Optimization Algorithms on Matrix Manifolds

Name: Optimization Algorithms on Matrix Manifolds
Brand: PRINCETON UNIVRSITY PRESS
SKU: 346263
Price: 108.13 EUR
Availability: InStock

P.A. ABSIL

Cena za kos z DDV. Dostava se obračuna ob zaključku nakupa.

108,13 €

Znižanje Razprodano

Različice izdelka

108,13 €

Na zalogi

Na zalogi v poslovalnicah takoj ali preko spletnega naročila

Preveri zalogo v knjigarnah

Status dobavljivosti: Na zalogi pri dobavitelju

Podrobnosti o izdelku

Poglej vse

ISBN

9780691132983

Mladinska knjiga ID

346263

Leto izida

2008

Velikost (šxdxv)

150 × 200 × 10

Status dobavljivosti

Na zalogi pri dobavitelju

Jezik

ANG

Založnik

PRINCETON UNIVRSITY PRESS

Avtor

P.A. ABSIL

Opis

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.