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Table of contents (14 chapters)
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Front Matter
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Preliminaries: Convex Analysis and Convex Programming
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Separable Programming
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Front Matter
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Convex Separable Programming with Bounds on the Variables
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Front Matter
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Selected Supplementary Topics and Applications
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Front Matter
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Back Matter
About this book
Convex separable programs subject to inequality/ equality constraint(s) and bounds on variables are also studied and iterative algorithms of polynomial complexity are proposed.
As an application, these algorithms are used in the implementation of stochastic quasigradient methods to some separable stochastic programs. Numerical approximation with respect to I1 and I4 norms, as a convex separable nonsmooth unconstrained minimization problem, is considered as well.
Audience: Advanced undergraduate and graduate students, mathematical programming/ operations research specialists.
Authors and Affiliations
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Department of Mathematics, South West University, Blagoevgrad, Bulgaria
Stefan M. Stefanov
Bibliographic Information
Book Title: Separable Programming
Book Subtitle: Theory and Methods
Authors: Stefan M. Stefanov
Series Title: Applied Optimization
DOI: https://doi.org/10.1007/978-1-4757-3417-1
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media Dordrecht 2001
Hardcover ISBN: 978-0-7923-6882-3Published: 31 May 2001
eBook ISBN: 978-1-4757-3417-1Published: 11 November 2013
Series ISSN: 1384-6485
Edition Number: 1
Number of Pages: XIX, 314
Topics: Optimization