Asymptotic complexity of Monte Carlo methods for solving linear systems

被引：12
|
作者：
Danilov, DL
Ermakov, SM
Halton, JH
机构：
[1] St Petersburg State Univ, Fac Math & Mech, St Petersburg 198904, Russia
[2] Univ N Carolina, Chapel Hill, NC 27599 USA
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2000年 / 85卷 / 1-2期
关键词：
algorithm complexity; Monte Carlo method; linear systems; collision estimator;
D O I：
10.1016/S0378-3758(99)00060-9
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
A comparative analysis was performed on the Monte Carlo collision estimator in the conjugated von-Neumann-Ulam scheme for solving systems of linear algebraic equations. It was found that there exist wide classes of linear systems for which the Monte Carlo algorithm asymptotically (by dimension of system) has a better order of complexity than the iterative method. (C) 2000 Published by Elsevier Science B.V. All rights reserved.
引用
收藏
页码：5 / 18
页数：14
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