F. Bullo.
Contraction Theory for Dynamical Systems.
Kindle Direct Publishing,
1.2 edition,
2024.
ISBN: 979-8836646806.
[bibtex-entry]
Thesis
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]
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]
A. Davydov and F. Bullo.
Exponential Stability of Parametric Optimization-Based Controllers via Lur'e contractivity.
IEEE Control Systems Letters,
8:1277-1282,
2024.
Keyword(s): Contraction Theory.
[bibtex-entry]
R. Ofir,
F. Bullo,
and M. Margaliot.
A sufficient condition for 2-contraction of a feedback interconnection.
IEEE Transactions on Automatic Control,
2024.
Note: Submitted.
Keyword(s): Contraction Theory.
[bibtex-entry]
A. V. Proskurnikov and F. Bullo.
Regular pairings for non-quadratic Lyapunov functions and contraction analysis.
SIAM Journal on Control and Optimization,
September 2024.
Note: Submitted.
Keyword(s): Contraction Theory.
[bibtex-entry]
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]
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]
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]
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]