Last-Iterate Convergence of Randomized Kaczmarz and SGD with Greedy Step Size

• URL: http://arxiv.org/abs/2604.09909v1
• Date: Fri, 10 Apr 2026 21:09:52 GMT
• Title: Last-Iterate Convergence of Randomized Kaczmarz and SGD with Greedy Step Size
• Abstract summary: We study last-iterate convergence of SGD with greedy step size over smooth quadratics.<n>We introduce the family of discrete contraction processes, whose behavior can be described by the evolution of a certain deterministic eigenvalue equation.
• Score: 4.3761172849639705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study last-iterate convergence of SGD with greedy step size over smooth quadratics in the interpolation regime, a setting which captures the classical Randomized Kaczmarz algorithm as well as other popular iterative linear system solvers. For these methods, we show that the $t$-th iterate attains an $O(1/t^{3/4})$ convergence rate, addressing a question posed by Attia, Schliserman, Sherman, and Koren, who gave an $O(1/t^{1/2})$ guarantee for this setting. In the proof, we introduce the family of stochastic contraction processes, whose behavior can be described by the evolution of a certain deterministic eigenvalue equation, which we analyze via a careful discrete-to-continuous reduction.

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