|edoc-Server der Humboldt-Universität zu Berlin|
|Author(s):||Matti Koivu, Helsinki School of Economics||Title:||Variance reduction in sample approximations of stochastic programs|
|Date of Acceptance:||02.10.2004|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Keywords (eng):||Stochastic optimization, discretization, varinace reduction techniques, randomizes quasi-monte carlo methods, antithetic variates|
Mathematical programming 3 (Vol. 103, 2005)
Springer (Berlin [u.a.])
|Metadata export: To export the complete metadata set as Endote or Bibtex format please click to the appropriate link.||Endnote Bibtex|
|This paper studies the use of randomized Quasi-Monte Carlo methods (RQMC) in sample approximations of stochastic programs. In high dimensional numerical integration, RQMC methods often substantially reduce the variance of sample approximations compared to MC. It seems thus natural to use RQMC methods in sample approximations of stochastic programs. It is shown, that RQMC methods produce epi-convergent approximations of the original problem. RQMC and MC methods are compared numerically in five different portfolio management models. In the tests, RQMC methods outperform MC sampling substantially reducing the sample variance and bias of optimal values in all the considered problems.|
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 May 2011: