Quadratic Programming

Quadratic programming (QP) focuses on optimizing a quadratic objective function subject to linear or quadratic constraints, aiming to find the optimal solution efficiently. Current research emphasizes developing faster and more robust QP solvers, particularly for large-scale problems, employing techniques like neural networks (e.g., graph neural networks), hypergraph-based methods, and adaptive algorithms that adjust parameters based on problem context. These advancements are crucial for diverse applications, including robotics control, machine learning (e.g., adversarial attacks, support vector machines), and dynamic pricing, where real-time or near real-time solutions are essential.