A frequency-domain analysis of inexact gradient methods
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Abstract (arXiv)
We study robustness properties of some iterative gradient-based methods for strongly convex functions, as well as for the larger class of functions with sector-bounded gradients, under a relative error model. Proofs of the corresponding convergence rates are based on frequency-domain criteria for the stability of nonlinear systems. Applications are given to inexact versions of gradient descent and the Triple Momentum Method. To further emphasize the usefulness of frequency-domain methods, we derive improved analytic bounds for the convergence rate of Nesterov's accelerated method (in the exact setting) on strongly convex functions.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Dec 2019.
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Citing papers per year: 2022: 3; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
7 papers.
- 2022PEPit: computer-assisted worst-case analyses of first-order optimization methods in Pythoncited by 10
- 2023Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learningcited by 10
- 2024DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reductioncited by 8
- 2022Convex Programs and Lyapunov Functions for Reinforcement Learning: A Unified Perspective on the Analysis of Value-Based Methodscited by 3
- 2025Tight analyses of first-order methods with error feedbackcited by 1
- 2022Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximationcited by 0
- 2026From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similaritycited by 0
- 2026From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity3 Feb 2026
- 2025Tight analyses of first-order methods with error feedback5 Jun 2025
- 2024DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction24 Aug 2024
- 2023Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning22 Feb 2023
- 2022Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximation20 Apr 2022
- 2022Convex Programs and Lyapunov Functions for Reinforcement Learning: A Unified Perspective on the Analysis of Value-Based Methods14 Feb 2022
- 2022PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python11 Jan 2022