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HALLaR: Hybrid Augmented Lagrangian Low-Rank Algorithm

Overview

The Hybrid Augmented Lagrangian Low-Rank (HALLaR) algorithm [1] by Monteiro, R. D., Sujanani, A. and Cifuentes, D. (2024) is designed to solve large-scale semidefinite programming (SDP) problems efficiently. This repository contains the implementation of the HALLaR algorithm in Python, along with supporting scripts and sample problems to demonstrate its functionality.

Algorithm Description

The HALLaR algorithm is an inexact augmented Lagrangian method that generates sequences of matrices and Lagrange multipliers through iterative recursions. It leverages a Hybrid Low-Rank (HLR) approach to efficiently solve the augmented Lagrangian subproblems by restricting the solution space to low-rank matrices, significantly reducing the computational complexity. Here, HLR is not yet fully implemented, but instead the minimize functions from scipy.optimize is used.

Repository Structure

The repository is organized as follows:

  • main.py: Implements the HALLaR algorithm.
  • HLR.py: Contains the shell for the HLR algorithm and supporting functions.
  • MSS_SDP.py: Defines functions for solving the Maximum Stable Set sample problem.
  • utils.py: Provides supporting utility functions.
  • create_graphs.py: A script to create sample graphs for testing.
  • requirements.txt: Contains the required dependencies.

Folders

  • graphs/: Contains sample graphs used for testing and evaluation.
  • plots/: Stores plotted convergence results.
  • visualisations/: Includes additional visualizations of the algorithm's performance.

Usage

To use the HALLaR algorithm, follow these steps:

  1. Clone the repository:

    git clone https://github.com/edannas/HALLaR.git
cd HALLaR

  2. Install required dependencies: Make sure you have all necessary Python packages installed. You can use pip to install the required packages:

    pip install -r requirements.txt

  3. Create sample graphs: Use the create_graphs.py script to generate sample graphs:

    python create_graphs.py

  4. Run the algorithm: Execute main.py to run the HALLaR algorithm on the sample problems:

    python main.py

  5. View results: Plots and visualizations will be saved in the plots/ and visualisations/ folders, respectively.

Contributing

We welcome contributions to enhance the algorithm and its implementation. If you have suggestions or improvements, please feel free to create a pull request. For major changes, please open an issue first to discuss what you would like to change.

References

Monteiro, R. D., Sujanani, A., & Cifuentes, D. (2024). A low-rank augmented Lagrangian method for large-scale semidefinite programming based on a hybrid convex-nonconvex approach. arXiv preprint arXiv:2401.12490.