GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms

• URL: http://arxiv.org/abs/2405.16255v1
• Date: Sat, 25 May 2024 14:36:33 GMT
• Title: GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms
• Authors: Chinedu Eleh, Masuzyo Mwanza, Ekene Aguegboh, Hans-Werner van Wyk,
• Abstract summary: We introduce GeoAdaLer (Geometric Adaptive Learner), a novel adaptive learning method for gradient descent optimization. The proposed method extends the concept of adaptive learning by introducing a geometrically inclined approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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