AI RESEARCH
Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
arXiv CS.LG
•
ArXi:2604.04130v1 Announce Type: cross Recent advancements in data science have significantly elevated the importance of orthogonally constrained optimization problems. The Riemannian approach has become a popular technique for addressing these problems due to the advantageous computational and analytical properties of the Stiefel manifold. Nonetheless, the interplay of nonsmoothness alongside orthogonality constraints