Non-Euclidean Contraction Theory for Robust Nonlinear Stability
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Abstract (arXiv)
We study necessary and sufficient conditions for contraction and incremental stability of dynamical systems with respect to non-Euclidean norms. First, we introduce weak pairings as a framework to study contractivity with respect to arbitrary norms, and characterize their properties. We introduce and study the sign and max pairings for the ℓ1 and ℓ_∞ norms, respectively. Using weak pairings, we establish five equivalent characterizations for contraction, including the one-sided Lipschitz condition for the vector field as well as matrix measure and Demidovich conditions for the corresponding Jacobian. Third, we extend our contraction framework in two directions: we prove equivalences for contraction of continuous vector fields and we formalize the weaker notion of equilibrium contraction, which ensures exponential convergence to an equilibrium. Finally, as an application, we provide (i) incremental input-to-state stability and finite input-state gain properties for contracting systems, and (ii) a general theorem about the Lipschitz interconnection of contracting systems, whereby the Hurwitzness of a gain matrix implies the contractivity of the interconnected system.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Mar 2021.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 2; 2024: 1; 2025: 2.
8 papers.
- 2021Robust Implicit Networks via Non-Euclidean Contractionscited by 19
- 2024Learning Neural Contracting Dynamics: Extended Linearization and Global Guaranteescited by 11
- 2021Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approachcited by 8
- 2021A Contraction Theory Approach to Optimization Algorithms from Acceleration Flowscited by 3
- 2023Contraction-Guided Adaptive Partitioning for Reachability Analysis of Neural Network Controlled Systemscited by 3
- 2023RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustnesscited by 1
- 2025Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomycited by 1
- 2025Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioningcited by 0
- 2025Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy19 Dec 2025
- 2025Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning1 Dec 2025
- 2024Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees12 Feb 2024
- 2023RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness22 Jun 2023
- 2023Contraction-Guided Adaptive Partitioning for Reachability Analysis of Neural Network Controlled Systems7 Apr 2023
- 2021Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach10 Dec 2021
- 2021Robust Implicit Networks via Non-Euclidean Contractions6 Jun 2021
- 2021A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows18 May 2021