Alexander Davydov

Orcid: 0000-0001-5629-2565

Affiliations:
  • University of Maryland, Department of Mechanical Engineering, College Park, MD, USA


According to our database1, Alexander Davydov authored at least 30 papers between 2020 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Book  In proceedings  Article  PhD thesis  Dataset  Other 

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Online presence:

On csauthors.net:

Bibliography

2026
Timescale Limits of Linear-Threshold Networks.
CoRR, April, 2026

Learning Certified Neural Network Controllers Using Contraction and Interval Analysis.
CoRR, March, 2026

Incremental Input-to-State Stability and Equilibrium Tracking for Stochastic Contracting Dynamics.
CoRR, February, 2026

2025
Time-Varying Convex Optimization: A Contraction and Equilibrium Tracking Approach.
IEEE Trans. Autom. Control., November, 2025

First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling.
IEEE Robotics Autom. Lett., November, 2025

Non-Euclidean Contraction Analysis of Continuous-Time Neural Networks.
IEEE Trans. Autom. Control., January, 2025

2024
Positive Competitive Networks for Sparse Reconstruction.
Neural Comput., 2024

Non-Euclidean Monotone Operator Theory and Applications.
J. Mach. Learn. Res., 2024

Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications.
IEEE Control. Syst. Lett., 2024

Perspectives on Contractivity in Control, Optimization, and Learning.
IEEE Control. Syst. Lett., 2024

Exponential Stability of Parametric Optimization-Based Controllers via Lur'e Contractivity.
IEEE Control. Syst. Lett., 2024

On Weakly Contracting Dynamics for Convex Optimization.
IEEE Control. Syst. Lett., 2024

Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023
Non-Euclidean Contraction Theory for Monotone and Positive Systems.
IEEE Trans. Autom. Control., September, 2023

The Yakubovich S-Lemma Revisited: Stability and Contractivity in Non-Euclidean Norms.
SIAM J. Control. Optim., August, 2023

Contractivity of Distributed Optimization and Nash Seeking Dynamics.
IEEE Control. Syst. Lett., 2023

Euclidean Contractivity of Neural Networks With Symmetric Weights.
IEEE Control. Syst. Lett., 2023

Contracting Dynamics for Time-Varying Convex Optimization.
CoRR, 2023

On the Equivalence of Multi-Agent 2D Coverage Control and Leader-Follower Consensus Network.
Proceedings of the American Control Conference, 2023

2022
Non-Euclidean Contraction Theory for Robust Nonlinear Stability.
IEEE Trans. Autom. Control., 2022

Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach.
CoRR, 2022

Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach.
Proceedings of the Learning for Dynamics and Control Conference, 2022

Non-Euclidean Monotone Operator Theory with Applications to Recurrent Neural Networks.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Non-Euclidean Contractivity of Recurrent Neural Networks.
Proceedings of the American Control Conference, 2022

2021
Non-Euclidean Contraction Theory via Semi-Inner Products.
CoRR, 2021

Robust Implicit Networks via Non-Euclidean Contractions.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

From Contraction Theory to Fixed Point Algorithms on Riemannian and Non-Euclidean Spaces.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Pursuer Coordination in Multi-Player Reach-Avoid Games through Control Barrier Functions.
Proceedings of the 2021 American Control Conference, 2021

2020
Sparsity Structure and Optimality of Multi-Robot Coverage Control.
IEEE Control. Syst. Lett., 2020


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