A Concise Lyapunov Analysis of Nesterov's Accelerated Gradient Method
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913929092923392 |
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| author | Liu, Jun |
| author_facet | Liu, Jun |
| contents | Convergence analysis of Nesterov's accelerated gradient method has attracted significant attention over the past decades. While extensive work has explored its theoretical properties and elucidated the intuition behind its acceleration, a simple and direct proof of its convergence rates is still lacking. We provide a concise Lyapunov analysis of the convergence rates of Nesterov's accelerated gradient method for both general convex and strongly convex functions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17373 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Concise Lyapunov Analysis of Nesterov's Accelerated Gradient Method Liu, Jun Optimization and Control Machine Learning Systems and Control Convergence analysis of Nesterov's accelerated gradient method has attracted significant attention over the past decades. While extensive work has explored its theoretical properties and elucidated the intuition behind its acceleration, a simple and direct proof of its convergence rates is still lacking. We provide a concise Lyapunov analysis of the convergence rates of Nesterov's accelerated gradient method for both general convex and strongly convex functions. |
| title | A Concise Lyapunov Analysis of Nesterov's Accelerated Gradient Method |
| topic | Optimization and Control Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2502.17373 |