High Curvature Means Low Rank: On the Sectional Curvature of Grassmann and Stiefel Manifolds and the Underlying Matrix Trace Inequalities
Ralf Zimmermann, Jakob Stoye · SIAM Journal on Matrix Analysis and Applications · 2025
Abstract. Methods and algorithms that work with data on nonlinear manifolds are collectively summarized under the term “Riemannian computing." In practice, curvature can be a key limiting factor for the performance of Riemannian computing methods. Yet curvature can also be a powerful tool in the theoretical analysis of Riemannian algorithms. In this work, we investigate the sectional curvature of the Stiefel and Grassmann manifold. On the Grassmannian, tight curvature bounds have been known since the late 1960s. On the Stiefel manifold under the canonical metric, it was believed that the sectional curvature does not exceed 5/4. Under the Euclidean metric, the maximum was conjectured to be at 1. For both manifolds, the sectional curvature is given by the Frobenius norm of certain structured commutator brackets of skew-symmetric matrices. We provide refined inequalities for such terms and pay special attention to the maximizers of the curvature bounds. In this way, we prove for the Stiefel manifold that the global bounds of 5/4 (canonical metric) and 1 (Euclidean metric) hold indeed. With this addition, a complete account of the curvature bounds in all admissible dimensions is obtained. We observe that “high curvature means low-rank"; more precisely, for the Stiefel and Grassmann manifolds under the canonical metric, the global curvature maximum is attained at tangent plane sections that are spanned by rank-two matrices, while the extreme curvature cases of the Euclidean Stiefel manifold occur for rank-one matrices. Numerical examples are included for illustration purposes. Reproducibility of computational results. This paper has been awarded the “SIAM Reproducibility Badge: code and data available” as a recognition that the authors have followed reproducibility principles valued by SIMAX and the scientific computing community. Code and data that allow readers to reproduce the results in this paper are available at https://github.com/RalfZimmermannSDU/StiefelCurvatureSIMAX and in the supplementary materials ( Curvature_supp.pdf [232KB], StiefelCurvatureSIMAX.zip [46.3KB]). [Formula: see text]