Nearly Optimal Sparse Fourier Transform
  We consider the problem of computing the k-sparse approximation to the discrete Fourier transform of an n-dimensional signal. We show:   * An O(k log n)-time randomized algorithm for the case where the input signal has at most k non-zero Fourier coefficients, and   * An O(k log n log(n/k))-time randomized algorithm for general input signals.   Both algorithms achieve o(n log n) time, and thus improve over the Fast Fourier Transform, for any k = o(n). They are the first known algorithms that satisfy this property. Also, if one assumes that the Fast Fourier Transform is optimal, the algorithm for the exactly k-sparse case is optimal for any k = n^{\Omega(1)}.   We complement our algorithmic results by showing that any algorithm for computing the sparse Fourier transform of a general signal must use at least \Omega(k log(n/k)/ log log n) signal samples, even if it is allowed to perform adaptive sampling. 
Haitham Hassanieh, Piotr Indyk, Dina Katabi, and Eric Price
cs.DS