Rate-Optimal Online Convex Optimization in Adaptive Linear Control

• URL: http://arxiv.org/abs/2206.01426v1
• Date: Fri, 3 Jun 2022 07:32:11 GMT
• Title: Rate-Optimal Online Convex Optimization in Adaptive Linear Control
• Authors: Asaf Cassel (1), Alon Cohen (2 and 3), Tomer Koren (1 and 3) ((1) School of Computer Science, Tel Aviv University, (2) School of Electrical Engineering, Tel Aviv University, (3) Google Research, Tel Aviv)
• Abstract summary: We consider the problem of controlling an unknown convex linear system under adversarially changing costs. We present the first computationally-gret that attains an optimal linear hindsight function.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of controlling an unknown linear dynamical system under adversarially changing convex costs and full feedback of both the state and cost function. We present the first computationally-efficient algorithm that attains an optimal $\smash{\sqrt{T}}$-regret rate compared to the best stabilizing linear controller in hindsight, while avoiding stringent assumptions on the costs such as strong convexity. Our approach is based on a careful design of non-convex lower confidence bounds for the online costs, and uses a novel technique for computationally-efficient regret minimization of these bounds that leverages their particular non-convex structure.

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