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Adaptive cross approximation (ACA) algorithms for symmetric positive semi-definite (SPSD) matrices.

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ACA-SPSD

In this repository we provide the implementation of the project "Volume maximization for cross approximation" of the class Low-Rank Approximation Techiques, MATH-403
Authors: Fabio Matti, Anna Paulish

Instructions

You can reproduce our results with

git clone https://github.com/FMatti/ACA-SPSD.git
cd ACA-SPSD
python main.py

This takes about 5 minutes. If dependency problems arise, you can image our Python environment using

python -m venv .venv

source .venv/bin/activate   # on Linux, macOS
.venv\Scripts\activate.bat  # on Windows

pip install --upgrade pip
pip install -r requirements.txt

Our implementations require a Python version $\geq$ 3.8.

File structure

Our implementations are located in the src/ directory. Our results can be found in the Jupyter notebook main.ipynb or equivalently reproduced by running the Python script main.py.

ACA-SPSD
│   README.md
|   main.ipynb             (Jupyter notebook with our results)
|   main.py                (equivalent Python script with our results)
|
└───src
|   |   algorithms.py      (implementations of the two algorithms)
|   |   helpers.py         (helper functions)
|   |   matrices.py        (definition of the example matrices)

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Adaptive cross approximation (ACA) algorithms for symmetric positive semi-definite (SPSD) matrices.

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