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Cited by 2,685

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

The Geometry of Algorithms with Orthogonality Constraints

Edelman, Alan, Arias, Tomás A., Smith, Steven T.·1998·SIAM Journal on Matrix Analysis and Applications

Abstract

In this paper we develop new Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds. These manifolds represent the constraints that arise in such areas as the symmetric eigenvalue problem, nonlinear eigenvalue problems, electronic structures computations, and signal processing. In addition to the new algorithms, we show how the geometrical framework gives penetrating new insights allowing us to create, understand, and compare algorithms. The theory proposed here provides a taxonomy for numerical linear algebra algorithms that provide a top level mathematical view of previously unrelated algorithms. It is our hope that developers of new algorithms and perturbation theories will benefit from the theory, methods, and examples in this paper.

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Metadata

Authors
Edelman, A., Arias, T. A., & Smith, S. T.
Venue
SIAM Journal on Matrix Analysis and Applications
Published
1998-01
Volume
20
Issue
2
Pages
303-353
ISSN
  • 0895-4798 (ISSN-L)
  • 1095-7162
OpenAlex
W2045512849
Cited by
2,685
References
4
FWCI
24.61
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Geometry of Algorithms with Orthogonality Constraints. https://app.cochat.ai/papers/10.1137/s0895479895290954
The paper
Edelman, A., Arias, T. A., & Smith, S. T. (1998). The Geometry of Algorithms with Orthogonality Constraints. SIAM Journal on Matrix Analysis and Applications. https://doi.org/10.1137/s0895479895290954

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.