Second Order Cone

Second-order cone programming (SOCP) is a type of convex optimization problem involving conic constraints, offering efficient and robust solutions for a range of complex problems. Current research focuses on applying SOCP to diverse fields, including robotics (e.g., legged robot control and simultaneous localization and mapping), machine learning (e.g., neural network activation functions and ensemble selection), and operations research (e.g., equitable workload distribution). The ability of SOCP to handle uncertainty and constraints makes it a powerful tool for designing efficient and reliable algorithms across various scientific and engineering disciplines.