  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.
