Quadratic Program

Quadratic programming (QP) focuses on efficiently solving optimization problems where the objective function is quadratic and constraints are linear. Current research emphasizes developing faster and more accurate QP solvers, particularly for large-scale problems arising in robotics, control systems, and machine learning, exploring techniques like graph neural networks and ADMM-based methods for improved efficiency and scalability. These advancements are crucial for real-time applications requiring rapid solutions to complex optimization problems, impacting fields ranging from autonomous driving to power grid management. Furthermore, research is actively exploring ways to integrate QP solvers seamlessly into larger machine learning pipelines, creating differentiable QP layers for end-to-end training.