Block Coordinate Descent on Smooth Manifolds: Convergence Theory and Twenty-One Examples

Abstract

Block coordinate descent is an optimization paradigm that iteratively updates one block of variables at a time, making it quite amenable to big data applications due to its scalability and performance. Its convergence behavior has been extensively studied in the (block-wise) convex case, but it is much less explored in the non-convex case. In this paper we analyze the convergence of block coordinate methods on non-convex sets and derive convergence rates on smooth manifolds under natural or weaker assumptions than prior work. Our analysis applies to many non-convex problems, including ones that seek low-dimensional structures (e.g., maximal coding rate reduction, neural collapse, reverse engineering adversarial attacks, generalized PCA, alternating projection); ones that seek combinatorial structures (homomorphic sensing, regression without correspondences, real phase retrieval, robust point matching); ones that seek geometric structures from visual data (e.g., essential matrix estimation, absolute pose estimation); and ones that seek inliers sparsely hidden in a large number of outliers (e.g., outlier-robust estimation via iteratively-reweighted least-squares). While our convergence theory applies to all these problems, yielding novel corollaries, it also applies to other, perhaps more familiar, problems (e.g., optimal transport, matrix factorization, Burer-Monteiro factorization), recovering previously known results.