عنوان

BONUS algorithm for large scale stochastic nonlinear programming problems /

(Number (ISBN

1493922815

(Number (ISBN

1493922823

(Number (ISBN

9781493922819

(Number (ISBN

9781493922826

Erroneous ISBN

9781493922819

Number

dltt

Title Proper

BONUS algorithm for large scale stochastic nonlinear programming problems /

General Material Designation

[Book]

First Statement of Responsibility

Urmila Diwekar, Amy David

Specific Material Designation and Extent of Item

1 online resource (xviii, 146 pages) :

Other Physical Details

illustrations (some color).

Series Title

SpringerBriefs in optimization,

ISSN of Series

2190-8354

Text of Note

Includes bibliographical references and index

Text of Note

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

0

Text of Note

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

International Standard Book Number

9781493922819

Algorithms.

Stochastic programming.

Algorithms.

Mathematics.

Stochastic Processes.

MAT-- 003000

MAT-- 029000

Number

Edition

Class number

Class number

Diwekar, Urmila M.

David, Amy

Ohio Library and Information Network.

Date of Transaction

20161014100150.2

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y