7 papers
Adaptive Linear Quadratic Control of Unknown Linear Time-Varying Systems via Policy Gradient Methods
Feiran Zhao, Florian Dörfler
Unknown linear time-varying (LTV) systems require the control policy to adapt from online closed-loop data as dynamics evolve. Existing methods usually update the policy by solving…
A Data-Enabled Primal-Dual Approach for Policy Learning with SDP Formulations
Han Wang, Feiran Zhao, Florian Dorfler
This paper develops a data-enabled primal-dual framework for learning optimal control policies for unknown linear discrete-time systems from online data. The proposed approach view…
Convergence Analysis of Distributed Optimization: A Dissipativity Framework
Aron Karakai, Jaap Eising, Andrea Martinelli +1
We develop a system-theoretic framework for the structured analysis of distributed optimization algorithms with decomposable cost functions. We model such algorithms as a network o…
On analysis of open optimization algorithms
Jaap Eising, Florian Dörfler
We consider optimization algorithms that are open systems, that is, with external inputs and outputs. Such algorithms arise for instance, when analyzing the effect of noise or dist…
Sparse optimal control in the Wasserstein space
Enrico Sartor, Florian Dörfler, Nicolas Lanzetti
We study sparse optimal control of a non-local continuity equation, where the goal is to steer a distribution via finitely many controllable agents or actuators. This model arises…
Visibility Allocation Systems: How Algorithmic Design Shapes Online Visibility and Societal Outcomes
Stefania Ionescu, Robin Forsberg, Elsa Lichtenegger +4
Throughout application domains, we now rely extensively on algorithmic systems to engage with ever-expanding datasets of information. Despite their benefits, these systems are ofte…