arXiv Machine Learning July 2, 2026 By Ahmet Alacaoglu, Yura Malitsky, Stephen J. Wright

Towards Weaker Variance Assumptions for Stochastic Optimization

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arXiv:2504. 09951v2 Announce Type: replace-cross Abstract: We revisit a classical assumption for analyzing stochastic gradient algorithms where the squared norm of the stochastic subgradient (or the variance for smooth problems) is allowed to grow as fast as the squared norm of the optimization variable.

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