5 papers
The Internal Model Principle of Time-Varying Optimization
Gianluca Bianchin, Bryan Van Scoy
Time-varying optimization problems are central to many engineering applications, where performance metrics and system constraints evolve dynamically with time. Several algorithms h…
Temporal Variabilities Limit Convergence Rates in Gradient-Based Online Optimization
Bryan Van Scoy, Gianluca Bianchin
This paper investigates the fundamental performance limits of gradient-based algorithms for time-varying optimization. Leveraging the internal model principle and root locus techni…
Feedback Optimization of Dynamical Systems in Time-Varying Environments: An Internal Model Principle Approach
Gianluca Bianchin, Bryan Van Scoy
Feedback optimization has emerged as a promising approach for regulating dynamical systems to optimal steady states that are implicitly defined by underlying optimization problems.…
Optimization of Linear Multi-Agent Dynamical Systems via Feedback Distributed Gradient Descent Methods
Amir Mehrnoosh, Gianluca Bianchin
Feedback optimization is an increasingly popular control paradigm to optimize dynamical systems, accounting for control objectives that concern the system operation at steady-state…
k-Dimensional Agreement in Multiagent Systems
Gianluca Bianchin, Miguel Vaquero, Jorge Cortes +1
Given a network of agents, we study the problem of designing a distributed algorithm that computes k independent weighted means of the network's initial conditions (namely, the age…