Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation

ArXi:2605.05446v1 Announce Type: cross Nonconvex methods have emerged as a dominant approach for low-rank matrix estimation, a problem that arises widely in machine learning and AI for learning and representing high-dimensional data. Existing analyses for these methods often require additional regularization to mitigate nonconvexity, even though such regularization is often unnecessary in practice. Moreover, most analyses rely on problem-specific arguments that are difficult to generalize to complex settings.