東華大學圖書館 |

語系: 繁體中文

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

Maximum-entropy sampling = algorithm...

Fampa, Marcia.

FindBook

Google Book

Amazon

博客來

Maximum-entropy sampling = algorithms and application /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Maximum-entropy sampling/ by Marcia Fampa, Jon Lee.
其他題名:	algorithms and application /
作者:	Fampa, Marcia.
其他作者:	Lee, Jon.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xvii, 195 p. :ill., digital ;24 cm.
內容註:	Overview -- Notation -- The problem and basic properties -- Branch-and-bound -- Upper bounds -- Environmental monitoring -- Opportunities -- Basic formulae and inequalities -- References -- Index.
Contained By:	Springer Nature eBook
標題:	Mathematical optimization. -
電子資源:	https://doi.org/10.1007/978-3-031-13078-6
ISBN:	9783031130786

Maximum-entropy sampling = algorithms and application /
Fampa, Marcia.

Maximum-entropy samplingalgorithms and application /[electronic resource] :by Marcia Fampa, Jon Lee. - Cham :Springer International Publishing :2022. - xvii, 195 p. :ill., digital ;24 cm. - Springer series in operations research and financial engineering,2197-1773. - Springer series in operations research and financial engineering..

Overview -- Notation -- The problem and basic properties -- Branch-and-bound -- Upper bounds -- Environmental monitoring -- Opportunities -- Basic formulae and inequalities -- References -- Index.

This monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), which is a fascinating topic at the intersection of mathematical optimization and data science. The text situates MESP in information theory, as the algorithmic problem of calculating a sub-vector of pre-specificed size from a multivariate Gaussian random vector, so as to maximize Shannon's differential entropy. The text collects and expands on state-of-the-art algorithms for MESP, and addresses its application in the field of environmental monitoring. While MESP is a central optimization problem in the theory of statistical designs (particularly in the area of spatial monitoring), this book largely focuses on the unique challenges of its algorithmic side. From the perspective of mathematical-optimization methodology, MESP is rather unique (a 0/1 nonlinear program having a nonseparable objective function), and the algorithmic techniques employed are highly non-standard. In particular, successful techniques come from several disparate areas within the field of mathematical optimization; for example: convex optimization and duality, semidefinite programming, Lagrangian relaxation, dynamic programming, approximation algorithms, 0/1 optimization (e.g., branch-and-bound), extended formulation, and many aspects of matrix theory. The book is mainly aimed at graduate students and researchers in mathematical optimization and data analytics.

ISBN: 9783031130786

Standard No.: 10.1007/978-3-031-13078-6doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5 / .F35 2022

Dewey Class. No.: 519.6

Maximum-entropy sampling = algorithms and application /
LDR:02730nmm a2200337 a 4500 001 2305084
003 DE-He213
005 20221029192806.0
006 m d
007 cr nn 008maaau
008 230409s2022 sz s 0 eng d
020 $a 9783031130786 $q (electronic bk.)
020 $a 9783031130779 $q (paper)
024 7 $a 10.1007/978-3-031-13078-6 $2 doi
035 $a 978-3-031-13078-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5 $b .F35 2022
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
090 $a QA402.5 $b .F198 2022
100 1 $a Fampa, Marcia. $3 3607870
245 1 0 $a Maximum-entropy sampling $h [electronic resource] : $b algorithms and application / $c by Marcia Fampa, Jon Lee.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xvii, 195 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in operations research and financial engineering, $x 2197-1773
505 0 $a Overview -- Notation -- The problem and basic properties -- Branch-and-bound -- Upper bounds -- Environmental monitoring -- Opportunities -- Basic formulae and inequalities -- References -- Index.
520 $a This monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), which is a fascinating topic at the intersection of mathematical optimization and data science. The text situates MESP in information theory, as the algorithmic problem of calculating a sub-vector of pre-specificed size from a multivariate Gaussian random vector, so as to maximize Shannon's differential entropy. The text collects and expands on state-of-the-art algorithms for MESP, and addresses its application in the field of environmental monitoring. While MESP is a central optimization problem in the theory of statistical designs (particularly in the area of spatial monitoring), this book largely focuses on the unique challenges of its algorithmic side. From the perspective of mathematical-optimization methodology, MESP is rather unique (a 0/1 nonlinear program having a nonseparable objective function), and the algorithmic techniques employed are highly non-standard. In particular, successful techniques come from several disparate areas within the field of mathematical optimization; for example: convex optimization and duality, semidefinite programming, Lagrangian relaxation, dynamic programming, approximation algorithms, 0/1 optimization (e.g., branch-and-bound), extended formulation, and many aspects of matrix theory. The book is mainly aimed at graduate students and researchers in mathematical optimization and data analytics.
650 0 $a Mathematical optimization. $3 517763
650 0 $a Maximum entropy method. $3 551403
650 1 4 $a Optimization. $3 891104
650 2 4 $a Operations Research, Management Science. $3 1532996
700 1 $a Lee, Jon. $3 644215
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer series in operations research and financial engineering. $3 1619945
856 4 0 $u https://doi.org/10.1007/978-3-031-13078-6
950 $a Mathematics and Statistics (SpringerNature-11649)