Conic Constraint

Conic constraints, mathematical expressions defining regions within cones, are crucial in optimization problems across diverse fields, from robotics and control systems to machine learning and medical image analysis. Current research focuses on developing efficient algorithms for solving conic optimization problems, particularly for resource-constrained environments like embedded systems, and on incorporating conic constraints into models for ensuring safety and satisfying physical limitations. This work is driving advancements in areas such as safe robot control, improved medical image analysis (e.g., nuclear detection and classification), and more efficient optimization techniques for complex systems. The development of faster and more robust solvers for conic optimization problems has significant implications for the scalability and reliability of numerous applications.