Lyapunov Theory for Discrete Time Systems
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Abstract (arXiv)
In this work, we present the equivalent of many theorems available for continuous time systems. In particular, the theory is applied to Averaging Theory and Separation of time scales. In particular the proofs developed for Averaging Theory and Separation of time scales departs from those typically used in continuous time systems that are based on twice differentiable change of variables and the multiple use of the Implicit Function Theorem and Mean Value Theorem. More specifically, by constructing a suitable Lyapunov function only Lipschitz conditions are necessary. Finally, it is shown that under mild condition on the so-called "interconnection conditions" the proposed tools can guarantee semi-global exponential stability rather than the more stringent local exponential stability typically found in the literature
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Sep 2018.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 3; 2024: 2; 2025: 2; 2026: 1.
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- 2023Neural Lyapunov Control for Discrete-Time Systemscited by 21
- 2022Quadratic models for understanding catapult dynamics of neural networkscited by 17
- 2021Almost Surely Stable Deep Dynamicscited by 16
- 2024Computing the Bias of Constant-step Stochastic Approximation with Markovian Noisecited by 9
- 2022Emergent specialization from participation dynamics and multi-learner retrainingcited by 5
- 2021Local Convergence of Adaptive Gradient Descent Optimizerscited by 4
- 2022An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX modelscited by 2
- 2021Fast symplectic integrator for Nesterov-type acceleration methodcited by 1
- 2021Extending AdamW by Leveraging Its Second Moment and Magnitudecited by 1
- 2023Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensuscited by 1
- 2023Self-supervised Deep Hyperspectral Inpainting with the Sparsity and Low-Rank Considerationscited by 1
- 2024Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systemscited by 1
- 2026Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial26 May 2026
- 2025Learnable Mixed Nash Equilibria are Collectively Rational16 Oct 2025
- 2025Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Models14 Jan 2025
- 2024Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systems12 Sep 2024
- 2024Computing the Bias of Constant-step Stochastic Approximation with Markovian Noise23 May 2024
- 2023Self-supervised Deep Hyperspectral Inpainting with the Sparsity and Low-Rank Considerations13 Jun 2023
- 2023Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus13 May 2023
- 2023Neural Lyapunov Control for Discrete-Time Systems11 May 2023
- 2022Emergent specialization from participation dynamics and multi-learner retraining6 Jun 2022
- 2022Quadratic models for understanding catapult dynamics of neural networks24 May 2022
- 2022An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models30 Mar 2022
- 2021Extending AdamW by Leveraging Its Second Moment and Magnitude9 Dec 2021
3 more
- 2021Fast symplectic integrator for Nesterov-type acceleration method1 Jun 2021
- 2021Almost Surely Stable Deep Dynamics26 Mar 2021
- 2021Local Convergence of Adaptive Gradient Descent Optimizers19 Feb 2021
- 2023Neural Lyapunov Control for Discrete-Time Systemscited by 21
- 2022Quadratic models for understanding catapult dynamics of neural networkscited by 17
- 2021Almost Surely Stable Deep Dynamicscited by 16
- 2024Computing the Bias of Constant-step Stochastic Approximation with Markovian Noisecited by 9
- 2022Emergent specialization from participation dynamics and multi-learner retrainingcited by 5
- 2021Local Convergence of Adaptive Gradient Descent Optimizerscited by 4
- 2022An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX modelscited by 2
- 2021Fast symplectic integrator for Nesterov-type acceleration methodcited by 1
- 2021Extending AdamW by Leveraging Its Second Moment and Magnitudecited by 1
- 2023Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensuscited by 1
- 2023Self-supervised Deep Hyperspectral Inpainting with the Sparsity and Low-Rank Considerationscited by 1
- 2024Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systemscited by 1
12 most cited of 15 All 15 (JSON)
- 2026Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial26 May 2026
- 2025Learnable Mixed Nash Equilibria are Collectively Rational16 Oct 2025
- 2025Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Models14 Jan 2025
- 2024Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systems12 Sep 2024
- 2024Computing the Bias of Constant-step Stochastic Approximation with Markovian Noise23 May 2024
- 2023Self-supervised Deep Hyperspectral Inpainting with the Sparsity and Low-Rank Considerations13 Jun 2023
- 2023Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus13 May 2023
- 2023Neural Lyapunov Control for Discrete-Time Systems11 May 2023
- 2022Emergent specialization from participation dynamics and multi-learner retraining6 Jun 2022
- 2022Quadratic models for understanding catapult dynamics of neural networks24 May 2022
- 2022An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models30 Mar 2022
- 2021Extending AdamW by Leveraging Its Second Moment and Magnitude9 Dec 2021
12 newest of 15 All 15 (JSON)