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We present an improved Adaptive Matching Pursuit algorithm for computing approximate sparse solutions for overde-termined systems of equations. The algorithms use a greedy approach, based on a neighbor permutation, to select the ordered support positions followed by a cyclical optimization of the selected coefficients. The sparsity level of the solution is… (More)

Randomized coordinate descent (RCD), attractive for its ro-bustness and ability to cope with large scale problems, is here investigated for the first time in an adaptive context. We present an RCD adaptive algorithm for finding sparse least-squares solutions to linear systems, in particular for FIR channel identification. The algorithm has low and tunable… (More)

Based on the iterated cyclic adaptive matching pursuit algorithm , we construct a low complexity approximate variant for finding sparse solutions to systems of linear equations. We employ a greedy neighbor permutation strategy coupled with an approximate scalar product matrix to ensure that the complexity of the algorithm remains low. The sparse solution is… (More)

We present a sliding window RLS for sparse filters, based on the greedy least squares algorithm. The algorithm adapts a partial QR factorization with pivoting, using a simplified search of the filter support that relies on a neighbor permutation technique. For relatively small window size, the proposed algorithm has a lower complexity than recent… (More)

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