Accelerated Over-Relaxation Heavy-Ball Method: Achieving Global Accelerated Convergence with Broad Generalization
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Abstract (arXiv)
The heavy-ball momentum method accelerates gradient descent with a momentum term but lacks accelerated convergence for general smooth strongly convex problems. This work introduces the Accelerated Over-Relaxation Heavy-Ball (AOR-HB) method, the first variant with provable global and accelerated convergence for such problems. AOR-HB closes a long-standing theoretical gap, extends to composite convex optimization and min-max problems, and achieves optimal complexity bounds. It offers three key advantages: (1) broad generalization ability, (2) potential to reshape acceleration techniques, and (3) conceptual clarity and elegance compared to existing methods.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Jun 2024.
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Citing papers per year: 2025: 3; 2026: 2.
5 papers.
- 2025Gradient-Variation Online Adaptivity for Accelerated Optimization with H"older Smoothnesscited by 5
- 2025Near-Optimal Convergence of Accelerated Gradient Methods under Generalized and (L0, L1)-Smoothnesscited by 1
- 2025Optimistic Online-to-Batch Conversions for Accelerated Convergence and Universalitycited by 1
- 2026Towards Understanding Momentum Acceleration in River-Valley Loss Landscapecited by 1
- 2026The Row Normalization Puzzle in Muoncited by 0
- 2026The Row Normalization Puzzle in Muon30 Sep 2026
- 2026Towards Understanding Momentum Acceleration in River-Valley Loss Landscape25 Sep 2026
- 2025Optimistic Online-to-Batch Conversions for Accelerated Convergence and Universality10 Nov 2025
- 2025Gradient-Variation Online Adaptivity for Accelerated Optimization with H"older Smoothness4 Nov 2025
- 2025Near-Optimal Convergence of Accelerated Gradient Methods under Generalized and (L0, L1)-Smoothness9 Aug 2025