2025/08/09 by Alexandros E. Tzikas, Tzikas, Alexandros E., Lukas Fiechtner +5
Computer Science · Decision Sciences · Engineering · #Advanced Control Systems Optimization #FOS: Electrical engineering #Optimization and Variational Analysis #Risk and Portfolio Optimization #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2508.07121
openalex publication_date 2025/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Distributionally robust control is a well-studied framework for optimal decision making under uncertainty, with the objective of minimizing an expected cost function over control actions, assuming the most adverse probability distribution from an ambiguity set. We consider an interpretable and expressive class of ambiguity sets defined by constraints on the expected value of functions of one-dimensional linear projections of the uncertain parameters. Prior work has shown that, under conditions, problems in this class can be reformulated as finite convex problems. In this work, we propose two iterative methods that can be used to approximately solve problems of this class in the general case. The first is an approximate algorithm based on best-response dynamics. The second is an approximate method that first reformulates the problem as a semi-infinite program and then solves a relaxation. We apply our methods to portfolio construction and trajectory planning scenarios.