A Grassmann Manifold Handbook: Basic Geometry and Computational Aspects
Thomas Bendokat, Ralf Zimmermann, P.-A. Absil

TL;DR
This paper provides a comprehensive overview of the geometry and computational methods related to the Grassmann manifold, including new algorithms and formulas that facilitate its application in various scientific fields.
Contribution
It introduces a modified algorithm for the Riemannian logarithm map and derives new formulas for parallel transport, exponential map derivatives, and Jacobi fields on the Grassmann manifold.
Findings
Enhanced numerical stability in computing the Riemannian logarithm map.
Derived explicit formulas for parallel transport and exponential map derivatives.
Bridged different geometric representations of the Grassmann manifold.
Abstract
The Grassmann manifold of linear subspaces is important for the mathematical modelling of a multitude of applications, ranging from problems in machine learning, computer vision and image processing to low-rank matrix optimization problems, dynamic low-rank decompositions and model reduction. With this mostly expository work, we aim to provide a collection of the essential facts and formulae on the geometry of the Grassmann manifold in a fashion that is fit for tackling the aforementioned problems with matrix-based algorithms. Moreover, we expose the Grassmann geometry both from the approach of representing subspaces with orthogonal projectors and when viewed as a quotient space of the orthogonal group, where subspaces are identified as equivalence classes of (orthogonal) bases. This bridges the associated research tracks and allows for an easy transition between these two approaches.…
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Taxonomy
TopicsMatrix Theory and Algorithms · Sparse and Compressive Sensing Techniques · Advanced Optimization Algorithms Research
