Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It places careful emphasis on both the numerical formulation of the algorithm and its differential geometric abstraction--illustrating how good algorithms draw equally from the insights of differential geometry, optimization, and numerical analysis. Two more theoretical chapters provide readers with the background in differential geometry necessary to algorithmic development. In the other chapters, several well-known optimization methods such as steepest descent and conjugate gradients are generalized to abstract manifolds. The book provides a generic development of each of these methods, building upon the material of the geometric chapters. It then guides readers through the calculations that turn these geometrically formulated methods into concrete numerical algorithms. The state-of-the-art algorithms given as examples are competitive with the best existing algorithms for a selection of eigenspace problems in numerical linear algebra.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
P.-A. Absil is associate professor of mathematical engineering at the Université Catholique de Louvain in Belgium. R. Mahony is reader in engineering at the Australian National University. R. Sepulchre is professor of electrical engineering and computer science at the University of Liège in Belgium.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : medimops, Berlin, Allemagne
Etat : good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present. N° de réf. du vendeur M00691132984-G
Quantité disponible : 1 disponible(s)
Vendeur : Goodwill of Silicon Valley, SAN JOSE, CA, Etats-Unis
Etat : very_good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in very good condition! The cover and any other included accessories are also in very good condition showing some minor use. The spine is straight, there are no rips tears or creases on the cover or the pages. N° de réf. du vendeur GWSVV.0691132984.VG
Quantité disponible : 1 disponible(s)
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur WP-9780691132983
Quantité disponible : 7 disponible(s)
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Etat : New. N° de réf. du vendeur 5017995-n
Quantité disponible : Plus de 20 disponibles
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 5017995
Quantité disponible : Plus de 20 disponibles
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
Hardback. Etat : New. Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It places careful emphasis on both the numerical formulation of the algorithm and its differential geometric abstraction--illustrating how good algorithms draw equally from the insights of differential geometry, optimization, and numerical analysis. Two more theoretical chapters provide readers with the background in differential geometry necessary to algorithmic development. In the other chapters, several well-known optimization methods such as steepest descent and conjugate gradients are generalized to abstract manifolds. The book provides a generic development of each of these methods, building upon the material of the geometric chapters. It then guides readers through the calculations that turn these geometrically formulated methods into concrete numerical algorithms.The state-of-the-art algorithms given as examples are competitive with the best existing algorithms for a selection of eigenspace problems in numerical linear algebra. Optimization Algorithms on Matrix Manifolds offers techniques with broad applications in linear algebra, signal processing, data mining, computer vision, and statistical analysis. It can serve as a graduate-level textbook and will be of interest to applied mathematicians, engineers, and computer scientists. N° de réf. du vendeur LU-9780691132983
Quantité disponible : 4 disponible(s)
Vendeur : Brook Bookstore On Demand, Napoli, NA, Italie
Etat : new. N° de réf. du vendeur 796b05c55ff5d3e7b75debf08a15fcb7
Quantité disponible : 7 disponible(s)
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Etat : New. N° de réf. du vendeur 5017995-n
Quantité disponible : Plus de 20 disponibles
Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlande
Etat : New. 2007. Hardcover. Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It is of interest to applied mathematicians, and computer scientists. Num Pages: 240 pages, 24 line illus. 3 tables. BIC Classification: PBW; TBC; UY. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 242 x 162 x 18. Weight in Grams: 462. . . . . . N° de réf. du vendeur V9780691132983
Quantité disponible : 1 disponible(s)
Vendeur : Rarewaves USA, OSWEGO, IL, Etats-Unis
Hardback. Etat : New. Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It places careful emphasis on both the numerical formulation of the algorithm and its differential geometric abstraction--illustrating how good algorithms draw equally from the insights of differential geometry, optimization, and numerical analysis. Two more theoretical chapters provide readers with the background in differential geometry necessary to algorithmic development. In the other chapters, several well-known optimization methods such as steepest descent and conjugate gradients are generalized to abstract manifolds. The book provides a generic development of each of these methods, building upon the material of the geometric chapters. It then guides readers through the calculations that turn these geometrically formulated methods into concrete numerical algorithms.The state-of-the-art algorithms given as examples are competitive with the best existing algorithms for a selection of eigenspace problems in numerical linear algebra. Optimization Algorithms on Matrix Manifolds offers techniques with broad applications in linear algebra, signal processing, data mining, computer vision, and statistical analysis. It can serve as a graduate-level textbook and will be of interest to applied mathematicians, engineers, and computer scientists. N° de réf. du vendeur LU-9780691132983
Quantité disponible : Plus de 20 disponibles