Stochastic Heavy Ball with Polyak Step Size and Armijo Line Search: A General Convergence Analysis

Abstract:Polyak step size (PS) and Armijo line search (ALS) have received increasing attention in stochastic optimization, with encouraging empirical performance and theoretical guarantees. However, their convergence theory for stochastic heavy ball (SHB) methods remains limited. In this work, we develop a unified convergence analysis for SHB equipped with PS and ALS. To this end, we introduce a modified Armijo rule that closely parallels the Polyak step size, together with a decoupling analysis that isolates the historical dependence induced by momentum. For SHB with standard PS and ALS, we establish expected convergence for strongly convex, convex, and non-convex objectives without interpolation or restrictive conditions on the momentum parameter. Under interpolation or strong growth, we further strengthen the results to almost sure rates and last-iterate convergence. Moreover, for general settings beyond interpolation, we prove almost sure convergence to the exact optimum or to stationarity for SHB with diminishing variants of PS and ALS. These results provide a more comprehensive theoretical view of Polyak step size and Armijo line search for stochastic heavy ball methods.

Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2609.36668 [cs.LG]

(or arXiv:2609.36668v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.36668 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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