語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Derivative-free and blackbox optimiz...
~
Audet, Charles.
FindBook
Google Book
Amazon
博客來
Derivative-free and blackbox optimization
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Derivative-free and blackbox optimization/ by Charles Audet, Warren Hare.
作者:
Audet, Charles.
其他作者:
Hare, Warren.
出版者:
Cham :Springer International Publishing : : 2017.,
面頁冊數:
xviii, 302 p. :ill., digital ;24 cm.
內容註:
Part I: Introduction and Background Material -- Introduction: Tools and Challenges -- Mathematical Background -- The Beginnings of DFO Algorithms -- Part I: Some Remarks on DFO -- Part II: Popular Heuristic Methods -- Genetic Algorithms -- Nelder-Mead -- Part II: Further Remarks on Heuristics -- Part III: Direct Search Methods -- Positive bases and Nonsmooth Optimization -- Generalized Pattern Search -- Mesh Adaptive Direct Search -- Part III: Further Remarks on Direct Search Methods -- Part IV: Model-based Methods -- Model-based Descent -- Model-based Trust Region -- Part IV: Further Remarks on Model-based Methods -- Part V: Extensions and Refinements -- Variables and Constraints -- Optimization Using Surrogates and Models -- Biobjective Optimization -- Part V: Final Remarks on DFO/BBO -- Part VI: Appendix: Comparing Optimization Methods -- Solutions to Selected Exercises.
Contained By:
Springer eBooks
標題:
Mathematical optimization. -
電子資源:
http://dx.doi.org/10.1007/978-3-319-68913-5
ISBN:
9783319689135
Derivative-free and blackbox optimization
Audet, Charles.
Derivative-free and blackbox optimization
[electronic resource] /by Charles Audet, Warren Hare. - Cham :Springer International Publishing :2017. - xviii, 302 p. :ill., digital ;24 cm. - Springer series in operations research and financial engineering,1431-8598. - Springer series in operations research and financial engineering..
Part I: Introduction and Background Material -- Introduction: Tools and Challenges -- Mathematical Background -- The Beginnings of DFO Algorithms -- Part I: Some Remarks on DFO -- Part II: Popular Heuristic Methods -- Genetic Algorithms -- Nelder-Mead -- Part II: Further Remarks on Heuristics -- Part III: Direct Search Methods -- Positive bases and Nonsmooth Optimization -- Generalized Pattern Search -- Mesh Adaptive Direct Search -- Part III: Further Remarks on Direct Search Methods -- Part IV: Model-based Methods -- Model-based Descent -- Model-based Trust Region -- Part IV: Further Remarks on Model-based Methods -- Part V: Extensions and Refinements -- Variables and Constraints -- Optimization Using Surrogates and Models -- Biobjective Optimization -- Part V: Final Remarks on DFO/BBO -- Part VI: Appendix: Comparing Optimization Methods -- Solutions to Selected Exercises.
This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization. The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead) Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region) Part V discusses dealing with constraints, using surrogates, and bi-objective optimization. End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix.
ISBN: 9783319689135
Standard No.: 10.1007/978-3-319-68913-5doiSubjects--Topical Terms:
517763
Mathematical optimization.
LC Class. No.: QA402.5
Dewey Class. No.: 519.6
Derivative-free and blackbox optimization
LDR
:02849nmm a2200325 a 4500
001
2112850
003
DE-He213
005
20171203021019.0
006
m d
007
cr nn 008maaau
008
180719s2017 gw s 0 eng d
020
$a
9783319689135
$q
(electronic bk.)
020
$a
9783319689128
$q
(paper)
024
7
$a
10.1007/978-3-319-68913-5
$2
doi
035
$a
978-3-319-68913-5
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA402.5
072
7
$a
PBU
$2
bicssc
072
7
$a
MAT003000
$2
bisacsh
082
0 4
$a
519.6
$2
23
090
$a
QA402.5
$b
.A899 2017
100
1
$a
Audet, Charles.
$3
756827
245
1 0
$a
Derivative-free and blackbox optimization
$h
[electronic resource] /
$c
by Charles Audet, Warren Hare.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2017.
300
$a
xviii, 302 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Springer series in operations research and financial engineering,
$x
1431-8598
505
0
$a
Part I: Introduction and Background Material -- Introduction: Tools and Challenges -- Mathematical Background -- The Beginnings of DFO Algorithms -- Part I: Some Remarks on DFO -- Part II: Popular Heuristic Methods -- Genetic Algorithms -- Nelder-Mead -- Part II: Further Remarks on Heuristics -- Part III: Direct Search Methods -- Positive bases and Nonsmooth Optimization -- Generalized Pattern Search -- Mesh Adaptive Direct Search -- Part III: Further Remarks on Direct Search Methods -- Part IV: Model-based Methods -- Model-based Descent -- Model-based Trust Region -- Part IV: Further Remarks on Model-based Methods -- Part V: Extensions and Refinements -- Variables and Constraints -- Optimization Using Surrogates and Models -- Biobjective Optimization -- Part V: Final Remarks on DFO/BBO -- Part VI: Appendix: Comparing Optimization Methods -- Solutions to Selected Exercises.
520
$a
This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization. The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead) Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region) Part V discusses dealing with constraints, using surrogates, and bi-objective optimization. End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix.
650
0
$a
Mathematical optimization.
$3
517763
650
1 4
$a
Mathematics.
$3
515831
650
2 4
$a
Optimization.
$3
891104
650
2 4
$a
Numerical Analysis.
$3
892626
700
1
$a
Hare, Warren.
$3
3270910
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer eBooks
830
0
$a
Springer series in operations research and financial engineering.
$3
1619945
856
4 0
$u
http://dx.doi.org/10.1007/978-3-319-68913-5
950
$a
Mathematics and Statistics (Springer-11649)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9325123
電子資源
11.線上閱覽_V
電子書
EB QA402.5
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入