Sequential subspace methods on Stiefel manifold optimization problems

We study the minimization of a quadratic over Stiefel manifolds (the set of all orthogonal \(r\)-frames in \IR^n), which has applications in high-dimensional semi-supervised classification tasks. To reduce the computational complexity, sequential subspace methods(SSM) are employed to convert the hig...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	arXiv.org 2024-04
Hauptverfasser:	Chen, Pengwen, Chung-Kuan, Cheng, Holtz, Chester
Format:	Artikel
Sprache:	eng
Schlagworte:	Convergence Critical point Eigenvalues Eigenvectors Manifolds (mathematics) Mathematical analysis Optimization Subspace methods Subspaces Task complexity Upper bounds
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!