Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems

• URL: http://arxiv.org/abs/2411.07102v1
• Date: Mon, 11 Nov 2024 16:26:33 GMT
• Title: Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems
• Authors: Matteo Lapucci, Davide Pucci,
• Abstract summary: We explore the relationship between recent line search approaches for deep optimization in the overparametrized regime and momentum directions. We introduce algorithmic that exploits a mix of data persistency, conjugateient type rules for the definition of the momentum parameter. The resulting algorithm is empirically shown to outperform other popular methods.
• Score: 0.5156484100374059
• License:
• Abstract: In this work, we address unconstrained finite-sum optimization problems, with particular focus on instances originating in large scale deep learning scenarios. Our main interest lies in the exploration of the relationship between recent line search approaches for stochastic optimization in the overparametrized regime and momentum directions. First, we point out that combining these two elements with computational benefits is not straightforward. To this aim, we propose a solution based on mini-batch persistency. We then introduce an algorithmic framework that exploits a mix of data persistency, conjugate-gradient type rules for the definition of the momentum parameter and stochastic line searches. The resulting algorithm is empirically shown to outperform other popular methods from the literature, obtaining state-of-the-art results in both convex and nonconvex large scale training problems.

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