Authors:
- 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)
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (10 chapters)
-
Front Matter
-
Back Matter
About this book
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
-
Clarendon Hills, USA
Urmila Diwekar
-
Krennert School of Business, Purdue University, West Lafayette, USA
Amy David
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