Code and Data Repository for Strong Partitioning and a Machine Learning Approximation for Accelerating the Global Optimization of Nonconvex QCQPs

Puntos clave

• This repository supports the global optimization of nonconvex QCQPs using innovative machine learning methods.
• Tools in the repository aim to enhance problem-solving efficiency for complex nonconvex optimization tasks.
• The dataset and software included facilitate the application of strong partitioning strategies in optimization.
• Data from the research by Kannan, Nagarajan, and Deka offers a foundation for future studies on this optimization challenge.