Stability, Approximation, and Decomposition in Two- and Multistage Stochastic Programming [electronic resource] / by Christian Küchler.

By:

Küchler, Christian [author.]

Contributor(s):

SpringerLink (Online service)

Material type: Text

TextPublisher: Wiesbaden : Vieweg+Teubner, 2009Description: 184 p. 49 illus. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783834893994

Subject(s):

Additional physical formats: Printed edition:: No titleDDC classification:

003.3 23

LOC classification:

TA342-343

Online resources:

Click here to access online

Contents:

Stability of Multistage Stochastic Programs -- Recombining Trees for Multistage Stochastic Programs -- Scenario Reduction with Respect to Discrepancy Distances.

In: Springer eBooksSummary: 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.

Holdings ( 1 )
Title notes ( 2 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
eBook	e-Library	EBook		Available

Total holds: 0

Stability of Multistage Stochastic Programs -- Recombining Trees for Multistage Stochastic Programs -- Scenario Reduction with Respect to Discrepancy Distances.

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.