Work

A sparse multidimensional FFT for real positive vectors

Year: 2016

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1604.06682

Authors: Letourneau, Pierre-David, Langston, Harper, Meister, Benoit, Lethin, Richard

Language: English

Topic: Mathematical Analysis and Transform Methods

Subfield: Applied Mathematics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2345050517

Cites: 25

Cited by: 2

Related to: 10