Name
Abstract | ||
|---|---|---|
We formulate a distributionally robust optimization problem where the deviation of the alternative distribution is controlled by a ϕ-divergence penalty in the objective, and show that a large class of these problems are essentially equivalent to a mean–variance problem. We also show that while a “small amount of robustness” always reduces the in-sample expected reward, the reduction in the variance, which is a measure of sensitivity to model misspecification, is an order of magnitude larger. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.orl.2018.05.005 | Operations Research Letters |
Keywords | Field | DocType |
Robust empirical optimization,Mean–variance optimization,Data-driven optimization,
ϕ-divergence,Regularization,Bias–variance trade-off | Probabilistic-based design optimization,Mathematical optimization,Divergence,Robust optimization,Robustness (computer science),Order of magnitude,Mathematics,Kullback–Leibler divergence | Journal |
Volume | Issue | ISSN |
46 | 4 | 0167-6377 |
Citations | PageRank | References |
2 | 0.36 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun-Ya Gotoh | 1 | 117 | 10.17 |
| Michael Jong Kim | 2 | 39 | 5.03 |
| Andrew E. B. Lim | 3 | 298 | 41.99 |