Publications

2025

• A. Davydov, V. Centorrino, A. Gokhale, G. Russo, and F. Bullo. Time-varying convex optimization: A contraction and equilibrium tracking approach. IEEE Transactions on Automatic Control, 2025. To Appear. doi:10.1109/TAC.2025.3576043.
• A. Davydov, F. Djeumou, M. Greiff, M. Suminaka, M. Thompson, J. Subosits, and T. Lew. First, learn what you don’t know: Active information gathering for driving at the limits of handling. IEEE Robotics and Automation Letters, 2025. To Appear. URL: https://arxiv.org/abs/2411.00107.
• A. Davydov, A. V. Proskurnikov, and F. Bullo. Non-Euclidean contraction analysis of continuous-time neural networks. IEEE Transactions on Automatic Control, 70(1):235–250, 2025. doi:10.1109/TAC.2024.3422217.

2024

• V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Positive competitive networks for sparse reconstruction. Neural Computation, 36(6):1163–1197, 2024. doi:10.1162/neco_a_01657.
• V. Centorrino, A. Davydov, A. Gokhale, G. Russo, and F. Bullo. On weakly contracting dynamics for convex optimization. IEEE Control Systems Letters, 8:1745–1750, 2024. doi:10.1109/LCSYS.2024.3414348.
• A. Gokhale, A. Davydov, and F. Bullo. Proximal gradient dynamics: Monotonicity, exponential convergence, and applications. IEEE Control Systems Letters, pages 2853–2858, 2024. doi:10.1109/LCSYS.2024.3516632.
• S. Jaffe, A. Davydov, D. Lapsekili, A. K. Singh, and F. Bullo. Learning neural contracting dynamics: Extended linearization and global guarantees. In Advances in Neural Information Processing Systems. 2024. URL: https://openreview.net/forum?id=YYnP3Xpv3y.
• A. Davydov and F. Bullo. Exponential stability of parametric optimization-based controllers via Lur’e contractivity. IEEE Control Systems Letters, 8:1277–1282, 2024. doi:10.1109/LCSYS.2024.3408110.
• A. Davydov and F. Bullo. Perspectives on contractivity in control, optimization, and learning. IEEE Control Systems Letters, 8:2087–2098, 2024. doi:10.1109/LCSYS.2024.3436127.
• A. Davydov, S. Jafarpour, A. V. Proskurnikov, and F. Bullo. Non-Euclidean monotone operator theory and applications. Journal of Machine Learning Research, 25(307):1–33, 2024. URL: http://www.jmlr.org/papers/v25/23-0805.html.

2023

• V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Euclidean contractivity of neural networks with symmetric weights. IEEE Control Systems Letters, 7:1724–1729, 2023. doi:10.1109/LCSYS.2023.3278250.
• A. Gokhale, A. Davydov, and F. Bullo. Contractivity of distributed optimization and Nash seeking dynamics. IEEE Control Systems Letters, 7:3896–3901, 2023. doi:10.1109/LCSYS.2023.3341987.
• S. Jafarpour, A. Davydov, and F. Bullo. Non-Euclidean contraction theory for monotone and positive systems. IEEE Transactions on Automatic Control, 68(9):5653–5660, 2023. doi:10.1109/TAC.2022.3224094.
• A. V. Proskurnikov, A. Davydov, and F. Bullo. The Yakubovich S-Lemma revisited: Stability and contractivity in non-Euclidean norms. SIAM Journal on Control and Optimization, 61(4):1955–1978, 2023. doi:10.1137/22M1512600.
• X. Xu, A. Davydov, and Y. Diaz-Mercado. On the equivalence of multi-agent 2d coverage control and leader-follower consensus network. In American Control Conference, 503–508. 2023. doi:10.23919/ACC55779.2023.10156475.
• A. Davydov, S. Jaffe, A. K. Singh, and F. Bullo. Retrieving $k$-nearest neighbors with modern Hopfield networks. In NeurIPS Workshop on Associative Memory & Hopfield Networks in 2023. 2023. URL: https://openreview.net/forum?id=bNBMnQXRJU.

2022

• S. Jafarpour, M. Abate, A. Davydov, F. Bullo, and S. Coogan. Robustness certificates for implicit neural networks: A mixed monotone contractive approach. In Learning for Dynamics and Control Conference, 917–930. 2022. URL: https://proceedings.mlr.press/v168/jafarpour22a.
• S. Jafarpour, A. Davydov, M. Abate, F. Bullo, and S. Coogan. Robust training and verification of implicit neural networks: a non-Euclidean contractive approach. In ICML Workshop on Formal Verification of Machine Learning. 2022. URL: https://arxiv.org/abs/2208.03889.
• A. Davydov, S. Jafarpour, M. Abate, F. Bullo, and S. Coogan. Comparative analysis of interval reachability for robust implicit and feedforward neural networks. In IEEE Conference on Decision and Control, 2073–2078. 2022. doi:10.1109/CDC51059.2022.9993217.
• A. Davydov, S. Jafarpour, and F. Bullo. Non-Euclidean contraction theory for robust nonlinear stability. IEEE Transactions on Automatic Control, 67(12):6667–6681, 2022. doi:10.1109/TAC.2022.3183966.
• A. Davydov, S. Jafarpour, A. V. Proskurnikov, and F. Bullo. Non-Euclidean monotone operator theory with applications to recurrent neural networks. In IEEE Conference on Decision and Control, 6332–6337. 2022. doi:10.1109/CDC51059.2022.9993197.
• A. Davydov, A. V. Proskurnikov, and F. Bullo. Non-Euclidean contractivity of recurrent neural networks. In American Control Conference, 1527–1534. 2022. O. Hugo Schuck Award. doi:10.23919/ACC53348.2022.9867357.

2021

• F. Bullo, P. Cisneros-Velarde, A. Davydov, and S. Jafarpour. From contraction theory to fixed point algorithms on Riemannian and non-Euclidean spaces. In IEEE Conference on Decision and Control, 2923–2928. 2021. Invited Tutorial Paper. doi:10.1109/CDC45484.2021.9682883.
• S. Jafarpour, A. Davydov, A. V. Proskurnikov, and F. Bullo. Robust implicit networks via non-Euclidean contractions. In Advances in Neural Information Processing Systems. 2021. URL: https://openreview.net/forum?id=SwfsoPuGYku.
• A. Davydov, P. Rivera-Ortiz, and Y. Diaz-Mercado. Pursuer coordination in multi-player reach-avoid games through control barrier functions. IEEE Control Systems Letters, 5(6):1910–1915, 2021. doi:10.1109/LCSYS.2020.3044549.

2020

• A. Davydov and Y. Diaz-Mercado. Sparsity structure and optimality of multi-robot coverage control. IEEE Control Systems Letters, 4(1):13–18, 2020. doi:10.1109/LCSYS.2019.2921513.
• A. Davydov, D. W. Yeo, and Y. Diaz-Mercado. Low-mobility atmospheric sensing via multi-vehicle adaptive coverage control. In AIAA Aviation Forum, 2821. 2020. doi:10.2514/6.2020-2821.