Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes

Journal Article · Mon Jul 01 00:00:00 EDT 2019 · Operations Research Letters

DOI:https://doi.org/10.1016/j.orl.2019.04.005· OSTI ID:1570433

Dou, Xialiang; Anitescu, Mihai

We present a distributionally robust formulation of a stochastic optimization problem for non-i.i.d vector autoregressive data. We use the Wasserstein distance to define robustness in the space of distributions and we show, using duality theory, that the problem is equivalent to a finite convex-concave saddle point problem. The performance of the method is demonstrated on both synthetic and real data. (C) 2019 Elsevier B.V. All rights reserved.

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE Office of Science - Office of Advanced Scientific Computing Research
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1570433
• Journal Information: Operations Research Letters, Vol. 47, Issue 4
• Country of Publication: United States
• Language: English

Related Subjects

Distributionally robust optimization
Saddle point problem
Wasserstein distance