arXiv (All)
open access
A unified analysis on the speedup of accelerated gradient methods: An inertial dynamics approach
Nesterov's Accelerated Gradient Method is one of the most popular first order optimization algorithms. When applied to $L$-smooth, $μ$-strongly convex functions, it converges at a rate of $\mathcal{O}\left(\left(1-\sqrt{\fracμ{L}}\right)^{k}\right)$. The more recent {\it Triple Momentum Method} and
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- An Inexact Augmented Lagrangian Method for $(L_0, L_1)$-Smooth Convex Optimization#819718
- An Inexact Augmented Lagrangian Method for $(L_0, L_1)$-Smooth Convex Optimization#809722
- Nonsmooth Optimization via Orthogonalized Momentum#1110366
- Stronger Lower Bounds for (Non-)Anytime Acceleration of Gradient Descent#927502
- Several Accelerated and Stable Pseudo-Energy-Dissipative Lagrange Multiplier Methods based on Second-order Flow and Variable Splitting#1144662
Record · ID 999641
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive