|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.|
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