Titlebook: Computational Stochastic Programming; Models, Algorithms, Lewis Ntaimo Book 2024 Springer Nature Switzerland AG 2024 Mean-risk linear and

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Introductiontochastic setting of SP. We assume that the reader is familiar with LP and skip all the fundamental concepts of LP such as duality theory and sensitivity analysis. Understanding these LP concepts is important to studying SP. We first introduce scenario trees for representing the underlying stochasti

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Mean-Risk Stochastic Linear Programming Methodspter, we turn to the derivation of two subgradient-based algorithms for the deviation risk measure . (ASD), termed . and . algorithms. Unlike MR-SLP with QDEV, CVaR, and EE, the DEP for ASD has a block angular structure due to a set of linking constraints. Therefore, the L-shaped method is . applica