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1. In this paper, we propose a model that describes uncertainty in both the distribution form discrete, gaussian, exponential, etc.
2. In particular, we study the associated multistage distributionally robust optimization problem, when only the mean, variance, and di.
3. Dr this paper surveys the primary research, both theoretical and applied, in the area of robust optimization ro, focusing on the computational attractiveness of ro approaches, as well as the modeling power and broad applicability of the methodology.
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5. Subject classifications programming stochastic.
6. we demonstrate that for a wide range of cost functions the associated distributionally robust or minmax stochastic program can be solved efficiently.
7. 这篇文章讲的是 momentbased dro.
8. In this paper, we propose a model that describes uncertainty in both the distribution form discrete, gaussian, exponential, etc.
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10. In this paper, we propose a model that describes uncertainty in both the distribution form discrete, gaussian, exponential, etc.
11. We demonstrate that for a wide range of cost functions the associated distributionally robust stochastic program can be solved efficiently.
12. in this paper, we consider a minimax approach to managing an inventory under distributional uncertainty.
13. Dr this paper surveys the primary research, both theoretical and applied, in the area of robust optimization ro, focusing on the computational attractiveness of ro approaches, as well as the modeling power and broad applicability of the methodology.
14. Distributionally robust optimization under moment uncertainty with application to datadriven problems 发表在 operations research, 2010.
15. Subject classifications programming stochastic.
16. Distributionally robust optimization under moment uncertainty with application to datadriven problems.
17. Distributionally robust optimization under moment uncertainty with application to datadriven problems.
18. Statistics estimation.
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20. We demonstrate that for a wide range of cost functions the associated distributionally robust or minmax stochastic program can be solved efficiently.
21. Statistics estimation.
22. We demonstrate that for a wide range of cost functions the associated distributionally robust or minmax stochastic program can be solved efficiently.
23. In particular, we study the associated multistage distributionally robust optimization problem, when only the mean, variance, and di.
24. Dr this paper surveys the primary research, both theoretical and applied, in the area of robust optimization ro, focusing on the computational attractiveness of ro approaches, as well as the modeling power and broad applicability of the methodology.
25. Distributionally robust optimization under moment uncertainty with application to datadriven problems 发表在 operations research, 2010.
26. Distributionally robust optimization under moment uncertainty with application to datadriven problems.
27. Statistics estimation.
28. in this paper, we consider a minimax approach to managing an inventory under distributional uncertainty.
29. 这篇文章讲的是 momentbased dro.
30. 这篇文章讲的是 momentbased dro.
31. We demonstrate that for a wide range of cost functions the associated distributionally robust or minmax stochastic program can be solved efficiently.
32. 这篇文章讲的是 momentbased dro.
33. In this paper, we propose a model that describes uncertainty in both the distribution form discrete, gaussian, exponential, etc.
34. Subject classifications programming stochastic.
35. Furthermore, by deriving new confidence regions for the mean and covariance of a random vector, we provide probabilistic arguments for using our model in problems that rely heavily on historical data.
36. In this paper, we propose a model that describes uncertainty in both the distribution form discrete, gaussian, exponential, etc.
37. In particular, we study the associated multistage distributionally robust optimization problem, when only the mean, variance, and di.
38. 这篇文章讲的是 momentbased dro.
39. And moments mean and covariance matrix.
40. Statistics estimation.
41. Dr this paper surveys the primary research, both theoretical and applied, in the area of robust optimization ro, focusing on the computational attractiveness of ro approaches, as well as the modeling power and broad applicability of the methodology.
42. In this paper, we propose a model that describes uncertainty in both the distribution form discrete, gaussian, exponential, etc.
43. And moments mean and covariance matrix.
44. Subject classifications programming stochastic.
45. Dr this paper surveys the primary research, both theoretical and applied, in the area of robust optimization ro, focusing on the computational attractiveness of ro approaches, as well as the modeling power and broad applicability of the methodology.
46. And moments mean and covariance matrix.
47. We demonstrate that for a wide range of cost functions the associated distributionally robust stochastic program can be solved efficiently.
48. in this paper, we consider a minimax approach to managing an inventory under distributional uncertainty.
49. in this paper, we consider a minimax approach to managing an inventory under distributional uncertainty.