Sparse recovery by means of nonnegative least squares - implementation -

Sparse recovery by means of nonnegative least squares by Simon Foucart, David Koslicki

This short note demonstrates that sparse recovery can be achieved by an `1-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.

The implementation is on Simon Foucart's publication page.

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