Problem Decomposition

Problem decomposition involves breaking down complex tasks into smaller, more manageable subproblems to improve efficiency and scalability in various domains, from planning and optimization to large language model reasoning. Current research focuses on developing effective decomposition strategies, including techniques like rolling horizon optimization and recursive grouping, and exploring modular architectures such as mixtures-of-experts to handle subproblems independently. These advancements are improving the performance and generalization capabilities of algorithms across diverse applications, ranging from logistics and scheduling to solving high-dimensional scientific equations, particularly where computational resources are limited.