A Sparse Recovery Algorithm Based on Particle Swarm Optimization

Sparse recovery is a hot topic around the areas of international mathematics and information processing at present, and it is mainly solved by two major strategies including convex relaxation methods and greedy pursuit methods. However, considering the former on efficiency and the latter on ability, they own shortcomings respectively, and neither can recover Gaussian signals with large sparsity level or small measurement level effectively. In this paper, a new sparse recovery algorithm propose is proposed and based on particle swarm optimization combining with the thought of greedy pursuit methods. It is demonstrated by a series of numerical simulations that when compared to other methods, the proposed algorithm could not only achieve better recovery performance, but also runs relatively fast when recovering Gaussian signals with normal sparsity level or normal measurement level.