Stochastic dominance and expected utility: survey and analysis
Management Science
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Robust Solutions to Uncertain Semidefinite Programs
SIAM Journal on Optimization
Robust portfolio selection problems
Mathematics of Operations Research
Operations Research
On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
Mathematics of Operations Research
Uncertain convex programs: randomized solutions and confidence levels
Mathematical Programming: Series A and B
A Conic Programming Approach to Generalized Tchebycheff Inequalities
Mathematics of Operations Research
Ambiguous chance constrained problems and robust optimization
Mathematical Programming: Series A and B
Persistence in discrete optimization under data uncertainty
Mathematical Programming: Series A and B
Optimization of Convex Risk Functions
Mathematics of Operations Research
Convex Approximations of Chance Constrained Programs
SIAM Journal on Optimization
A Robust Optimization Perspective on Stochastic Programming
Operations Research
Constructing Uncertainty Sets for Robust Linear Optimization
Operations Research
Robust solutions of uncertain linear programs
Operations Research Letters
Robust linear optimization under general norms
Operations Research Letters
Constructing Uncertainty Sets for Robust Linear Optimization
Operations Research
A Soft Robust Model for Optimization Under Ambiguity
Operations Research
Theory and Applications of Robust Optimization
SIAM Review
Computers and Operations Research
Robust Optimization in Simulation: Taguchi and Krige Combined
INFORMS Journal on Computing
Worst-Case Value at Risk of Nonlinear Portfolios
Management Science
Inverse Optimization: A New Perspective on the Black-Litterman Model
Operations Research
Robust investment decisions under supply disruption in petroleum markets
Computers and Operations Research
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We illustrate the correspondence between uncertainty sets in robust optimization and some popular risk measures in finance and show how robust optimization can be used to generalize the concepts of these risk measures. We also show that by using properly defined uncertainty sets in robust optimization models, one can construct coherent risk measures and address the issue of the computational tractability of the resulting formulations. Our results have implications for efficient portfolio optimization under different measures of risk.