語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Linear and nonlinear optimization
~
Thapa, Mukund N.
Linear and nonlinear optimization
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Linear and nonlinear optimization/ by Richard W. Cottle, Mukund N. Thapa.
作者:
Cottle, Richard W.
其他作者:
Thapa, Mukund N.
出版者:
New York, NY :Springer New York : : 2017.,
面頁冊數:
xxxi, 614 p. :ill., digital ; : 24 cm.;
Contained By:
Springer eBooks
標題:
Business. -
電子資源:
http://dx.doi.org/10.1007/978-1-4939-7055-1
ISBN:
9781493970551
Linear and nonlinear optimization
Cottle, Richard W.
Linear and nonlinear optimization
[electronic resource] /by Richard W. Cottle, Mukund N. Thapa. - New York, NY :Springer New York :2017. - xxxi, 614 p. :ill., digital ;24 cm. - International series in operations research & management science,v.2530884-8289 ;. - International series in operations research & management science ;106..
Chapter 1. LP Models and Applications -- Chapter 2. Linear Equations and Inequalities -- Chapter 3. The Simplex Algorithm -- Chapter 4. The Simplex Algorithm Continued -- Chapter 5. Duality and the Dual Simplex Algorithm -- Chapter 6. Postoptimality Analysis -- Chapter 7. Some Computational Considerations -- Chapter 8. NLP Models and Applications -- Chapter 9. Unconstrained Optimization -- Chapter 10. Descent Methods -- Chapter 11. Optimality Conditions -- Chapter 12. Problems with Linear Constraints -- Chapter 13. Problems with Nonlinear Constraints -- Chapter 14. Interior-Point Methods.
This textbook on Linear and Nonlinear Optimization is intended for graduate and advanced undergraduate students in operations research and related fields. It is both literate and mathematically strong, yet requires no prior course in optimization. As suggested by its title, the book is divided into two parts covering in their individual chapters LP Models and Applications; Linear Equations and Inequalities; The Simplex Algorithm; Simplex Algorithm Continued; Duality and the Dual Simplex Algorithm; Postoptimality Analyses; Computational Considerations; Nonlinear (NLP) Models and Applications; Unconstrained Optimization; Descent Methods; Optimality Conditions; Problems with Linear Constraints; Problems with Nonlinear Constraints; Interior-Point Methods; and an Appendix covering Mathematical Concepts. Each chapter ends with a set of exercises. The book is based on lecture notes the authors have used in numerous optimization courses the authors have taught at Stanford University. It emphasizes modeling and numerical algorithms for optimization with continuous (not integer) variables. The discussion presents the underlying theory without always focusing on formal mathematical proofs (which can be found in cited references) Another feature of this book is its inclusion of cultural and historical matters, most often appearing among the footnotes. "This book is a real gem. The authors do a masterful job of rigorously presenting all of the relevant theory clearly and concisely while managing to avoid unnecessary tedious mathematical details. This is an ideal book for teaching a one or two semester masters-level course in optimization - it broadly covers linear and nonlinear programming effectively balancing modeling, algorithmic theory, computation, implementation, illuminating historical facts, and numerous interesting examples and exercises. Due to the clarity of the exposition, this book also serves as a valuable reference for self-study." Professor Ilan Adler, IEOR Department, UC Berkeley "A carefully crafted introduction to the main elements and applications of mathematical optimization. This volume presents the essential concepts of linear and nonlinear programming in an accessible format filled with anecdotes, examples, and exercises that bring the topic to life. The authors plumb their decades of experience in optimization to provide an enriching layer of historical context. Suitable for advanced undergraduates and masters students in management science, operations research, and related fields." Michael P. Friedlander, IBM Professor of Computer Science, Professor of Mathematics, University of British Columbia.
ISBN: 9781493970551
Standard No.: 10.1007/978-1-4939-7055-1doiSubjects--Topical Terms:
558617
Business.
LC Class. No.: HD30.23 / .C688 2017
Dewey Class. No.: 515
Linear and nonlinear optimization
LDR
:04331nam a2200337 a 4500
001
905518
003
DE-He213
005
20170611101746.0
006
m d
007
cr nn 008maaau
008
190308s2017 nyu s 0 eng d
020
$a
9781493970551
$q
(electronic bk.)
