Optimal Monte Carlo algorithms

被引：4
|
作者：
Dimov, Ivan T. [1 ]
机构：
[1] Bulgarian Acad Sci, Dept Parallel Algorithms, Inst Parallel Proc, Sofia 1113, Bulgaria
来源：
IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, Proceedings | 2006年
关键词：
Monte Carlo algorithms; deterministic algorithms; integral equations; unimprovable rate of convergence;
D O I：
10.1109/JVA.2006.37
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The question "what Monte Carlo can do and cannot do efficiently " is discussed for some functional spaces that define the regularity of the input data. Important for practical computations data classes are considered: classes of functions with bounded derivatives and Holder type conditions. Theoretical performance analysis of some algorithms with unimprovable rate of convergence is given. Estimates of complexity of two classes of algorithms - deterministic and randomized for the solution of a class of integral equations are presented.
引用
收藏
页码：125 / 131
页数：7
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