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Publications about 'Contraction Theory'
Books and proceedings
  1. F. Bullo. Contraction Theory for Dynamical Systems. Kindle Direct Publishing, 1.1 edition, 2023. ISBN: 979-8836646806. [bibtex-entry]


Thesis
  1. F. Seccamonte. Bilevel Optimization in Learning and Control with Applications to Network Flow Estimation. PhD thesis, Mechanical Engineering Department, University of California at Santa Barbara, September 2023. Keyword(s): Contraction Theory, Network Systems, Power Networks. [bibtex-entry]


  2. K. D. Smith. Control and Estimation in Network Systems. PhD thesis, Electrical and Computer Engineering Department, University of California at Santa Barbara, December 2022. Keyword(s): Contraction Theory, Network Systems, Power Networks. [bibtex-entry]


Articles in journal, book chapters
  1. V. Centorrino, F. Bullo, and G. Russo. Modelling and Contractivity of Neural-Synaptic Networks with Hebbian Learning. Automatica, 164:111636, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  2. V. Centorrino, A. Davydov, A. Gokhale, G. Russo, and F. Bullo. On Weakly Contracting Dynamics for Convex Optimization. IEEE Control Systems Letters, March 2024. Note: Submitted. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  3. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Positive Competitive Networks for Sparse Reconstruction. Neural Computation, January 2024. Note: To appear. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  4. O. Dalin, R. Ofir, E. Bar Shalom, A. Ovseevich, F. Bullo, and M. Margaliot. Verifying $k$-Contraction without Computing $k$-Compounds. IEEE Transactions on Automatic Control, 69(3):1492-1506, 2024. Keyword(s): Contraction Theory. [bibtex-entry]


  5. A. Davydov and F. Bullo. Exponential Stability of Parametric Optimization-Based Controllers via Lur'e contractivity. IEEE Control Systems Letters, 2024. Note: Submitted. Keyword(s): Contraction Theory. [bibtex-entry]


  6. A. Davydov and F. Bullo. Perspectives on Contractivity in Control, Optimization and Learning. IEEE Control Systems Letters, April 2024. Note: Submitted. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  7. G. De Pasquale, K. D. Smith, F. Bullo, and M. E. Valcher. Dual Seminorms, Ergodic Coefficients, and Semicontraction Theory. IEEE Transactions on Automatic Control, 69(5), 2024. Note: To appear. Keyword(s): Contraction Theory. [bibtex-entry]


  8. 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. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  9. L. Cothren, F. Bullo, and E. Dall'Anese. Singular Perturbation via Contraction Theory. IEEE Transactions on Automatic Control, October 2023. Note: Submitted. Keyword(s): Contraction Theory. [bibtex-entry]


  10. A. Davydov, V. Centorrino, A. Gokhale, G. Russo, and F. Bullo. Contracting Dynamics for Time-Varying Convex Optimization. IEEE Transactions on Automatic Control, June 2023. Note: Submitted. Keyword(s): Contraction Theory. [bibtex-entry]


  11. A. Davydov, S. Jafarpour, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Monotone Operator Theory and Applications. Journal of Machine Learning Research, June 2023. Note: Submitted. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  12. A. Davydov, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Contraction Analysis of Continuous-Time Neural Networks. IEEE Transactions on Automatic Control, August 2023. Note: Submitted. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  13. R. Delabays and F. Bullo. Semicontraction and Synchronization of Kuramoto-Sakaguchi Oscillator Networks. IEEE Control Systems Letters, 7:1566-1571, 2023. Keyword(s): Contraction Theory, Oscillator Networks. [bibtex-entry]


  14. 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. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  15. 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. Keyword(s): Contraction Theory. [bibtex-entry]


  16. K. D. Smith and F. Bullo. Contractivity of the Method of Successive Approximations for Optimal Control. IEEE Control Systems Letters, 7:919-924, 2023. Keyword(s): Contraction Theory. [bibtex-entry]


  17. P. Cisneros-Velarde, S. Jafarpour, and F. Bullo. Contraction Theory for Dynamical Systems on Hilbert Spaces. IEEE Transactions on Automatic Control, 67(12):6710-6715, 2022. Keyword(s): Contraction Theory. [bibtex-entry]


