Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Mathematics of Operations Research
Robust Solutions to Uncertain Semidefinite Programs
SIAM Journal on Optimization
Operations Research
Adjustable robust solutions of uncertain linear programs
Mathematical Programming: Series A and B
Tractable Approximations to Robust Conic Optimization Problems
Mathematical Programming: Series A and B
Extending Scope of Robust Optimization: Comprehensive Robust Counterparts of Uncertain Problems
Mathematical Programming: Series A and B
Strong Formulations of Robust Mixed 0–1 Programming
Mathematical Programming: Series A and B
Retailer-Supplier Flexible Commitments Contracts: A Robust Optimization Approach
Manufacturing & Service Operations Management
Two-Stage Robust Network Flow and Design Under Demand Uncertainty
Operations Research
A Robust Optimization Perspective on Stochastic Programming
Operations Research
A Linear Decision-Based Approximation Approach to Stochastic Programming
Operations Research
Robust solutions of uncertain linear programs
Operations Research Letters
Distributionally Robust Optimization and Its Tractable Approximations
Operations Research
Robust Optimization Made Easy with ROME
Operations Research
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In this paper, we introduce the extended affinely adjustable robust counterpart to modeling and solving multistage uncertain linear programs with fixed recourse. Our approach first reparameterizes the primitive uncertainties and then applies the affinely adjustable robust counterpart proposed in the literature, in which recourse decisions are restricted to be linear in terms of the primitive uncertainties. We propose a special case of the extended affinely adjustable robust counterpart---the splitting-based extended affinely adjustable robust counterpart---and illustrate both theoretically and computationally that the potential of the affinely adjustable robust counterpart method is well beyond the one presented in the literature. Similar to the affinely adjustable robust counterpart, our approach ends up with deterministic optimization formulations that are tractable and scalable to multistage problems.