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An optimization primer

Royset, Johannes O.

An optimization primer

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An optimization primerby 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.
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$q(electronic bk.)

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$q(electronic bk.)

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

183292
Mathematical optimization.

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

Dewey Class. No.: 519.6

An optimization primer
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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.
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