Overview
- Incorporates the BONUS algorithm into real world applications
- Characterizes a fast algorithm for large scale stochastic nonlinear programming problems
- Describes a new technique that can be used in areas such as security, sensor and energy systems
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Optimization (BRIEFSOPTI)
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Keywords
Table of contents (10 chapters)
Reviews
“The authors try to introduce and give a survey of two types of solution algorithms: the BONUS (Better Optimization of Nonlinear Uncertain System) algorithm and the L-shaped BONUS algorithm. … the text is written in an understandable way and it should prove useful to specialists from different fields of investigation.” (Vlasta Kaňková, Mathematical Reviews, May, 2016)
Authors and Affiliations
Bibliographic Information
Book Title: BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems
Authors: Urmila Diwekar, Amy David
Series Title: SpringerBriefs in Optimization
DOI: https://doi.org/10.1007/978-1-4939-2282-6
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Urmila Diwekar, Amy David 2015
Softcover ISBN: 978-1-4939-2281-9Published: 06 March 2015
eBook ISBN: 978-1-4939-2282-6Published: 05 March 2015
Series ISSN: 2190-8354
Series E-ISSN: 2191-575X
Edition Number: 1
Number of Pages: XVIII, 146
Number of Illustrations: 38 b/w illustrations, 19 illustrations in colour
Topics: Operations Research, Management Science, Systems Theory, Control, Dynamical Systems and Ergodic Theory, Algorithms