Gradient Proj ection for Sparse Reconstruction-Barzilai-Borwein Algorithm Based on Particle Swarm Optimization
LIANG Dan-ya
LI Hong-wei
WANG Ke
QU Kun
Abstract：In order to decrease the running time,the number of iteration and effectively improve the recon-struction performance of Gradient Proj ection for Sparse reconstruction-Barzilai-Borwein algorithm,Particle Swarm Optimization which has the global search ability is introduced in it.Using PSO's global development ability and the local search ability of GPSR-BB algorithm,the convergence speed is increased and the run-ning time is reduced.By the improvement of algorithm line search conditions,the reconstruction precision is improved effectively.Simulation results show that the improved GPSR-BB algorithm is shorter than the traditional algorithm by 43% in running time and by 39.7% in number of iteration.With the condition of a certain measurement dimension,the improved GPSR-BB algorithm is higher than the traditional one by 0.04 in average probability of success and lower than the traditional one by 0.09 in reconstruction error.
Keywords：Compressed sensing (CS)Signal reconstructionGPSR-BB algorithmPSO algorithm
Publication Date：2015-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 81-84 )