Compare different SG-Schemes based on large least square problems
This work provides an incremental analysis of existing optimizers for machine learning practitioners dealing with large-scale least-square problems.
The study reviewed and analyzed popular stochastic gradient-based optimization schemes on large least-square problems, focusing on comparing different hyper-parameters to find better model parameters.
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.