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Matthew T. Hale

5 papers hereh-index 318 citations7 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • math.OC4
  • eess.SY1
same name
  • Matthew T. Hale — 13 papers, h 4
  • Matthew T. Hale — 4 papers, h 1
  • Matthew T. Hale — 2 papers, h 1
  • Matthew T. Hale — 2 papers, h 2
  • Matthew T. Hale — 1 paper, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedA Compositional Framework for First-Order Optimization

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing math.OCShow all

4 papers · 1 filter

math.OC2025

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…

math.OC2025

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…

math.OC2025

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…

math.OC2024★ 1 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.