First-Order Methods In Optimization Paperback / softback
by Amir Beck
Part of the MOS-SIAM Series on Optimization series
Paperback / softback
Description
The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-order methods that are frequently used in solving large-scale problems.
First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration.
With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage. The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books. First-Order Methods in Optimization offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods.
Information
-
Out of stock
- Format:Paperback / softback
- Pages:484 pages
- Publisher:Society for Industrial & Applied Mathematics,U.S.
- Publication Date:30/11/2017
- Category:
- ISBN:9781611974980
Information
-
Out of stock
- Format:Paperback / softback
- Pages:484 pages
- Publisher:Society for Industrial & Applied Mathematics,U.S.
- Publication Date:30/11/2017
- Category:
- ISBN:9781611974980