  18. P. Cisneros-Velarde, S. Jafarpour, and F. Bullo. Distributed and Time-Varying Primal-Dual Dynamics via Contraction Analysis. IEEE Transactions on Automatic Control, 67(7):3560-3566, 2022. Keyword(s): Contraction Theory. [bibtex-entry]


  19. 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. Keyword(s): Contraction Theory. [bibtex-entry]


  20. S. Jafarpour, P. Cisneros-Velarde, and F. Bullo. Weak and Semi-Contraction for Network Systems and Diffusively-Coupled Oscillators. IEEE Transactions on Automatic Control, 67(3):1285-1300, 2022. Keyword(s): Contraction Theory. [bibtex-entry]


  21. R. Ofir, F. Bullo, and M. Margaliot. Minimum Effort Decentralized Control Design for Contracting Network Systems. IEEE Control Systems Letters, 6:2731-2736, 2022. Keyword(s): Contraction Theory. [bibtex-entry]


  22. J. W. Simpson-Porco and F. Bullo. Contraction Theory on Riemannian Manifolds. Systems & Control Letters, 65:74-80, 2014. Keyword(s): Nonlinear Control, Mechanical Control Systems, Contraction Theory. [bibtex-entry]


Conference articles
  1. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Biologically Plausible Neural Networks for Sparse Reconstruction: A Normative Framework. In Workshop “Mathematics for Artificial Intelligence and Machine Learning”, Milan, Italy, january 2024. Note: Oral Presentation. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  2. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Towards a Top/Down Normative Framework for a Biologically Plausible Explanation of Neural Circuits: Application to Sparse Reconstruction Problems. In 5th International Convention on the Mathematics of Neuroscience and AI, Rome, Italy, May 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  3. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Contractivity of Symmetric Neural Networks for Non-negative Sparse Approximation. In CCS/Italy 2023 Italian Regional Conference on Complex Systems, Naples, Italy, october 2023. Note: Poster Presentation. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  4. V. Centorrino, F. Bullo, and G. Russo. Contraction Analysis of Hopfield Neural Networks with Hebbian Learning. In IEEE Conf. on Decision and Control, Cancun, Mexico, December 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  5. P. Cisneros-Velarde and F. Bullo. A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows. In International Conference on Artificial Intelligence and Statistics, May 2022. Keyword(s): Contraction Theory. [bibtex-entry]


  6. 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 Conf. on Decision and Control, Cancun, Mexico, 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  7. A. Davydov, S. Jafarpour, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Monotone Operator Theory with Applications to Recurrent Neural Networks. In IEEE Conf. on Decision and Control, Cancun, Mexico, December 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  8. A. Davydov, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Contractivity of Recurrent Neural Networks. In American Control Conference, Atlanta, USA, pages 1527-1534, May 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  9. 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, June 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  10. 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, July 2022. Keyword(s): Contraction Theory. [bibtex-entry]


  11. K. D. Smith, F. Seccamonte, A. Swami, and F. Bullo. Physics-Informed Implicit Representations of Equilibrium Network Flows. In Advances in Neural Information Processing Systems, November 2022. Keyword(s): Contraction Theory. [bibtex-entry]


  12. 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 Conf. on Decision and Control, December 2021. Keyword(s): Contraction Theory. [bibtex-entry]


  13. S. Jafarpour, A. Davydov, A. V. Proskurnikov, and F. Bullo. Robust Implicit Networks via Non-Euclidean Contractions. In Advances in Neural Information Processing Systems, December 2021. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


Miscellaneous
  1. G. De Pasquale, K. D. Smith, F. Bullo, and M. E. Valcher. Dual Seminorms, Ergodic Coefficients, and Semicontraction Theory, 2022. Note: Available at http://arxiv.org/abs/2201.03103. Keyword(s): Contraction Theory. [bibtex-entry]


  2. S. Jafarpour, P. Cisneros-Velarde, and F. Bullo. Weak and Semi-Contraction Theory with Application to Network Systems, 2020. Note: ArXiv e-print. Keyword(s): Contraction Theory. [bibtex-entry]



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