (search for similar items in EconPapers)
Date: 2006
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DOI: 10.1007/s10479-006-6165-z

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