TY - GEN
T1 - Data-driven distributionally robust optimization over a network via distributed semi-infinite programming
AU - Cherukuri, Ashish
AU - Zolanvari, Alireza
AU - Banjac, Goran
AU - Hota, Ashish R.
N1 - Funding Information:
A. Cherukuri and A. Zolanvari are with the Engineering and Technology Institute Groningen, University of Groningen, The Netherlands ({a.k.cherukuri,a.zolanvari}@rug.nl), G. Banjac conducted his work while affiliated with the Automatic Control Laboratory, ETH Zürich, Switzerland ([email protected]), and A. R. Hota is with the Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, India ([email protected]). A. R. Hota was supported in part by IIT Kharagpur under the ISIRD funding scheme.
Publisher Copyright:
© 2022 IEEE.
PY - 2023/1/10
Y1 - 2023/1/10
N2 - This paper focuses on solving a data-driven distributionally robust optimization problem over a network of agents. The agents aim to minimize the worst-case expected cost computed over a Wasserstein ambiguity set that is centered at the empirical distribution. The samples of the uncertainty are distributed across the agents. Our approach consists of reformulating the problem as a semi-infinite program and then designing a distributed algorithm that solves a generic semi-infinite problem that has the same information structure as the reformulated problem. In particular, the decision variables consist of both local ones that agents are free to optimize over and global ones where they need to agree on. Our distributed algorithm is an iterative procedure that combines the notions of distributed ADMM and the cutting-surface method. We show that the iterates converge asymptotically to a solution of the distributionally robust problem to any pre-specified accuracy. Simulations illustrate our results.
AB - This paper focuses on solving a data-driven distributionally robust optimization problem over a network of agents. The agents aim to minimize the worst-case expected cost computed over a Wasserstein ambiguity set that is centered at the empirical distribution. The samples of the uncertainty are distributed across the agents. Our approach consists of reformulating the problem as a semi-infinite program and then designing a distributed algorithm that solves a generic semi-infinite problem that has the same information structure as the reformulated problem. In particular, the decision variables consist of both local ones that agents are free to optimize over and global ones where they need to agree on. Our distributed algorithm is an iterative procedure that combines the notions of distributed ADMM and the cutting-surface method. We show that the iterates converge asymptotically to a solution of the distributionally robust problem to any pre-specified accuracy. Simulations illustrate our results.
UR - http://www.scopus.com/inward/record.url?scp=85147000966&partnerID=8YFLogxK
U2 - 10.1109/CDC51059.2022.9992604
DO - 10.1109/CDC51059.2022.9992604
M3 - Conference contribution
AN - SCOPUS:85147000966
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 4771
EP - 4775
BT - 2022 IEEE 61st Conference on Decision and Control, CDC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 61st IEEE Conference on Decision and Control, CDC 2022
Y2 - 6 December 2022 through 9 December 2022
ER -