Decomposition Method

Decomposition methods break down complex problems into smaller, more manageable sub-problems, facilitating efficient solution and improved interpretability. Current research focuses on developing novel algorithms, such as those integrating deep learning with traditional decomposition techniques for time series forecasting, or employing graph classification to optimize the selection of decomposition strategies for various optimization problems. These advancements enhance the scalability and accuracy of solutions across diverse fields, including optimization, machine learning, and signal processing, leading to improved model performance and reduced computational costs. The resulting insights are valuable for both theoretical understanding and practical applications.