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Exact Penalization, Level Function Method and Modified Cutting-Plane Method for Stochastic Programs with Second Order Stochastic Dominance Constraints

Hailin Sun (mathhlsun***at***gmail.com)
Huifu Xu (h.xu***at***soton.ac.uk)
Rudabeh Meskarian (rm3g08***at***soton.ac.uk)
Yong Wang (mathwy***at***hit.edu.cn)

Abstract: Level function methods and cutting plane methods have been recently proposed to solve stochastic programs with stochastic second order dominance (SSD) constraints. A level function method requires an exact penalization setup because it can only be applied to the objective function, not the constraints. Slater constraint qualification (SCQ) is often needed for deriving exact penalization. In this paper, we show that although the original problem usually does satisfy the SCQ but, through some reformulation of the constraints, the constraint qualification can be satisfied under some moderate conditions. Exact penalization schemes based on L1-norm and L_\infty-norm are subsequently derived through Robinson's error bound on convex system and Clarke's exact penalty function theorem. Moreover, we propose a modified cutting plane method which constructs a cutting plane through the maximum of the reformulated constraint functions. In comparison with the existing cutting methods, it is numerically more efficient because only a single cutting plane is constructed and added at each iteration. We have carried out a number of numerical experiments and the results show that our methods display better performances particularly in the case when the underlying functions are nonlinear w.r.t. decision variables.

Keywords: Slater constraint qualification, exact penalization, modified cutting-plane method, level function method

Category 1: Stochastic Programming

Category 2: Applications -- OR and Management Sciences (Finance and Economics )

Citation: School of Mathematics, University of Southampton, February, 2012.

Download: [PDF]

Entry Submitted: 02/15/2012
Entry Accepted: 02/17/2012
Entry Last Modified: 02/19/2012

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