Y Li, G Qu, N Li - IEEE Transactions on Automatic Control, 2020 - ieeexplore.ieee.org
This article considers online optimization with a finite prediction window of cost functions and additional switching costs on the decisions. We study the fundamental limits of dynamic …
Y Li, S Das, N Li - Proceedings of the AAAI Conference on Artificial …, 2021 - ojs.aaai.org
This paper considers online optimal control with affine constraints on the states and actions under linear dynamics with bounded random disturbances. The system dynamics and …
In this paper, we address tracking of a time-varying parameter with unknown dynamics. We formalize the problem as an instance of online optimization in a dynamic setting. Using …
We consider the problem of online control of systems with time-varying linear dynamics. To state meaningful guarantees over changing environments, we introduce the metric of {\it …
X Cao, KJR Liu - IEEE Transactions on automatic control, 2018 - ieeexplore.ieee.org
In this paper, online convex optimization problem with time-varying constraints is studied from the perspective of an agent taking sequential actions. Both the objective function and …
Making use of predictions is a crucial, but under-explored, area of online algorithms. This paper studies a class of online optimization problems where we have external noisy …
We present a unified, black-box-style method for developing and analyzing online convex optimization (OCO) algorithms for full-information online learning in delayed-feedback …
We study optimal regret bounds for control in linear dynamical systems under adversarially changing strongly convex cost functions, given the knowledge of transition dynamics. This …
This paper presents competitive algorithms for a novel class of online optimization problems with memory. We consider a setting where the learner seeks to minimize the sum of a hitting …