|edoc-Server der Humboldt-Universität zu Berlin|
|Author(s):||Daniel Kuhn, University of St. Gallen||Title:||Aggregation and Discretization in Multistage Stochastic Programming|
|Date of Acceptance:||28.12.2005|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Complete Preprint:||pdf (urn:nbn:de:kobv:11-10059940)|
|Keywords (eng):||aggregation, stochastic programming, approximation, discretization, bounds|
|Metadata export: To export the complete metadata set as Endote or Bibtex format please click to the appropriate link.||Endnote Bibtex|
|print on demand: If you click on this icon you can order a print copy of this publication.|
|Diese Seite taggen: These icons lead to social bookmarking systems where you can create and manage personal bookmarks and discover bookmakrs of other users.|
|Multistage stochastic programs have applications in many areas and support policy makers in finding rational decisions that hedge against unforeseen neg- ative events. In order to ensure computational tractability, continuous-state stochastic programs are usually discretized; and frequently, the curse of dimensionality dictates that decision stages must be aggregated. In this article we construct two discrete, stage-aggregated stochastic programs which provide upper and lower bounds on the optimal value of the original problem. The approximate problems involve finitely many decisions and constraints, thus principally allowing for numerical solution.|
These data concerning access statistics for individual documents
have been compiled using the webserver log files aggregated by AWSTATS.
They refer to a monthly access count to the full text documents as well as to the entry page.
As for format versions of a document which consist of multiple files (such as HTML) the highest monthly access number to one of the files (chapters) is shown respectivly.
To see the detailled access numbers please move the mouse pointer over the single bars of the digaram.
Gesamtzahl der Zugriffe seit Jul 2011: