1 citations · 1 across the 5 of their papers we have counts for
6 papers
Control Systems with Model Hierarchies: A Category-Theoretic Framework
Baike She, Tyler Hanks, James Fairbanks +1
Storage functions are widely used to relate a control system to a simpler model for analysis and controller design, and to quantify model mismatch when a controller is designed on…
Asynchronous Nonlinear Sheaf Diffusion for Multi-Agent Coordination
Yichen Zhao, Tyler Hanks, Hans Riess +3
Cellular sheaves and sheaf Laplacians provide a far-reaching generalization of graphs and graph Laplacians, resulting in a wide array of applications ranging from machine learning…
Distributed Multi-agent Coordination over Cellular Sheaves
Tyler Hanks, Hans Riess, Samuel Cohen +3
Techniques for coordination of multi-agent systems are vast and varied, often utilizing purpose-built solvers or controllers with tight coupling to the types of systems involved or…
A Hybrid Systems Model of Feedback Optimization for Linear Systems: Convergence and Robustness
Oscar Jed Chuy, Matthew Hale, Ricardo Sanfelice
Feedback optimization algorithms compute inputs to a system using real-time output measurements, which helps mitigate the effects of disturbances. However, existing work often mode…
Distributed Nonconvex Optimization with Exponential Convergence Rate via Hybrid Systems Methods
Katherine R. Hendrickson, Dawn M. Hustig-Schultz, Matthew T. Hale +1
We present a hybrid systems framework for distributed multi-agent optimization in which agents execute computations in continuous time and communicate in discrete time. The optimiz…
A Compositional Framework for First-Order Optimization
Tyler Hanks, Matthew Klawonn, Evan Patterson +2
Optimization decomposition methods are a fundamental tool to develop distributed solution algorithms for large scale optimization problems arising in fields such as machine learnin…