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Convex and stochastic optimization

Bonnans, J. Frederic.

Convex and stochastic optimization

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Convex and stochastic optimizationby J. Frederic Bonnans.
作者:	Bonnans, J. Frederic.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xiii, 311 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Mathematical optimization.
電子資源:	https://doi.org/10.1007/978-3-030-14977-2
ISBN:	nam a2200337 a 4500

Convex and stochastic optimization
Bonnans, J. Frederic.

Convex and stochastic optimization[electronic resource] /by J. Frederic Bonnans. - Cham :Springer International Publishing :2019. - xiii, 311 p. :ill., digital ;24 cm. - Universitext,0172-5939. - Universitext..

1 A convex optimization toolbox -- 2 Semideﬁnite and semiinﬁnite programming -- 3 An integration toolbox -- 4 Risk measures -- 5 Sampling and optimizing -- 6 Dynamic stochastic optimization -- 7 Markov decision processes -- 8 Algorithms -- 9 Generalized convexity and transportation theory -- References -- Index.

This textbook provides an introduction to convex duality for optimization problems in Banach spaces, integration theory, and their application to stochastic programming problems in a static or dynamic setting. It introduces and analyses the main algorithms for stochastic programs, while the theoretical aspects are carefully dealt with. The reader is shown how these tools can be applied to various fields, including approximation theory, semidefinite and second-order cone programming and linear decision rules. This textbook is recommended for students, engineers and researchers who are willing to take a rigorous approach to the mathematics involved in the application of duality theory to optimization with uncertainty.

ISBN: nam a2200337 a 4500

Standard No.: 10.1007/978-3-030-14977-2doiSubjects--Topical Terms:

183292
Mathematical optimization.

LC Class. No.: QA402.5 / .B66 2019

Dewey Class. No.: 519.6

Convex and stochastic optimization
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520 $a This textbook provides an introduction to convex duality for optimization problems in Banach spaces, integration theory, and their application to stochastic programming problems in a static or dynamic setting. It introduces and analyses the main algorithms for stochastic programs, while the theoretical aspects are carefully dealt with. The reader is shown how these tools can be applied to various fields, including approximation theory, semidefinite and second-order cone programming and linear decision rules. This textbook is recommended for students, engineers and researchers who are willing to take a rigorous approach to the mathematics involved in the application of duality theory to optimization with uncertainty.
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