685 research outputs found
The solution of multi-scale partial differential equations using wavelets
Wavelets are a powerful new mathematical tool which offers the possibility to
treat in a natural way quantities characterized by several length scales. In
this article we will show how wavelets can be used to solve partial
differential equations which exhibit widely varying length scales and which are
therefore hardly accessible by other numerical methods. As a benchmark
calculation we solve Poisson's equation for a 3-dimensional Uranium dimer. The
length scales of the charge distribution vary by 4 orders of magnitude in this
case. Using lifted interpolating wavelets the number of iterations is
independent of the maximal resolution and the computational effort therefore
scales strictly linearly with respect to the size of the system
Wavelets and Fast Numerical Algorithms
Wavelet based algorithms in numerical analysis are similar to other transform
methods in that vectors and operators are expanded into a basis and the
computations take place in this new system of coordinates. However, due to the
recursive definition of wavelets, their controllable localization in both space
and wave number (time and frequency) domains, and the vanishing moments
property, wavelet based algorithms exhibit new and important properties.
For example, the multiresolution structure of the wavelet expansions brings
about an efficient organization of transformations on a given scale and of
interactions between different neighbouring scales. Moreover, wide classes of
operators which naively would require a full (dense) matrix for their numerical
description, have sparse representations in wavelet bases. For these operators
sparse representations lead to fast numerical algorithms, and thus address a
critical numerical issue.
We note that wavelet based algorithms provide a systematic generalization of
the Fast Multipole Method (FMM) and its descendents.
These topics will be the subject of the lecture. Starting from the notion of
multiresolution analysis, we will consider the so-called non-standard form
(which achieves decoupling among the scales) and the associated fast numerical
algorithms. Examples of non-standard forms of several basic operators (e.g.
derivatives) will be computed explicitly.Comment: 32 pages, uuencoded tar-compressed LaTeX file. Uses epsf.sty (see
`macros'
Multiscale Computation with Interpolating Wavelets
Multiresolution analyses based upon interpolets, interpolating scaling
functions introduced by Deslauriers and Dubuc, are particularly well-suited to
physical applications because they allow exact recovery of the multiresolution
representation of a function from its sample values on a finite set of points
in space. We present a detailed study of the application of wavelet concepts to
physical problems expressed in such bases. The manuscript describes algorithms
for the associated transforms which, for properly constructed grids of variable
resolution, compute correctly without having to introduce extra grid points. We
demonstrate that for the application of local homogeneous operators in such
bases, the non-standard multiply of Beylkin, Coifman and Rokhlin also proceeds
exactly for inhomogeneous grids of appropriate form. To obtain less stringent
conditions on the grids, we generalize the non-standard multiply so that
communication may proceed between non-adjacent levels. The manuscript concludes
with timing comparisons against naive algorithms and an illustration of the
scale-independence of the convergence rate of the conjugate gradient solution
of Poisson's equation using a simple preconditioning, suggesting that this
approach leads to an O(n) solution of this equation.Comment: 33 pages, figures available at
http://laisla.mit.edu/muchomas/Papers/nonstand-figs.ps . Updated: (1) figures
file (figs.ps) now appear with the posting on the server; (2) references got
lost in the last submissio
Fast and accurate con-eigenvalue algorithm for optimal rational approximations
The need to compute small con-eigenvalues and the associated con-eigenvectors
of positive-definite Cauchy matrices naturally arises when constructing
rational approximations with a (near) optimally small error.
Specifically, given a rational function with poles in the unit disk, a
rational approximation with poles in the unit disk may be obtained
from the th con-eigenvector of an Cauchy matrix, where the
associated con-eigenvalue gives the approximation error in the
norm. Unfortunately, standard algorithms do not accurately compute
small con-eigenvalues (and the associated con-eigenvectors) and, in particular,
yield few or no correct digits for con-eigenvalues smaller than the machine
roundoff. We develop a fast and accurate algorithm for computing
con-eigenvalues and con-eigenvectors of positive-definite Cauchy matrices,
yielding even the tiniest con-eigenvalues with high relative accuracy. The
algorithm computes the th con-eigenvalue in operations
and, since the con-eigenvalues of positive-definite Cauchy matrices decay
exponentially fast, we obtain (near) optimal rational approximations in
operations, where is the
approximation error in the norm. We derive error bounds
demonstrating high relative accuracy of the computed con-eigenvalues and the
high accuracy of the unit con-eigenvectors. We also provide examples of using
the algorithm to compute (near) optimal rational approximations of functions
with singularities and sharp transitions, where approximation errors close to
machine precision are obtained. Finally, we present numerical tests on random
(complex-valued) Cauchy matrices to show that the algorithm computes all the
con-eigenvalues and con-eigenvectors with nearly full precision
- …