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SpringerBriefs in Optimization

BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems

Authors: Diwekar, Urmila, David, Amy

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  • 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
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eBook $54.99
price for Mexico (gross)
  • ISBN 978-1-4939-2282-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for Mexico
  • ISBN 978-1-4939-2281-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world.

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)


Table of contents (10 chapters)

Table of contents (10 chapters)

Buy this book

eBook $54.99
price for Mexico (gross)
  • ISBN 978-1-4939-2282-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for Mexico
  • ISBN 978-1-4939-2281-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems
Authors
Series Title
SpringerBriefs in Optimization
Copyright
2015
Publisher
Springer-Verlag New York
Copyright Holder
Urmila Diwekar, Amy David
eBook ISBN
978-1-4939-2282-6
DOI
10.1007/978-1-4939-2282-6
Softcover ISBN
978-1-4939-2281-9
Series ISSN
2190-8354
Edition Number
1
Number of Pages
XVIII, 146
Number of Illustrations
38 b/w illustrations, 19 illustrations in colour
Topics