Higher order quasi-Monte Carlo methods: A comparison

نویسندگان

  • Dirk Nuyens
  • Ronald Cools
چکیده

Quasi-Monte Carlo is usually employed to speed up the convergence of Monte Carlo in approximating multivariate integrals. While convergence of the Monte Carlo method is O(N−1/2), that of plain quasi-Monte Carlo can achieve O(N−1). Several methods exist to increase its convergence to O(N−α ), α > 1, if the integrand has enough smoothness. We discuss two methods: lattice rules with periodization and higher order digital nets, and present a numerical comparison.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Comparison of Quasi-Monte Carlo-Based Methods for Simulation of Markov Chains

Monte Carlo MC method is probably the most widespread simulation technique due to its ease of use Quasi Monte Carlo QMC methods have been designed in order to speed up the convergence rate of MC but their implementation requires more stringent assumptions For instance the direct QMC simulation of Markov chains is ine cient due to the correlation of the points used We propose here to survey the ...

متن کامل

On quasi-Monte Carlo rules achieving higher order convergence

Quasi-Monte Carlo rules which can achieve arbitrarily high order of convergence have been introduced recently. The construction is based on digital nets and the analysis of the integration error uses Walsh functions. Various approaches have been used to show arbitrarily high convergence. In this paper we explain the ideas behind higher order quasi-Monte Carlo rules by leaving out most of the te...

متن کامل

Monte Carlo Extension of Quasi-monte Carlo

This paper surveys recent research on using Monte Carlo techniques to improve quasi-Monte Carlo techniques. Randomized quasi-Monte Carlo methods provide a basis for error estimation. They have, in the special case of scrambled nets, also been observed to improve accuracy. Finally through Latin supercube sampling it is possible to use Monte Carlo methods to extend quasi-Monte Carlo methods to hi...

متن کامل

Multi-level Higher Order Qmc Galerkin Discretization for Affine Parametric Operator Equations

We develop a convergence analysis of a multi-level algorithm combining higher order quasi-Monte Carlo (QMC) quadratures with general Petrov-Galerkin discretizations of countably affine parametric operator equations of elliptic and parabolic type, extending both the multi-level first order analysis in [F.Y. Kuo, Ch. Schwab, and I.H. Sloan, Multi-level quasi-Monte Carlo finite element methods for...

متن کامل

Exploring multi-dimensional spaces: a Comparison of Latin Hypercube and Quasi Monte Carlo Sampling Techniques

Three sampling methods are compared for efficiency on a number of test problems of various complexity for which analytic quadratures are available. The methods compared are Monte Carlo with pseudo-random numbers, Latin Hypercube Sampling, and Quasi Monte Carlo with sampling based on Sobol’ sequences. Generally results show superior performance of the Quasi Monte Carlo approach based on Sobol’ s...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010