Stability of Stochastic Programming Problems

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Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning (2019, background): “Classical stability results in stochastic programming are surveyed in [31, 88].”
Generation of multivariate scenario trees to model stochasticity in power management (2005, background): “The recent paper [ 28 ] surveys quantitative stability results for stochastic programs.”
Statistical estimation of composite risk functionals and risk optimization problems (2015, background): “Römisch 2003, ch. 8 contains results on perturbation analysis for optimization problems with risk functionals.”
Scenario tree modeling for multistage stochastic programs (2009, background): “Their inputs are multivariate stochastic processes {ξt}Tt=1 defined on some probability space (Ω,F , IP ) and with ξt taking values in some IRd.”
Lectures on Stochastic Programming: Modeling and Theory (2009, methodology): “Stability of models with probabilistic constraints is addressed in Dentcheva [54], Henrion [100, 99], and Henrion and Römisch [219, 103].”
Discrepancy distances and scenario reduction in two-stage stochastic mixed-integer programming (2008, background): “As argued in [23], (semi-)metrics with ζ-structure (cf.”
Distributionally Robust Optimization: A Review (2019, background): “g, Rachev [250], Römisch [263], or (3) input uncertainty analysis in stochastic simulation models, see, e.”