7 papers · 1 filter
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…
Stability, Contraction, and Controllers for Affine Systems
L. P. Wieringa, A. Padoan, F. Dorfler +1
Recent developments in data-driven control have revived interest in the behavioral approach to systems theory, where systems are defined as sets of trajectories rather than being d…
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…
Adaptive Control of Unknown Linear Switched Systems via Policy Gradient Methods
Felix Laurent, Feiran Zhao, Jaap Eising +1
We consider the policy gradient adaptive control (PGAC) framework, which adaptively updates a control policy in real time, by performing data-based gradient descent steps on the li…
From Time Series to Affine Systems
A. Padoan, J. Eising, I. Markovsky
The paper extends core results of behavioral systems theory from linear to affine time-invariant systems. We characterize the behavior of affine time-invariant systems via kernel,…
Set-valued regression and cautious suboptimization: From noisy data to optimality
Jaap Eising, Jorge Cortes
This paper deals with the problem of finding suboptimal values of an unknown function on the basis of measured data corrupted by bounded noise. As a prior, we assume that the unkno…