# Compressed Sensing

## Optimized Projections for Compressed Sensing via Direct Mutual Coherence Minimization

We propose to find an optimal projection matrix by minimizing the mutual coherence of PD directly to recover the signal from a small number m of measurements.

## Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm

We propose to use a family of nonconvex surrogates of L 0 -norm on the singular values of a matrix to approximate the rank function.

## A comparison of typical ℓp minimization algorithms

We review some typical algorithms,Iteratively Reweighted ℓ1 minimization(IRL1),Iteratively Reweighted Least Squares(IRLS) (and its general form General Iteratively Reweighted Least Squares(GIRLS)), and Iteratively Thresholding Method(ITM), forℓp minimization and do comprehensive comparison among them, in which IRLS is identified as having the bestperformance and being the fastest as well.