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

Rohit Kannan; Harsha Nagarajan; Deepjyoti Deka

doi:10.1287/ijoc.2023.0424.cd

August 4, 2025INFORMS journal on computing

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

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Key Points

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.

Implication

Repository provides tools for accelerating global optimization in nonconvex QCQPs using machine learning techniques.

Abstract

The software and data in this repository are a snapshot of the software and data that were used in the research reported in the paper by R. Kannan, H. Nagarajan, and D. Deka.

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Cite This Study

Kannan et al. (2025) studied this question.

synapsesocial.com/papers/68c1b18b54b1d3bfb60e879c https://doi.org/10.1287/ijoc.2023.0424.cd

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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