Divide and Conquer

Divide-and-conquer is a computational strategy that tackles complex problems by recursively breaking them into smaller, more manageable subproblems, solving these independently, and then combining the solutions. Current research focuses on applying this approach to diverse fields, including large-scale optimization, video understanding, code generation, and machine learning model training and deployment, often employing neural networks, graph neural networks, or large language models to handle the subproblems and their integration. This strategy offers significant advantages in terms of scalability, efficiency, and robustness, particularly for problems exceeding the capacity of single-stage methods, impacting various applications from AI-driven decision-making to resource-constrained hardware implementations.