contextual stochastic optimization

Model-Uncertainty-Aware Residuals-Based Sample Average Approximation

Published: 2026/07/13

We consider a contextual stochastic optimization (CSO) problem, where one has observations of the uncertain parameters together with concurrent observations of covariates, and the goal is to choose decisions that minimize expected cost conditioned on new covariate observations. The empirical residuals-based sample average approximation (ER-SAA) of the CSO problem constructs scenarios of uncertainty by combining … Read more

Data-Driven Contextual Optimization with Gaussian Mixtures: Flow-Based Generalization, Robust Models, and Multistage Extensions

Published: 2025/08/16, Updated: 2025/09/16

Robust Optimization, Stochastic Programming contextual stochastic optimization, data-driven dynamic programming, distributionally robust optimization, gaussian mixture models, multistage stochastic programming, normalizing flows

Contextual optimization enhances decision quality by leveraging side information to improve predictions of uncertain parameters. However, existing approaches face significant challenges when dealing with multimodal or mixtures of distributions. The inherent complexity of such structures often precludes an explicit functional relationship between the contextual information and the uncertain parameters, limiting the direct applicability of parametric … Read more