The convergence of sensing, computing, communication and control elements drives the
traditional point-to-point control systems towards networked control systems. Sampled-data …

Gradient-based methods have been widely used for system design and optimization in
diverse application domains. Recently, there has been a renewed interest in studying …

In distributed optimization of multi-agent systems, agents cooperate to minimize a global
function which is a sum of local objective functions. Motivated by applications including …

This paper addresses the optimal control problem known as the linear quadratic regulator in
the case when the dynamics are unknown. We propose a multistage procedure, called …

This monograph covers some recent advances in a range of acceleration techniques
frequently used in convex optimization. We first use quadratic optimization problems to …

Certifying the safety or robustness of neural networks against input uncertainties and
adversarial attacks is an emerging challenge in the area of safe machine learning and …

This paper introduces a new parameterization of deep neural networks (both fully-connected
and convolutional) with guaranteed $\ell^ 2$ Lipschitz bounds, ie limited sensitivity to input …

This study provides an overview and in‐depth analysis of recent advances in stability of
linear systems with time‐varying delays. First, recent developments of a delay convex …

This article presents basic concepts and recent research directions about the stability of
sampled-data systems with aperiodic sampling. We focus mainly on the stability problem for …

This paper develops a new framework to analyze and design iterative optimization
algorithms built on the notion of integral quadratic constraints (IQCs) from robust control …