Alexandru Onose

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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)
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