Variance reduction in sample approximations of stochastic programs
Variance reduction in sample approximations of stochastic programs
复制标题
随机程序样本近似值的方差减少
DOI：
10.1007/s10107-004-0557-0
复制
发表时间：
2005
影响因子：
2.7
通讯作者：
M. Koivu
中科院分区：
文献类型：
--
作者：
M. Koivu
摘要：本文研究了随机拟蒙特卡罗方法（RQMC）在随机规划的样本近似中的应用。在数值积分中，与蒙特卡罗（MC）相比，RQMC方法通常大大减少了样本近似的方差。因此，在随机规划的样本近似中使用RQMC方法似乎是很自然的。结果表明，RQMC方法产生epi-convergent逼近的原问题。在五个不同的投资组合管理模型中，对RQMC和MC方法进行了数值比较。在测试中，RQMC方法优于MC抽样大大降低了样本方差和偏差的最优值在所有考虑的问题。
Abstract.This paper studies the use of randomized Quasi-Monte Carlo methods (RQMC) in sample approximations of stochastic programs. In numerical integration, RQMC methods often substantially reduce the variance of sample approximations compared to Monte Carlo (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.