And for convex relaxation à la basis pursuit

Following on the heels of the previous post, post previous to that, and the post previous to that, I am now comparing recovery performance of \(\ell_1\) minimization, and cyclic MP. Click on the image below to see the results.

The story is the same: sparse vectors with elements distributed Normal and Laplacian appear more recoverable using greedy methods like OMP and CMP. For vectors with elements distributed Bernoulli, bimodal Gaussian and uniform, convex relaxation has the advantage.


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