mlsanityData to 7 Oct 2026

Closed-form geodesics and trust-region method to calculate Riemannian logarithms on Stiefel and its quotient manifolds

arXiv 2103.13327 ↗First posted 12 Mar 2021math.DG math.AG math.OCcited by 7

Not on the map. Why?

Abstract (arXiv)

We provide two closed-form geodesic formulas for a family of metrics on Stiefel manifold, parameterized by two positive numbers, having both the embedded and canonical metrics as special cases. The closed-form formulas allow us to compute geodesics by matrix exponential in reduced dimension for low-rank manifolds. Combining with the use of Fr'echet derivatives to compute the gradient of the square Frobenius distance between a geodesic ending point to a given point on the manifold, we show the logarithm map and geodesic distance between two endpoints on the manifold could be computed by it minimizing this square distance by a it trust-region solver. This leads to a new framework to compute the geodesic distance for manifolds with known geodesic formula but no closed-form logarithm map. We show the approach works well for Stiefel as well as flag manifolds. The logarithm map could be used to compute the Riemannian center of mass for these manifolds equipped with the above metrics. We also deduce simple trigonometric formulas for the Riemannian exponential and logarithm maps on the Grassmann manifold.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 5 of its 32 references that are in the map. First posted on arXiv 12 Mar 2021.

Builds on

5 of 32 references.

References · 5

  1. 2018geomstats: a Python Package for Riemannian Geometry in Machine Learningcited by 71
  2. 2019SciPy 1.0--Fundamental Algorithms for Scientific Computing in Pythoncited by 49
  3. 2019Optimization on flag manifoldscited by 13
  4. 2020Operator-valued formulas for Riemannian Gradient and Hessian and families of tractable metricscited by 3
  5. 2018Nonlinearly Preconditioned L-BFGS as an Acceleration Mechanism for Alternating Least Squares, with Application to Tensor Decompositioncited by 2

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 3.

7 papers.