The Resource BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems, by Urmila Diwekar, Amy David, (electronic resource)
BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems, by Urmila Diwekar, Amy David, (electronic resource)
Resource Information
The item BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems, by Urmila Diwekar, Amy David, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.This item is available to borrow from 1 library branch.
Resource Information
The item BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems, by Urmila Diwekar, Amy David, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.
This item is available to borrow from 1 library branch.
 Summary
 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
 Language
 eng
 Extent
 XVIII, 146 p. 57 illus., 19 illus. in color.
 Contents

 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 LShaped BONUS Algorithm
 9. The Environmental Trading Problem
 10. Water Security Networks
 References
 Index
 Isbn
 9781493922826
 Label
 BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems
 Title
 BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems
 Statement of responsibility
 by Urmila Diwekar, Amy David
 Language
 eng
 Summary
 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
 http://library.link/vocab/creatorName
 Diwekar, Urmila
 Image bit depth
 0
 Literary form
 non fiction
 http://library.link/vocab/relatedWorkOrContributorName

 David, Amy.
 SpringerLink (Online service)
 Series statement
 SpringerBriefs in Optimization,
 http://library.link/vocab/subjectName

 Mathematics
 Dynamics
 Ergodic theory
 System theory
 Algorithms
 Operations research
 Management science
 Label
 BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems, by Urmila Diwekar, Amy David, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier.
 Color
 not applicable
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent.
 Contents
 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 LShaped BONUS Algorithm  9. The Environmental Trading Problem  10. Water Security Networks  References  Index
 Control code
 9781493922826
 Dimensions
 unknown
 Extent
 XVIII, 146 p. 57 illus., 19 illus. in color.
 File format
 multiple file formats
 Form of item
 electronic
 Governing access note
 Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, noncommercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
 Isbn
 9781493922826
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia.
 Media type code

 c
 Other control number
 10.1007/9781493922826
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (OCoLC)1086549612
 Label
 BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems, by Urmila Diwekar, Amy David, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier.
 Color
 not applicable
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent.
 Contents
 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 LShaped BONUS Algorithm  9. The Environmental Trading Problem  10. Water Security Networks  References  Index
 Control code
 9781493922826
 Dimensions
 unknown
 Extent
 XVIII, 146 p. 57 illus., 19 illus. in color.
 File format
 multiple file formats
 Form of item
 electronic
 Governing access note
 Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, noncommercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
 Isbn
 9781493922826
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia.
 Media type code

 c
 Other control number
 10.1007/9781493922826
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (OCoLC)1086549612
Library Links
Embed
Settings
Select options that apply then copy and paste the RDF/HTML data fragment to include in your application
Embed this data in a secure (HTTPS) page:
Layout options:
Include data citation:
<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.library.eui.eu/portal/BONUSAlgorithmforLargeScaleStochastic/DRx9bGrsuQo/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/BONUSAlgorithmforLargeScaleStochastic/DRx9bGrsuQo/">BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems, by Urmila Diwekar, Amy David, (electronic resource)</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>
Note: Adjust the width and height settings defined in the RDF/HTML code fragment to best match your requirements
Preview
Cite Data  Experimental
Data Citation of the Item BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems, by Urmila Diwekar, Amy David, (electronic resource)
Copy and paste the following RDF/HTML data fragment to cite this resource
<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.library.eui.eu/portal/BONUSAlgorithmforLargeScaleStochastic/DRx9bGrsuQo/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/BONUSAlgorithmforLargeScaleStochastic/DRx9bGrsuQo/">BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems, by Urmila Diwekar, Amy David, (electronic resource)</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>