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Convex and stochastic optimization

Bonnans, J. Frederic.

Convex and stochastic optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Convex and stochastic optimizationby J. Frederic Bonnans.
Author:	Bonnans, J. Frederic.
Published:	Cham :Springer International Publishing :2019.
Description:	xiii, 311 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Mathematical optimization.
Online resource:	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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245 1 0 $a Convex and stochastic optimization $h [electronic resource] / $c by J. Frederic Bonnans.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xiii, 311 p. : $b ill., digital ; $c 24 cm.
490 1 $a Universitext, $x 0172-5939
505 0 $a 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.
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.
650 0 $a Mathematical optimization. $3 183292
650 0 $a Stochastic processes. $3 181874
650 0 $a Convex functions. $3 191128
650 1 4 $a Optimization. $3 274084
650 2 4 $a Probability Theory and Stochastic Processes. $3 274061
710 2 $a SpringerLink (Online service) $3 273601
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856 4 0 $u https://doi.org/10.1007/978-3-030-14977-2
950 $a Mathematics and Statistics (Springer-11649)