Stability, approximation, and decomposition in two- and multistage stochastic programming /
General Material Designation
[Book]
First Statement of Responsibility
Christian Küchler.
EDITION STATEMENT
Edition Statement
1. Aufl.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Wiesbaden :
Name of Publisher, Distributor, etc.
Vieweg + Teubner,
Date of Publication, Distribution, etc.
2009.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (x, 168 pages) :
Other Physical Details
illustrations
SERIES
Series Title
Vieweg + Teubner research : Stochastic programming
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references (pages 159-168).
CONTENTS NOTE
Text of Note
Preface; Contents; List of Figures; List of Tables; Index of Notation; Chapter 1 Introduction; 1.1 Stochastic Programming Models; 1.2 Approximations, Stability, and Decomposition; 1.3 Contributions; Chapter 2 Stability of Multistage Stochastic Programs; 2.1 Problem Formulation; 2.2 Continuity of the Recourse Function; 2.3 Approximations; 2.4 Calm Decisions; 2.5 Stability; Chapter 3 Recombining Trees for Multistage Stochastic Programs; 3.1 Problem Formulation and Decomposition; 3.2 An Enhanced Nested Benders Decomposition; 3.3 Construction of Recombining Trees; 3.4 Case Study.
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DISSERTATION (THESIS) NOTE
Text of Note
Diss.: Berlin, Humboldt-University, 2009.
SUMMARY OR ABSTRACT
Text of Note
Stochastic programming provides a framework for modelling, analyzing, and solving optimization problems with some parameters being not known up to a probability distribution. Such problems arise in a variety of applications, such as inventory control, financial planning and portfolio optimization, airline revenue management, scheduling and operation of power systems, and supply chain management. Christian Küchler studies various aspects of the stability of stochastic optimization problems as well as approximation and decomposition methods in stochastic programming. In particular, the author presents an extension of the Nested Benders decomposition algorithm related to the concept of recombining scenario trees. The approach combines the concept of cut sharing with a specific aggregation procedure and prevents an exponentially growing number of subproblem evaluations. Convergence results and numerical properties are discussed.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Springer
Stock Number
978-3-8348-0921-6
OTHER EDITION IN ANOTHER MEDIUM
Title
Stability, approximation, and decomposition in two- and multistage stochastic programming.