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Convex and stochastic optimization
~
Bonnans, J. Frederic.
Convex and stochastic optimization
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Convex and stochastic optimization/ by 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:
9783030149772
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: 9783030149772
Standard No.: 10.1007/978-3-030-14977-2doiSubjects--Topical Terms:
527675
Mathematical optimization.
LC Class. No.: QA402.5 / .B66 2019
Dewey Class. No.: 519.6
Convex and stochastic optimization
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by J. Frederic Bonnans.
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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.
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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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Mathematics and Statistics (Springer-11649)
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