東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Derivative-free and blackbox optimiz...

Audet, Charles.

Linked to FindBook

Google Book

Amazon

博客來

Derivative-free and blackbox optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Derivative-free and blackbox optimization/ by Charles Audet, Warren Hare.
Author:	Audet, Charles.
other author:	Hare, Warren.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xviii, 302 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Mathematical optimization. -
Online resource:	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)