Numerical Optimization

Numerical optimization focuses on finding the best solution to complex problems by iteratively improving an initial guess. Current research emphasizes enhancing existing algorithms like differential evolution and branch and bound through techniques such as adaptive backtracking and machine learning integration, as well as exploring novel approaches using graph neural networks and surrogate models to improve efficiency and scalability, particularly for black-box optimization problems. These advancements are crucial for tackling increasingly complex challenges across diverse fields, from autonomous driving and robotics to materials science and machine learning model compression, where efficient and effective optimization is paramount.