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BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems

  • Book
  • © 2015

Overview

Authors:
  1. Urmila Diwekar

    1. Clarendon Hills, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Amy David

    1. Krennert School of Business, Purdue University, West Lafayette, USA

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    You can also search for this author in PubMed   Google Scholar

  • 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)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Introduction

    • Urmila Diwekar, Amy David
    Pages 1-7

  3. Uncertainty Analysis and Sampling Techniques

    • Urmila Diwekar, Amy David
    Pages 9-25

  4. Probability Density Functions and Kernel Density Estimation

    • Urmila Diwekar, Amy David
    Pages 27-34

  5. The BONUS Algorithm

    • Urmila Diwekar, Amy David
    Pages 35-56

  6. Water Management Under Weather Uncertainty

    • Urmila Diwekar, Amy David
    Pages 57-66

  7. Real-Time Optimization forWater Management

    • Urmila Diwekar, Amy David
    Pages 67-79

  8. Sensor Placement Under Uncertainty for Power Plants

    • Urmila Diwekar, Amy David
    Pages 81-94

  9. The L-Shaped BONUS Algorithm

    • Urmila Diwekar, Amy David
    Pages 95-115

  10. The Environmental Trading Problem

    • Urmila Diwekar, Amy David
    Pages 117-126

  11. Water Security Networks

    • Urmila Diwekar, Amy David
    Pages 127-137

  12. Back Matter

    Pages 139-146
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Keywords

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)

Authors and Affiliations

  • Clarendon Hills, USA

    Urmila Diwekar

  • Krennert School of Business, Purdue University, West Lafayette, USA

    Amy David

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