Convex Formulation

Convex formulations are increasingly used to solve complex optimization problems across diverse scientific and engineering domains. Current research focuses on developing efficient algorithms for solving these formulations, particularly within robotics (e.g., for contact modeling and trajectory optimization) and machine learning (e.g., for robust model verification and skill reproduction). This approach offers advantages such as guaranteed convergence and the ability to leverage established optimization solvers, leading to improved robustness and computational efficiency in various applications. The resulting advancements are impacting fields ranging from space robotics to the development of more reliable machine learning models.