Conic Optimization

Conic optimization focuses on solving optimization problems where the feasible region is defined by conic constraints, encompassing a wide range of applications from control systems to machine learning. Current research emphasizes developing efficient algorithms, particularly first-order methods like conic descent and its variants, and leveraging machine learning techniques to improve solver speed and accuracy, including dual Lagrangian learning approaches. These advancements are crucial for deploying conic optimization in resource-constrained environments (e.g., embedded systems) and for tackling large-scale problems in various fields, leading to improved performance and scalability in diverse applications.