10,415 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
Stochastic collocation on unstructured multivariate meshes
Collocation has become a standard tool for approximation of parameterized
systems in the uncertainty quantification (UQ) community. Techniques for
least-squares regularization, compressive sampling recovery, and interpolatory
reconstruction are becoming standard tools used in a variety of applications.
Selection of a collocation mesh is frequently a challenge, but methods that
construct geometrically "unstructured" collocation meshes have shown great
potential due to attractive theoretical properties and direct, simple
generation and implementation. We investigate properties of these meshes,
presenting stability and accuracy results that can be used as guides for
generating stochastic collocation grids in multiple dimensions.Comment: 29 pages, 6 figure
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
Tractability of multivariate analytic problems
In the theory of tractability of multivariate problems one usually studies
problems with finite smoothness. Then we want to know which -variate
problems can be approximated to within by using, say,
polynomially many in and function values or arbitrary
linear functionals.
There is a recent stream of work for multivariate analytic problems for which
we want to answer the usual tractability questions with
replaced by . In this vein of research, multivariate
integration and approximation have been studied over Korobov spaces with
exponentially fast decaying Fourier coefficients. This is work of J. Dick, G.
Larcher, and the authors. There is a natural need to analyze more general
analytic problems defined over more general spaces and obtain tractability
results in terms of and .
The goal of this paper is to survey the existing results, present some new
results, and propose further questions for the study of tractability of
multivariate analytic questions
Matrix-free weighted quadrature for a computationally efficient isogeometric -method
The -method is the isogeometric method based on splines (or NURBS, etc.)
with maximum regularity. When implemented following the paradigms of classical
finite element methods, the computational resources required by the method
are prohibitive even for moderate degree. In order to address this issue, we
propose a matrix-free strategy combined with weighted quadrature, which is an
ad-hoc strategy to compute the integrals of the Galerkin system. Matrix-free
weighted quadrature (MF-WQ) speeds up matrix operations, and, perhaps even more
important, greatly reduces memory consumption. Our strategy also requires an
efficient preconditioner for the linear system iterative solver. In this work
we deal with an elliptic model problem, and adopt a preconditioner based on the
Fast Diagonalization method, an old idea to solve Sylvester-like equations. Our
numerical tests show that the isogeometric solver based on MF-WQ is faster than
standard approaches (where the main cost is the matrix formation by standard
Gaussian quadrature) even for low degree. But the main achievement is that,
with MF-WQ, the -method gets orders of magnitude faster by increasing the
degree, given a target accuracy. Therefore, we are able to show the
superiority, in terms of computational efficiency, of the high-degree
-method with respect to low-degree isogeometric discretizations. What we
present here is applicable to more complex and realistic differential problems,
but its effectiveness will depend on the preconditioner stage, which is as
always problem-dependent. This situation is typical of modern high-order
methods: the overall performance is mainly related to the quality of the
preconditioner
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