14 papers
A Generalized Plant Perspective on Linear-Convex Feedback Optimization
Fabian Jakob, Andrea Iannelli
Feedback optimization is a control approach for driving a dynamical system to the solution of an optimization problem by interconnecting the plant with an algorithm. Existing stabi…
Sample-Efficient Model-Free Policy Gradient Methods for Stochastic LQR via Robust Linear Regression
Bowen Song, Sebastien Gros, Andrea Iannelli
Policy gradient algorithms are widely used in reinforcement learning and belong to the class of approximate dynamic programming methods. This paper studies two key policy gradient…
Convergence Guarantees of Model-free Policy Gradient Methods for LQR with Stochastic Data
Bowen Song, Andrea Iannelli
Policy gradient (PG) methods are the backbone of many reinforcement learning algorithms due to their good performance in policy optimization problems. As a gradient-based approach,…
Structure, Analysis, and Synthesis of First-Order Algorithms
Jared Miller, Carsten Scherer, Fabian Jakob +1
Optimization algorithms can be interpreted through the lens of dynamical systems as the interconnection of linear systems and a set of subgradient nonlinearities. This dynamical sy…
Analysis and Synthesis of Switched Optimization Algorithms
Jared Miller, Fabian Jakob, Carsten Scherer +1
Deployment of optimization algorithms over communication networks face challenges associated with time delays and corruptions. Fixed time delays can destabilize popular gradient-ba…
A Linear Parameter-Varying Framework for the Analysis of Time-Varying Optimization Algorithms
Fabian Jakob, Andrea Iannelli
In this paper we propose a framework to analyze iterative first-order optimization algorithms for time-varying convex optimization. We assume that the temporal variability is cause…