A sparse multidimensional FFT for real positive vectors
Data Structures and Algorithms
2016-12-08 v6
Abstract
We present a sparse multidimensional FFT (sMFFT) randomized algorithm for real positive vectors. The algorithm works in any fixed dimension, requires (O(R log(R) log(N)) ) samples and runs in O( R log^2(R) log(N)) complexity (where N is the total size of the vector in d dimensions and R is the number of nonzeros). It is stable to low-level noise and exhibits an exponentially small probability of failure.
Cite
@article{arxiv.1604.06682,
title = {A sparse multidimensional FFT for real positive vectors},
author = {Pierre-David Letourneau and Harper Langston and Benoit Meister and Richard Lethin},
journal= {arXiv preprint arXiv:1604.06682},
year = {2016}
}
Comments
Fixed minor typos. Corrected use of Q^{-1} in Algorithm 3 and theorem