A Sparse Fast Chebyshev Transform for High-Dimensional Approximation
Abstract
We present the Fast Chebyshev Transform (FCT), a fast, randomized algorithm to compute a Chebyshev approximation of functions in high-dimensions from the knowledge of the location of its nonzero Chebyshev coefficients. Rather than sampling a full-resolution Chebyshev grid in each dimension, we randomly sample several grids with varied resolutions and solve a least-squares problem in coefficient space in order to compute a polynomial approximating the function of interest across all grids simultaneously. We theoretically and empirically show that the FCT exhibits quasi-linear scaling and high numerical accuracy on challenging and complex high-dimensional problems. We demonstrate the effectiveness of our approach compared to alternative Chebyshev approximation schemes. In particular, we highlight our algorithm's effectiveness in high dimensions, demonstrating significant speedups over commonly-used alternative techniques.
Cite
@article{arxiv.2309.14584,
title = {A Sparse Fast Chebyshev Transform for High-Dimensional Approximation},
author = {Dalton Jones and Pierre-David Letourneau and Matthew J. Morse and M. Harper Langston},
journal= {arXiv preprint arXiv:2309.14584},
year = {2023}
}