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This brief describes the basics of Riemannian
optimization-optimization on Riemannian manifolds-introduces
algorithms for Riemannian optimization problems, discusses the
theoretical properties of these algorithms, and suggests possible
applications of Riemannian optimization to problems in other
fields. To provide the reader with a smooth introduction to
Riemannian optimization, brief reviews of mathematical optimization
in Euclidean spaces and Riemannian geometry are included.
Riemannian optimization is then introduced by merging these
concepts. In particular, the Euclidean and Riemannian conjugate
gradient methods are discussed in detail. A brief review of recent
developments in Riemannian optimization is also provided.
Riemannian optimization methods are applicable to many problems in
various fields. This brief discusses some important applications
including the eigenvalue and singular value decompositions in
numerical linear algebra, optimal model reduction in control
engineering, and canonical correlation analysis in statistics.
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