This repository contains 4 optimization algorithms: Gradient Descent, Randomized BCGD, Cyclic BCGD and BCGD with Gauss Southwell rule

common.py contains the functions to calculate the gradient, the objective function and stepsizes

GradientDescent contains the implementation of the Gradient Descent algorithm.

RandomBCGD.py contains the implemetation of the Randomized BCGD algorithm.

CyclicBCDG.py contains our implementation of the Cyclic BCGD algorithm.

GaussSouthwellBCDG.py contains our implementation of the Gauss Southwell BCGD algorithm.

config.yml contains the parameters used to run the algorithms in main.ipynb.

main.ipynb shows the results of running the algorithms on randomly generated data points as well as on the real dataset.

Authors

The current version of the code is implemented by Anna Badalyan, based on the group work done by:

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
__pycache__		__pycache__
.DS_Store		.DS_Store
CyclicBCGD.py		CyclicBCGD.py
DataPreparation.py		DataPreparation.py
GaussSouthwellBCGD.py		GaussSouthwellBCGD.py
GradientDescent.py		GradientDescent.py
README.md		README.md
RandomBCGD.py		RandomBCGD.py
Report.pdf		Report.pdf
citrus.csv		citrus.csv
common.py		common.py
config.yml		config.yml
main.ipynb		main.ipynb