Minimization of $ell_{1-2}$ for Compressed Sensing

We study minimization of the difference of $ell_1$ and $ell_2$ norms as a nonconvex and Lipschitz continuous metric for solving constrained and unconstrained compressed sensing problems. We establish exact (stable) sparse recovery results under a restricted isometry property (RIP) condition for the constrained problem, and a full-rank theorem of the sensing matrix restricted to the support of the sparse solution. We present an iterative method for $ell_{1-2}$ minimization based on the difference of convex functions algorithm and prove that it converges to a stationary point satisfying the first-order optimality condition. We propose a sparsity oriented simulated annealing procedure with non-Gaussian random perturbation and prove the almost sure convergence of the combined algorithm (DCASA) to a global minimum. Computation examples on success rates of sparse solution recovery show that if the sensing matrix is ill-conditioned (non RIP satisfying), then our method is better than existing nonconvex compressed sensing solvers in the literature. Likewise in the magnetic resonance imaging (MRI) phantom image recovery problem, $ell_{1-2}$ succeeds with eight projections. Irrespective of the conditioning of the sensing matrix, $ell_{1-2}$ is better than $ell_1$ in both the sparse signal and the MRI phantom image recovery problems.