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PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates
March 15, 2024, 4:43 a.m. | Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell
cs.LG updates on arXiv.org arxiv.org
Abstract: This paper introduces PROMISE ($\textbf{Pr}$econditioned Stochastic $\textbf{O}$ptimization $\textbf{M}$ethods by $\textbf{I}$ncorporating $\textbf{S}$calable Curvature $\textbf{E}$stimates), a suite of sketching-based preconditioned stochastic gradient algorithms for solving large-scale convex optimization problems arising in machine learning. PROMISE includes preconditioned versions of SVRG, SAGA, and Katyusha; each algorithm comes with a strong theoretical analysis and effective default hyperparameter values. In contrast, traditional stochastic gradient methods require careful hyperparameter tuning to succeed, and degrade in the presence of ill-conditioning, a ubiquitous phenomenon …
abstract algorithm algorithms arxiv cs.lg gradient machine machine learning math.oc optimization paper saga scalable scale stochastic type versions
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