Titlebook: Stochastic Programming; The State of the Art Gerd Infanger Book 2011 Springer Science+Business Media, LLC 2011

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Stochastic Programming Approximations Using Limited Moment Information, with Application to Asset Ad moment information of the underlying random vectors to approximate the expected recourse function. Bounds that use first-order moments, as well as higher-order moments are described in the context of saddle functions and specialized for convex recourse functions. Relation of these bounds to genera

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Risk Aversion in Two-Stage Stochastic Integer Programming,sulting mean–risk stochastic integer programs emphasis is placed on structural properties of these optimization problems and on algorithms for their solution. Bibliographical notes conclude the Chapter.

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Portfolio Optimization with Risk Control by Stochastic Dominance Constraints,pproach based on stochastic dominance. The portfolio return rate in this model is required to stochastically dominate a random benchmark, such as an index, or reference portfolio return. We formulate optimality conditions and duality relations for these models and construct equivalent optimization m

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,Mean–Absolute Deviation Model,large–scale portfolio optimization problems. MAD model has been used for solving huge portfolio optimization models including internationally diversified investment model, long-term ALM model, mortgage–backed security portfolio optimization model. Also, the MAD model enjoys several nice theoretical

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,Growth–Security Models and Stochastic Dominance,nother, then the ordering of random variables is required. In this paper the levels of stochastic dominance for random variables are used to define bi-criteria problems for determining an efficient investment strategy. The criteria are characterized as growth and security, respectively, and produce