Name
Abstract | ||
|---|---|---|
We study a class of multiagent stochastic optimization problems where the objective is to minimize the expected value of a function which depends on a random variable. The probability distribution of the random variable is unknown to the agents. The agents aim to cooperatively find, using their collected data, a solution with guaranteed out-of-sample performance. The approach is to formulate a data-driven distributionally robust optimization problem using Wasserstein ambiguity sets, which turns out to be equivalent to a convex program. We reformulate the latter as a distributed optimization problem and identify a convex–concave augmented Lagrangian, whose saddle points are in correspondence with the optimizers, provided a min–max interchangeability criteria is met. Our distributed algorithm design, then consists of the saddle-point dynamics associated to the augmented Lagrangian. We formally establish that the trajectories converge asymptotically to a saddle point and, hence, an optimizer of the problem. Finally, we identify classes of functions that meet the min–max interchangeability criteria. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TAC.2019.2955031 | IEEE Transactions on Automatic Control |
Keywords | DocType | Volume |
Optimization,Random variables,Probability distribution,Measurement,Distributed algorithms,Linear programming,Convex functions | Journal | 65 |
Issue | ISSN | Citations |
10 | 0018-9286 | 0 |
PageRank | References | Authors |
0.34 | 16 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashish Cherukuri | 1 | 0 | 0.34 |
| Jorge Cortes | 2 | 1452 | 128.75 |