A sparse multidimensional FFT for real positive vectors

A sparse multidimensional FFT for real positive vectors

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 numbe...

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Veröffentlicht in:arXiv.org 2016-12
Hauptverfasser: Pierre-David Letourneau, Harper Langston, Meister, Benoit, Lethin, Richard
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
International markets
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Zusammenfassung: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.
ISSN:2331-8422