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Stability, Approximation, and Decomposition in Two- And Multistage Stochastic Programming - by  Christian Küchler (Paperback) - 1 of 1

Stability, Approximation, and Decomposition in Two- And Multistage Stochastic Programming - by Christian Küchler (Paperback)

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Highlights

  • Stochastic programming provides a framework for modelling, analyzing, and solving optimization problems with some parameters being not known up to a probability distribution.
  • About the Author: Dr. Christian Küchler completed his doctoral thesis at the Humboldt University, Berlin.
  • 184 Pages
  • Mathematics, Probability & Statistics
  • Series Name: Stochastic Programming

Description



Book Synopsis



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.



From the Back Cover



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.



About the Author



Dr. Christian Küchler completed his doctoral thesis at the Humboldt University, Berlin. He currently works as a quantitative analyst at Landesbank Berlin AG.
Manufacturer Suggested Age: 22 Years and Up
Language: English
Genre: Mathematics
Sub-Genre: Probability & Statistics
Format: Paperback
Series Title: Stochastic Programming
Number of Pages: 184
Author: Christian Küchler
Street Date: September 24, 2009
TCIN: 1011491036
UPC: 9783834809216
Item Number (DPCI): 247-17-1039
Origin: Made in the USA or Imported
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