6,793 research outputs found
Some Results on the Complexity of Numerical Integration
This is a survey (21 pages, 124 references) written for the MCQMC 2014
conference in Leuven, April 2014. We start with the seminal paper of Bakhvalov
(1959) and end with new results on the curse of dimension and on the complexity
of oscillatory integrals. Some small errors of earlier versions are corrected
Hot new directions for quasi-Monte Carlo research in step with applications
This article provides an overview of some interfaces between the theory of
quasi-Monte Carlo (QMC) methods and applications. We summarize three QMC
theoretical settings: first order QMC methods in the unit cube and in
, and higher order QMC methods in the unit cube. One important
feature is that their error bounds can be independent of the dimension
under appropriate conditions on the function spaces. Another important feature
is that good parameters for these QMC methods can be obtained by fast efficient
algorithms even when is large. We outline three different applications and
explain how they can tap into the different QMC theory. We also discuss three
cost saving strategies that can be combined with QMC in these applications.
Many of these recent QMC theory and methods are developed not in isolation, but
in close connection with applications
The construction of good lattice rules and polynomial lattice rules
A comprehensive overview of lattice rules and polynomial lattice rules is
given for function spaces based on semi-norms. Good lattice rules and
polynomial lattice rules are defined as those obtaining worst-case errors
bounded by the optimal rate of convergence for the function space. The focus is
on algebraic rates of convergence for
and any , where is the decay of a series representation
of the integrand function. The dependence of the implied constant on the
dimension can be controlled by weights which determine the influence of the
different dimensions. Different types of weights are discussed. The
construction of good lattice rules, and polynomial lattice rules, can be done
using the same method for all ; but the case is special
from the construction point of view. For the
component-by-component construction and its fast algorithm for different
weighted function spaces is then discussed
On Weak Tractability of the Clenshaw-Curtis Smolyak Algorithm
We consider the problem of integration of d-variate analytic functions
defined on the unit cube with directional derivatives of all orders bounded by
1. We prove that the Clenshaw Curtis Smolyak algorithm leads to weak
tractability of the problem. This seems to be the first positive tractability
result for the Smolyak algorithm for a normalized and unweighted problem. The
space of integrands is not a tensor product space and therefore we have to
develop a different proof technique. We use the polynomial exactness of the
algorithm as well as an explicit bound on the operator norm of the algorithm.Comment: 18 page
Constructing lattice points for numerical integration by a reduced fast successive coordinate search algorithm
In this paper, we study an efficient algorithm for constructing node sets of
high-quality quasi-Monte Carlo integration rules for weighted Korobov, Walsh,
and Sobolev spaces. The algorithm presented is a reduced fast successive
coordinate search (SCS) algorithm, which is adapted to situations where the
weights in the function space show a sufficiently fast decay. The new SCS
algorithm is designed to work for the construction of lattice points, and, in a
modified version, for polynomial lattice points, and the corresponding
integration rules can be used to treat functions in different kinds of function
spaces. We show that the integration rules constructed by our algorithms
satisfy error bounds of optimal convergence order. Furthermore, we give details
on efficient implementation such that we obtain a considerable speed-up of
previously known SCS algorithms. This improvement is illustrated by numerical
results. The speed-up obtained by our results may be of particular interest in
the context of QMC for PDEs with random coefficients, where both the dimension
and the required numberof points are usually very large. Furthermore, our main
theorems yield previously unknown generalizations of earlier results.Comment: 33 pages, 2 figure
Efficient calculation of the worst-case error and (fast) component-by-component construction of higher order polynomial lattice rules
We show how to obtain a fast component-by-component construction algorithm
for higher order polynomial lattice rules. Such rules are useful for
multivariate quadrature of high-dimensional smooth functions over the unit cube
as they achieve the near optimal order of convergence. The main problem
addressed in this paper is to find an efficient way of computing the worst-case
error. A general algorithm is presented and explicit expressions for base~2 are
given. To obtain an efficient component-by-component construction algorithm we
exploit the structure of the underlying cyclic group.
We compare our new higher order multivariate quadrature rules to existing
quadrature rules based on higher order digital nets by computing their
worst-case error. These numerical results show that the higher order polynomial
lattice rules improve upon the known constructions of quasi-Monte Carlo rules
based on higher order digital nets
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