Riemannian Optimization and Its Applications
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Beschreibung
Details the Riemannian conjugate gradient method so that the reader can make light work of implementing the algorithm An accessible journey from unconstrained optimization in Euclidean space to Riemannian optimization theory Covers applications in control, statistics, and numerical linear algebra that can be extended to the reader’s interests
Autor*in
Hiroyuki Sato
Themen in »Riemannian Optimization and Its Applications«
Riemannian Optimization Optimization on Manifolds Conjugate Gradient Method Singular Value Decomposition Optimal Model Reduction Canonical Correlation Analysis matrix theory
Stimmen zu »Riemannian Optimization and Its Applications«
“The author successfully presents all of this varied material using a consistent and modern notation. … The book meticulously provides references with a comprehensive list at the end. It includes information about software libraries that implement Riemannian optimization in MATLAB, Python, R, C++, and Julia. Both the proofs and calculations in the examples are given with sufficient detail using a consistent notation.” (Anders Linnér, Mathematical Reviews, October, 2022)
“The book is a very nice introductory reference for students, engineers, and practitioners to get started in the field of Riemannian optimization. … A highlight of the book is that it reviews the most important work in the field and also mentions current research topics. Thus, I also highly recommended it to researchers getting a broad overview of what is currently studied in the field, without being too detailed or theoretical.” (Lena Sembach, SIAM Review, Vol. 64 (2), June, 2022)
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