020
$a
9781493970537
$q
(paper)
024
7
$a
10.1007/978-1-4939-7055-1
$2
doi
035
$a
978-1-4939-7055-1
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
HD30.23
$b
.C688 2017
072
7
$a
KJT
$2
bicssc
072
7
$a
KJMD
$2
bicssc
072
7
$a
BUS049000
$2
bisacsh
082
0 4
$a
515
$2
23
090
$a
HD30.23
$b
.C849 2017
100
1
$a
Cottle, Richard W.
$3
1172714
245
1 0
$a
Linear and nonlinear optimization
$h
[electronic resource] /
$c
by Richard W. Cottle, Mukund N. Thapa.
260
$a
New York, NY :
$b
Springer New York :
$b
Imprint: Springer,
$c
2017.
300
$a
xxxi, 614 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
International series in operations research & management science,
$x
0884-8289 ;
$v
v.253
505
0
$a
Chapter 1. LP Models and Applications -- Chapter 2. Linear Equations and Inequalities -- Chapter 3. The Simplex Algorithm -- Chapter 4. The Simplex Algorithm Continued -- Chapter 5. Duality and the Dual Simplex Algorithm -- Chapter 6. Postoptimality Analysis -- Chapter 7. Some Computational Considerations -- Chapter 8. NLP Models and Applications -- Chapter 9. Unconstrained Optimization -- Chapter 10. Descent Methods -- Chapter 11. Optimality Conditions -- Chapter 12. Problems with Linear Constraints -- Chapter 13. Problems with Nonlinear Constraints -- Chapter 14. Interior-Point Methods.
520
$a
This textbook on Linear and Nonlinear Optimization is intended for graduate and advanced undergraduate students in operations research and related fields. It is both literate and mathematically strong, yet requires no prior course in optimization. As suggested by its title, the book is divided into two parts covering in their individual chapters LP Models and Applications; Linear Equations and Inequalities; The Simplex Algorithm; Simplex Algorithm Continued; Duality and the Dual Simplex Algorithm; Postoptimality Analyses; Computational Considerations; Nonlinear (NLP) Models and Applications; Unconstrained Optimization; Descent Methods; Optimality Conditions; Problems with Linear Constraints; Problems with Nonlinear Constraints; Interior-Point Methods; and an Appendix covering Mathematical Concepts. Each chapter ends with a set of exercises. The book is based on lecture notes the authors have used in numerous optimization courses the authors have taught at Stanford University. It emphasizes modeling and numerical algorithms for optimization with continuous (not integer) variables. The discussion presents the underlying theory without always focusing on formal mathematical proofs (which can be found in cited references) Another feature of this book is its inclusion of cultural and historical matters, most often appearing among the footnotes. "This book is a real gem. The authors do a masterful job of rigorously presenting all of the relevant theory clearly and concisely while managing to avoid unnecessary tedious mathematical details. This is an ideal book for teaching a one or two semester masters-level course in optimization - it broadly covers linear and nonlinear programming effectively balancing modeling, algorithmic theory, computation, implementation, illuminating historical facts, and numerous interesting examples and exercises. Due to the clarity of the exposition, this book also serves as a valuable reference for self-study." Professor Ilan Adler, IEOR Department, UC Berkeley "A carefully crafted introduction to the main elements and applications of mathematical optimization. This volume presents the essential concepts of linear and nonlinear programming in an accessible format filled with anecdotes, examples, and exercises that bring the topic to life. The authors plumb their decades of experience in optimization to provide an enriching layer of historical context. Suitable for advanced undergraduates and masters students in management science, operations research, and related fields." Michael P. Friedlander, IBM Professor of Computer Science, Professor of Mathematics, University of British Columbia.
650
0
$a
Business.
$3
558617
650
0
$a
Operations research.
$3
573517
650
0
$a
Decision making.
$3
528319
650
0
$a
Mathematical models.
$3
527886
650
0
$a
Mathematical optimization.
$3
527675
650
0
$a
Management science.
$3
719678
650
1 4
$a
Business and Management.
$2
eflch
$3
934826
650
2 4
$a
Operation Research/Decision Theory.
$3
881408
650
2 4
$a
Operations Research, Management Science.
$3
785065
650
2 4
$a
Optimization.
$3
669174
650
2 4
$a
Mathematical Modeling and Industrial Mathematics.
$3
669172
700
1
$a
Thapa, Mukund N.
$3
1172715
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer eBooks
830
0
$a
International series in operations research & management science ;
$v
106.
$3
744236
856
4 0
$u
http://dx.doi.org/10.1007/978-1-4939-7055-1
950
$a
Business and Management (Springer-41169)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入