Rank-constrained Optimization: A Riemannian Manifold Approach

This paper presents an algorithm that solves optimization problems on a matrix manifold M ⊆ R with an additional rank inequality constraint. New geometric objects are defined to facilitate efficiently finding a suitable rank. The convergence properties of the algorithm are given and a weighted low-rank approximation problem is used to illustrate the efficiency and effectiveness of the algorithm.