And finally, a little Probabilistic OMP

Following on the heels of the previous post, and the post previous to that, post previous to that, and the post previous to that, I am now comparing recovery performance of Probabilistic OMP (PrOMP), and straight-up OMP. In my implementation of PrOMP, I limit the number of independent trials to 10. Furthermore, I make \(\epsilon = 0.001\), and I set the number of high probability atoms to 2. The end condition tolerance is the same as for OMP (\(||\vr||/||\vx|| < 10^{-5}\)). Click on the image below to see the results.


PrOMP.jpg
No matter the category of sparse vector distribution, PrOMP is like the Walt Disney of greedy methods: it wins the most Oscars, and apparently has fun doing it too. These results confirm my earlier ones here, and here.

Overall, a highly productive day considering the time change!

If you wish to reproduce these results at home, then use this MATLAB code. (CVX needed.) Let me know of any bugs!

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