Sparse Recovery by Means of Nonnegative Least Squares

This letter demonstrates that sparse recovery can be achieved by an L1-minimization ersatz easily implemented using a conventional nonnegative least squares algorithm. A connection with orthogonal matching pursuit is also highlighted. The preliminary results call for more investigations on the potential of the method and on its relations to classical sparse recovery algorithms.

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