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

An optimization primer

Royset, Johannes O.

FindBook

Google Book

Amazon

博客來

An optimization primer

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An optimization primer/ by Johannes O. Royset, Roger J.-B. Wets.
作者:	Royset, Johannes O.
其他作者:	Wets, Roger J.-B.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	1 online resource (xviii, 676 p.) :ill., digital ;24 cm.
內容註:	Prelude -- Convex optimization -- Optimization under uncertainty -- Minimization problems -- Perturbation and duality -- Without convexity or smoothness -- Generalized Equations -- Risk modeling and sample averages -- Games and minsup problems -- Decomposition.
Contained By:	Springer Nature eBook
標題:	Mathematical optimization. -
電子資源:	https://doi.org/10.1007/978-3-030-76275-9
ISBN:	9783030762759

An optimization primer
Royset, Johannes O.

An optimization primer[electronic resource] /by Johannes O. Royset, Roger J.-B. Wets. - Cham :Springer International Publishing :2021. - 1 online resource (xviii, 676 p.) :ill., digital ;24 cm. - Springer series in operations research and financial engineering,2197-1773. - Springer series in operations research and financial engineering..

Prelude -- Convex optimization -- Optimization under uncertainty -- Minimization problems -- Perturbation and duality -- Without convexity or smoothness -- Generalized Equations -- Risk modeling and sample averages -- Games and minsup problems -- Decomposition.

This richly illustrated book introduces the subject of optimization to a broad audience with a balanced treatment of theory, models and algorithms. Through numerous examples from statistical learning, operations research, engineering, finance and economics, the text explains how to formulate and justify models while accounting for real-world considerations such as data uncertainty. It goes beyond the classical topics of linear, nonlinear and convex programming and deals with nonconvex and nonsmooth problems as well as games, generalized equations and stochastic optimization. The book teaches theoretical aspects in the context of concrete problems, which makes it an accessible onramp to variational analysis, integral functions and approximation theory. More than 100 exercises and 200 fully developed examples illustrate the application of the concepts. Readers should have some foundation in differential calculus and linear algebra. Exposure to real analysis would be helpful but is not prerequisite.

ISBN: 9783030762759

Standard No.: 10.1007/978-3-030-76275-9doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5 / R69 2021

Dewey Class. No.: 519.6

An optimization primer
LDR:02358nmm a2200337 a 4500 001 2262374
003 DE-He213
005 20220328103343.0
006 m o d
007 cr nn 008maaau
008 220616s2021 sz s 0 eng d
020 $a 9783030762759 $q (electronic bk.)
020 $a 9783030762742 $q (paper)
024 7 $a 10.1007/978-3-030-76275-9 $2 doi
035 $a 978-3-030-76275-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5 $b R69 2021
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 .R892 2021
100 1 $a Royset, Johannes O. $3 2191157
245 1 3 $a An optimization primer $h [electronic resource] / $c by Johannes O. Royset, Roger J.-B. Wets.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a 1 online resource (xviii, 676 p.) : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in operations research and financial engineering, $x 2197-1773
505 0 $a Prelude -- Convex optimization -- Optimization under uncertainty -- Minimization problems -- Perturbation and duality -- Without convexity or smoothness -- Generalized Equations -- Risk modeling and sample averages -- Games and minsup problems -- Decomposition.
520 $a This richly illustrated book introduces the subject of optimization to a broad audience with a balanced treatment of theory, models and algorithms. Through numerous examples from statistical learning, operations research, engineering, finance and economics, the text explains how to formulate and justify models while accounting for real-world considerations such as data uncertainty. It goes beyond the classical topics of linear, nonlinear and convex programming and deals with nonconvex and nonsmooth problems as well as games, generalized equations and stochastic optimization. The book teaches theoretical aspects in the context of concrete problems, which makes it an accessible onramp to variational analysis, integral functions and approximation theory. More than 100 exercises and 200 fully developed examples illustrate the application of the concepts. Readers should have some foundation in differential calculus and linear algebra. Exposure to real analysis would be helpful but is not prerequisite.
650 0 $a Mathematical optimization. $3 517763
650 1 4 $a Optimization. $3 891104
700 1 $a Wets, Roger J.-B. $3 663924
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/10.1007/978-3-030-76275-9
950 $a Mathematics and Statistics (SpringerNature-11649)