A sparse multidimensional FFT for real positive vectors
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 lowlevel noise and exhibits an exponentially small probability of failure.
 Publication:

arXiv eprints
 Pub Date:
 April 2016
 arXiv:
 arXiv:1604.06682
 Bibcode:
 2016arXiv160406682L
 Keywords:

 Computer Science  Data Structures and Algorithms
 EPrint:
 Fixed minor typos. Corrected use of Q^{1} in Algorithm 3 and theorem