This paper proposes a novel sparsity adaptive simulated annealing algorithm to solve the issue of sparse recovery. This algorithm combines the advantage of the sparsity adaptive matching pursuit (SAMP) algorithm and the simulated annealing method in global searching for the recovery of the sparse signal. First, we calculate the sparsity and the initial support collection as the initial search points of the proposed optimization algorithm by using the idea of SAMP. Then, we design a two-cycle reconstruction method to find the support sets efficiently and accurately by updating the optimization direction. Finally, we take advantage of the sparsity adaptive simulated annealing algorithm in global optimization to guide the sparse reconstruction. The proposed sparsity adaptive greedy pursuit model has a simple geometric structure, it can get the global optimal solution, and it is better than the greedy algorithm in terms of recovery quality. Our experimental results validate that the proposed algorithm outperforms existing state-of-the-art sparse reconstruction algorithms.

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global optimal solution sparsity adaptive matching pursuit samp algorithm support collection global searching for the recovery of the sparse signal optimization algorithm optimization direction stateoftheart sparse reconstruction algorithms twocycle reconstruction method

Yangyang Li,Jianping Zhang,Guiling Sun,Dongxue Lu,.The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing. 2019 (),.

Yangyang Li,Jianping Zhang,Guiling Sun,Dongxue Lu,"The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing" 2019

Yangyang Li,Jianping Zhang,Guiling Sun,Dongxue Lu,"The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing"