Authors:
- Provides a pedagogical, self-contained analysis of the theory of convex optimization and stochastic programming
- Offers a synthetical view of many applications such as semidefinite programming, Markov processes, generalized convexity and optimal transport
- Includes a study of algorithmic aspects: dynamic programming, stochastic dual dynamic programming (in the case of convex Bellman value functions) and linear decision rules
Part of the book series: Universitext (UTX)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
About this book
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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Authors and Affiliations
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Inria and CMAP, Ecole Polytechnique, Palaiseau, France
J. Frédéric Bonnans
About the author
J.F. Bonnans is an expert in convex analysis and dynamic optimization, both in the deterministic and stochastic setting. His main contributions deal with the sensitivity analysis of optimization problems, high order optimality conditions, optimal control and stochastic control. He worked on quantization methods for stochastic programming problems, on the approximate dynamic programming for problems with monotone value function, and on sparse linear regression.
Bibliographic Information
Book Title: Convex and Stochastic Optimization
Authors: J. Frédéric Bonnans
Series Title: Universitext
DOI: https://doi.org/10.1007/978-3-030-14977-2
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-14976-5Published: 29 April 2019
eBook ISBN: 978-3-030-14977-2Published: 24 April 2019
Series ISSN: 0172-5939
Series E-ISSN: 2191-6675
Edition Number: 1
Number of Pages: XIII, 311
Topics: Optimization, Probability Theory and Stochastic Processes