GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms
<|reference_start|>GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms: 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<|reference_end|>
@article{eleh2024geoadaler:,
    title={GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent
  Algorithms},
    author={Chinedu Eleh, Masuzyo Mwanza, Ekene Aguegboh, Hans-Werner van Wyk},
    journal={arXiv preprint arXiv:2405.16255},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16255},
    primaryClass={cs.LG math.OC stat.ML}
}