Data-driven distributionally robust optimization over a network via distributed semi-infinite programming

TL;DR

This paper develops a distributed algorithm combining ADMM and cutting-surface methods to solve a data-driven distributionally robust optimization problem over a network, ensuring convergence to an optimal solution.

Contribution

It introduces a novel distributed semi-infinite programming approach for distributionally robust optimization with Wasserstein ambiguity sets, enabling agents to collaboratively solve the problem.

Findings

The algorithm converges asymptotically to the optimal solution.

Simulations demonstrate the effectiveness of the proposed method.

The approach handles distributed data and decision variables efficiently.

Abstract

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.