Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization

Matrix eigen-decomposition is a classic and long-standing problem that plays a fundamental role in scientific computing and machine learning. Despite some existing algorithms for this inherently non-convex problem, the study remains inadequate for the need of large data nowadays. To address this gap, we propose a Doubly Stochastic Riemannian Gradient EIGenSolver, DSRG-EIGS, where the double stochasticity comes from the generalization of the stochastic Euclidean gradient ascent and the stochastic Euclidean coordinate ascent to Riemannian manifolds. As a result, it induces a greatly reduced complexity per iteration, enables the algorithm to completely avoid the matrix inversion, and consequently makes it well-suited to large-scale applications. We theoretically analyze its convergence properties and empirically validate it on real-world datasets. Encouraging experimental results demonstrate its advantages over the deterministic counterpart.