BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems

  • Urmila Diwekar
  • Amy David

Part of the SpringerBriefs in Optimization book series (BRIEFSOPTI)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Urmila Diwekar, Amy David
    Pages 1-7
  3. Urmila Diwekar, Amy David
    Pages 9-25
  4. Urmila Diwekar, Amy David
    Pages 27-34
  5. Urmila Diwekar, Amy David
    Pages 35-56
  6. Urmila Diwekar, Amy David
    Pages 57-66
  7. Urmila Diwekar, Amy David
    Pages 67-79
  8. Urmila Diwekar, Amy David
    Pages 81-94
  9. Urmila Diwekar, Amy David
    Pages 95-115
  10. Urmila Diwekar, Amy David
    Pages 117-126
  11. Urmila Diwekar, Amy David
    Pages 127-137
  12. Back Matter
    Pages 139-146

About this book

Introduction

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.

Keywords

BONUS algorithm SNLP Stochastic Programming power systems sensor placement water management

Authors and affiliations

  • Urmila Diwekar
    • 1
  • Amy David
    • 2
  1. 1.Clarendon HillsUSA
  2. 2.Krennert School of BusinessPurdue UniversityWest LafayetteUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-2282-6
  • Copyright Information Urmila Diwekar, Amy David 2015
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4939-2281-9
  • Online ISBN 978-1-4939-2282-6
  • Series Print ISSN 2190-8354
  • Series Online ISSN 2191-575X
  • About this book
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