Fast Adaptive Algorithms in the Non-Standard Form for Multidimensional Problems

Mathematics – Numerical Analysis

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

37 pages, 7 figures. Revised version (reorganized for clarity) accepted for publication, Appl. Comput. Harmon. Anal

Scientific paper

We present a fast, adaptive multiresolution algorithm for applying integral operators with a wide class of radially symmetric kernels in dimensions one, two and three. This algorithm is made efficient by the use of separated representations of the kernel. We discuss operators of the class $(-\Delta+\mu^{2}I)^{-\alpha}$, where $\mu\geq0$ and $0<\alpha<3/2$, and illustrate the algorithm for the Poisson and Schr\"{o}dinger equations in dimension three. The same algorithm may be used for all operators with radially symmetric kernels approximated as a weighted sum of Gaussians, making it applicable across multiple fields by reusing a single implementation. This fast algorithm provides controllable accuracy at a reasonable cost, comparable to that of the Fast Multipole Method (FMM). It differs from the FMM by the type of approximation used to represent kernels and has an advantage of being easily extendable to higher dimensions.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Fast Adaptive Algorithms in the Non-Standard Form for Multidimensional Problems does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Fast Adaptive Algorithms in the Non-Standard Form for Multidimensional Problems, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Fast Adaptive Algorithms in the Non-Standard Form for Multidimensional Problems will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-356559

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.