Implementation of Mini-Batch Conjugate Gradient with Data Persistency (MBCG-DP) proposed in
In order to execute the code, you will need a working Anaconda installation. We suggest the creation of a new conda environment with Python 3.11.3 or above.
Requirements can be installed through:
pip install -r requirements.txt
In order to run the experiments, execute the following:
python trainval.py [options]
The following arguments shall be specified:
| Short Option | Long Option | Type | Description | Default |
|---|---|---|---|---|
-e |
--exp_group_list |
str |
List of experiments to be executed | None (required) |
-d |
--datadir |
str |
Path to save the datasets (downloaded automatically) | None (required) |
-sb |
--savedir_base |
str |
Path to save the output logs | None (required) |
The file exp_configs.py specifies the experiments avaialable and their configurations.
An example usage is:
python trainval -e mushrooms -d Datasets -sb Results
The PlotResults functionalities enable the generation of plots based on logs produced during the experiments.
Our experimental framework is a simplified version of that of SLS and PoNoS.
In case you employed our code for research purposes, please cite:
@misc{lapucci2024effectivelyleveragingmomentumterms,
title={Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems},
author={Matteo Lapucci and Davide Pucci},
year={2024},
eprint={2411.07102},
archivePrefix={arXiv},
primaryClass={math.OC},
url={https://arxiv.org/abs/2411.07102},
}