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arXiv · 2405.16255

GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms

Chinedu Eleh·Masuzyo Mwanza·Ekene Aguegboh·Hans-Werner van Wyk
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Abstract

The Adam optimization method has achieved remarkable success in addressing contemporary challenges in stochastic optimization. This method falls within the realm of adaptive sub-gradient techniques, yet the underlying geometric principles guiding its performance have remained shrouded in mystery, and have long confounded researchers. In this paper, we introduce GeoAdaLer (Geometric Adaptive Learner), a novel adaptive learning method for stochastic gradient descent optimization, which draws from the geometric properties of the optimization landscape. Beyond emerging as a formidable contender, the proposed method extends the concept of adaptive learning by introducing a geometrically inclined approach that enhances the interpretability and effectiveness in complex optimization scenarios

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Chinedu Eleh, Masuzyo Mwanza, Ekene Aguegboh, Hans-Werner van Wyk. 2025-05-31. GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms. https://arxiv.org/abs/2405.16255

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