1 citations · 1 across the 15 of their papers we have counts for
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Adaptive Time-Domain Harmonic Control for Noise-Vibration-Harshness Reduction of Electric Drives
Klaus Herburger, Fabian Jakob, David Gänzle +2
Reducing Noise, Vibration, and Harshness (NVH) in electric drives is crucial for applications such as electric vehicle drivetrains and heat-pump compressors, where strict NVH requi…
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 and stability of Q-learning in Hierarchical Reinforcement Learning
Massimiliano Manenti, Andrea Iannelli
Hierarchical Reinforcement Learning promises, among other benefits, to efficiently capture and utilize the temporal structure of a decision-making problem and to enhance continual…
Accelerated ADMM: Automated Parameter Tuning and Improved Linear Convergence
Meisam Tavakoli, Fabian Jakob, Guido Carnevale +2
This work studies the linear convergence of an accelerated scheme of the Alternating Direction Method of Multipliers (ADMM) for strongly convex and Lipschitz-smooth problems. We us…
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…
Hidden Convexity in Active Learning: A Convexified Online Input Design for ARX Systems
Nicolas Chatzikiriakos, Bowen Song, Philipp Rank +1
The goal of this work is to accelerate the identification of an unknown ARX system from trajectory data through online input design. Specifically, we present an active learning alg…
Complexity guarantees for risk-neutral generalized Nash equilibrium problems
Haochen Tao, Andrea Iannelli, Meggie Marschner +3
In this paper, we address \ac{SGNEP} seeking with risk-neutral agents. Our main contribution lies the development of a stochastic variance-reduced gradient (SVRG) technique, modifi…
Robustness of Online Identification-based Policy Iteration to Noisy Data
Bowen Song, Andrea Iannelli
This article investigates the core mechanisms of indirect data-driven control for unknown systems, focusing on the application of policy iteration (PI) within the context of the li…
Online Convex Optimization and Integral Quadratic Constraints: An automated approach to regret analysis
Fabian Jakob, Andrea Iannelli
We propose a novel approach for analyzing dynamic regret of first-order constrained online convex optimization algorithms for strongly convex and Lipschitz-smooth objectives. Cruci…
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,…
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…