|edoc-Server der Humboldt-Universität zu Berlin|
Vincent Guiges, IMPA|
Werner Römisch, Humboldt-University
|Title:||SDDP for multistage stochastic linear programs based on spectral risk measures|
|Date of Acceptance:||09.04.2012|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Complete Preprint:||pdf (urn:nbn:de:kobv:11-100201028)|
|Keywords (eng):||stochastic programming, Monte-Carlo sampling, spectral risk measure, risk-averse optimization, decomposition algorithms|
|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.|
|We consider risk-averse formulations of multistage stochastic linear programs. For these formulations, based on convex combinations of spectral risk measures, risk-averse dynamic programming equations can be written. As a result, the Stochastic Dual Dynamic Programming (SDDP) algorithm can be used to obtain approximations of the corresponding risk-averse recourse functions. This allows us to deﬁne a risk-averse nonanticipative feasible policy for the stochastic linear program. Formulas for the cuts that approximate the recourse functions are given.|
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 Apr 2012: