|edoc-Server der Humboldt-Universität zu Berlin|
N. Miller, Rutgers University|
A. Ruszczynski, Rutgers University
|Title:||Risk-Averse Two-Stage Stochastic Linear Programming: Modeling and Decomposition|
|Date of Acceptance:||16.10.2009|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Complete Preprint:||pdf (urn:nbn:de:kobv:11-100101218)|
|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.|
|We formulate a risk-averse two-stage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as a composition of conditional risk measures. We analyze properties of the problem and derive necessary and sufﬁcient optimality conditions. Next, we construct two decomposition methods for solving the problem. The ﬁrst method is based on the generic cutting plane approach, while the second method exploits the composite structure of the objective function. We illustrate their performance on a portfolio optimization problem.|
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: