Polyak's Heavy Ball Method Achieves Accelerated Local Rate of Convergence under Polyak-Lojasiewicz Inequality
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Abstract (arXiv)
In this work, we analyze the convergence of Polyak's heavy ball method in both continuous and discrete time for non-convex C4-objective functions satisfying the Polyak-Lojasiewicz inequality. Under this weak assumption, we recover the asymptotic convergence rates originally derived by Polyak in [Polyak, U.S.S.R. Comput. Math. and Math. Phys., 1964] for strongly convex objectives. Our results demonstrate that the heavy ball method exhibits asymptotic local acceleration on this class of functions. In particular, in the discrete time setting, we prove local convergence of the iterates to a minimum once the method enters a sufficiently small neighborhood of the set of minima, for a broad range of hyperparameters, including aggressive choices for the momentum parameter and the step-size for which global convergence is known to fail. Instead of the usually employed Lyapunov-type arguments, our approach leverages a new differential geometric perspective of the Polyak-Lojasiewicz inequality proposed in [Rebjock and Boumal, Math. Program., 2025].
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 28 of its 64 references that are in the map. First posted on arXiv 22 Oct 2024.
Builds on
28 of 64 references.
References · 28
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16 more
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- 2021Convergence rates for the Heavy-Ball continuous dynamics for non-convex optimization, under Polyak-L ojasiewicz conditioncited by 11
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025Towards understanding Accelerated Stein Variational Gradient Flow -- Analysis of Generalized Bilinear Kernels for Gaussian target distributionscited by 2
- 2026Towards Understanding Momentum Acceleration in River-Valley Loss Landscapecited by 1
- 2026Optimal Asymptotic Rates for (Stochastic) Gradient Descent under the Local PL-Condition: A Geometric Approachcited by 0
- 2026Towards Understanding Momentum Acceleration in River-Valley Loss Landscape25 Sep 2026
- 2026Optimal Asymptotic Rates for (Stochastic) Gradient Descent under the Local PL-Condition: A Geometric Approach14 May 2026
- 2025Towards understanding Accelerated Stein Variational Gradient Flow -- Analysis of Generalized Bilinear Kernels for Gaussian target distributions4 Sep 2025