國立虎尾科技大學 |

Optimization on Solution Sets of Common Fixed Point Problems

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Optimization on Solution Sets of Common Fixed Point Problems/ by Alexander J. Zaslavski.
作者:	Zaslavski, Alexander J.
面頁冊數:	XI, 434 p.online resource. :
Contained By:	Springer Nature eBook
標題:	Numerical Analysis. -
電子資源:	https://doi.org/10.1007/978-3-030-78849-0
ISBN:	9783030788490

Optimization on Solution Sets of Common Fixed Point Problems
Zaslavski, Alexander J.

Optimization on Solution Sets of Common Fixed Point Problems[electronic resource] /by Alexander J. Zaslavski. - 1st ed. 2021. - XI, 434 p.online resource. - Springer Optimization and Its Applications,1781931-6836 ;. - Springer Optimization and Its Applications,104.

Preface -- Introduction -- Fixed Point Subgradient Algorithm -- Proximal Point Subgradient Algorithm -- Cimmino Subgradient Projection Algorithm -- Iterative Subgradient Projection Algorithm -- Dynamic Strong-Averaging Subgradient Algorithm -- Fixed Point Gradient Projection Algorithm -- Cimmino Gradient Projection Algorithm -- A Class of Nonsmooth Convex Optimization Problems -- Zero-Sum Games with Two Players -- References -- Index.

This book is devoted to a detailed study of the subgradient projection method and its variants for convex optimization problems over the solution sets of common fixed point problems and convex feasibility problems. These optimization problems are investigated to determine good solutions obtained by different versions of the subgradient projection algorithm in the presence of sufficiently small computational errors. The use of selected algorithms is highlighted including the Cimmino type subgradient, the iterative subgradient, and the dynamic string-averaging subgradient. All results presented are new. Optimization problems where the underlying constraints are the solution sets of other problems, frequently occur in applied mathematics. The reader should not miss the section in Chapter 1 which considers some examples arising in the real world applications. The problems discussed have an important impact in optimization theory as well. The book will be useful for researches interested in the optimization theory and its applications.

ISBN: 9783030788490

Standard No.: 10.1007/978-3-030-78849-0doiSubjects--Topical Terms:

671433
Numerical Analysis.

LC Class. No.: QA402.5-402.6

Dewey Class. No.: 519.6

Optimization on Solution Sets of Common Fixed Point Problems
LDR:02910nam a22004095i 4500 001 1053856
003 DE-He213
005 20210813121936.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030788490 $9 978-3-030-78849-0
024 7 $a 10.1007/978-3-030-78849-0 $2 doi
035 $a 978-3-030-78849-0
050 4 $a QA402.5-402.6
072 7 $a PBU $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBU $2 thema
082 0 4 $a 519.6 $2 23
100 1 $a Zaslavski, Alexander J. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1022503
245 1 0 $a Optimization on Solution Sets of Common Fixed Point Problems $h [electronic resource] / $c by Alexander J. Zaslavski.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XI, 434 p. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Optimization and Its Applications, $x 1931-6836 ; $v 178
505 0 $a Preface -- Introduction -- Fixed Point Subgradient Algorithm -- Proximal Point Subgradient Algorithm -- Cimmino Subgradient Projection Algorithm -- Iterative Subgradient Projection Algorithm -- Dynamic Strong-Averaging Subgradient Algorithm -- Fixed Point Gradient Projection Algorithm -- Cimmino Gradient Projection Algorithm -- A Class of Nonsmooth Convex Optimization Problems -- Zero-Sum Games with Two Players -- References -- Index.
520 $a This book is devoted to a detailed study of the subgradient projection method and its variants for convex optimization problems over the solution sets of common fixed point problems and convex feasibility problems. These optimization problems are investigated to determine good solutions obtained by different versions of the subgradient projection algorithm in the presence of sufficiently small computational errors. The use of selected algorithms is highlighted including the Cimmino type subgradient, the iterative subgradient, and the dynamic string-averaging subgradient. All results presented are new. Optimization problems where the underlying constraints are the solution sets of other problems, frequently occur in applied mathematics. The reader should not miss the section in Chapter 1 which considers some examples arising in the real world applications. The problems discussed have an important impact in optimization theory as well. The book will be useful for researches interested in the optimization theory and its applications.
650 2 4 $a Numerical Analysis. $3 671433
650 2 4 $a Operations Research, Management Science. $3 785065
650 1 4 $a Optimization. $3 669174
650 0 $a Numerical analysis. $3 527939
650 0 $a Management science. $3 719678
650 0 $a Operations research. $3 573517
650 0 $a Mathematical optimization. $3 527675
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030788483
776 0 8 $i Printed edition: $z 9783030788506
776 0 8 $i Printed edition: $z 9783030788513
830 0 $a Springer Optimization and Its Applications, $x 1931-6828 ; $v 104 $3 1255232
856 4 0 $u https://doi.org/10.1007/978-3-030-78849-0
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)