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Compare different SG-Schemes based on large least square problems

arXiv 2503.01507 ↗First posted 3 Mar 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

This study reviews popular stochastic gradient-based schemes based on large least-square problems. These schemes, often called optimizers in machine learning, play a crucial role in finding better model parameters. Hence, this study focuses on viewing such optimizers with different hyper-parameters and analyzing them based on least square problems. Codes that produced results in this work are available on https://github.com/q-viper/gradients-based-methods-on-large-least-square.

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References · 3

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2016An overview of gradient descent optimization algorithmscited by 1,033

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