2 citations · 3 across the 6 of their papers we have counts for
11 papers · 1 filter
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.…
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…
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…
Data-Driven Exact Pole Placement for Linear Systems
Gianluca Bianchin
The exact pole placement problem concerns computing a feedback gain that will assign the poles of a system, controlled via static state feedback, at a set of pre-specified location…
Planning a Return to Normal after the COVID-19 Pandemic: Identifying Safe Contact Levels via Online Optimization
Gianluca Bianchin, Emiliano Dall'Anese, Jorge I. Poveda +3
Since the early months of 2020, non-pharmaceutical interventions (NPIs) -- implemented at varying levels of severity and based on widely-divergent perspectives of risk tolerance --…