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Convex and Stochastic Optimization

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Convex and Stochastic Optimization/ by J. Frédéric Bonnans.
Author:	Bonnans, J. Frédéric.
Description:	XIII, 311 p.online resource. :
Contained By:	Springer Nature eBook
Subject:	Mathematical optimization. -
Online resource:	https://doi.org/10.1007/978-3-030-14977-2
ISBN:	9783030149772

Convex and Stochastic Optimization
Bonnans, J. Frédéric.

Convex and Stochastic Optimization[electronic resource] /by J. Frédéric Bonnans. - 1st ed. 2019. - XIII, 311 p.online resource. - 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: 9783030149772

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

527675
Mathematical optimization.

LC Class. No.: QA402.5-402.6

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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