Boolean Algebraic Manipulation

Boolean algebraic manipulation focuses on efficiently optimizing and interpreting Boolean functions, crucial for applications ranging from logic synthesis in electronic design automation to interpretable machine learning. Current research emphasizes developing novel algorithms, such as proximal gradient descent and graph neural network-based approaches, to address the computational complexity inherent in manipulating Boolean matrices and formulas. These advancements aim to improve the scalability and interpretability of Boolean methods, leading to more efficient hardware designs and more transparent machine learning models.