The risk-averse (and prudent) newsboy
Management Science
Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Mathematics of Operations Research
Robust Truss Topology Design via Semidefinite Programming
SIAM Journal on Optimization
Robust Solutions to Uncertain Semidefinite Programs
SIAM Journal on Optimization
Robust portfolio selection problems
Mathematics of Operations Research
Ambiguous Risk Measures and Optimal Robust Portfolios
SIAM Journal on Optimization
Parametric estimation and tests through divergences and the duality technique
Journal of Multivariate Analysis
Regret in the Newsvendor Model with Partial Information
Operations Research
Technical Note---A Risk-Averse Newsvendor Model Under the CVaR Criterion
Operations Research
Constructing Uncertainty Sets for Robust Linear Optimization
Operations Research
A Soft Robust Model for Optimization Under Ambiguity
Operations Research
Fully Distribution-Free Profit Maximization: The Inventory Management Case
Mathematics of Operations Research
Information, Divergence and Risk for Binary Experiments
The Journal of Machine Learning Research
Theory and Applications of Robust Optimization
SIAM Review
On Divergences and Informations in Statistics and Information Theory
IEEE Transactions on Information Theory
Hi-index | 0.01 |
In this paper we focus on robust linear optimization problems with uncertainty regions defined by φ-divergences for example, chi-squared, Hellinger, Kullback--Leibler. We show how uncertainty regions based on φ-divergences arise in a natural way as confidence sets if the uncertain parameters contain elements of a probability vector. Such problems frequently occur in, for example, optimization problems in inventory control or finance that involve terms containing moments of random variables, expected utility, etc. We show that the robust counterpart of a linear optimization problem with φ-divergence uncertainty is tractable for most of the choices of φ typically considered in the literature. We extend the results to problems that are nonlinear in the optimization variables. Several applications, including an asset pricing example and a numerical multi-item newsvendor example, illustrate the relevance of the proposed approach. This paper was accepted by Gérard P. Cachon, optimization.