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BONUS algorithm for large scale stoc...

David, Amy.

BONUS algorithm for large scale stochastic nonlinear programming problems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	BONUS algorithm for large scale stochastic nonlinear programming problemsby Urmila Diwekar, Amy David.
作者:	Diwekar, Urmila.
其他作者:	David, Amy.
出版者:	New York, NY :Springer New York :2015.
面頁冊數:	xviii, 146 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Stochastic programming.
電子資源:	http://dx.doi.org/10.1007/978-1-4939-2282-6
ISBN:	9781493922826 (electronic bk.)

BONUS algorithm for large scale stochastic nonlinear programming problems
Diwekar, Urmila.

BONUS algorithm for large scale stochastic nonlinear programming problems[electronic resource] /by Urmila Diwekar, Amy David. - New York, NY :Springer New York :2015. - xviii, 146 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in optimization,2190-8354. - SpringerBriefs in optimization..

1. Introduction -- 2. Uncertainty Analysis and Sampling Techniques -- 3. Probability Density Functions and Kernel Density Estimation -- 4. The BONUS Algorithm -- 5. Water Management under Weather Uncertainty -- 6. Real Time Optimization for Water Management -- 7. Sensor Placement under Uncertainty for Power Plants -- 8. The L-Shaped BONUS Algorithm -- 9. The Environmental Trading Problem -- 10. Water Security Networks -- References -- Index.

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.

ISBN: 9781493922826 (electronic bk.)

Standard No.: 10.1007/978-1-4939-2282-6doiSubjects--Topical Terms:

186295
Stochastic programming.

LC Class. No.: T57.79

Dewey Class. No.: 519.7

BONUS algorithm for large scale stochastic nonlinear programming problems
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520 $a 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.
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