Intelligent greedy pursuit model for sparse reconstruction based on l0 minimization

Abstract

l 0 minimization based sparse reconstruction is an NP-hard problem with very high computational complexity, which is difficult to be achieved by traditional algorithms. Although greedy algorithm aims at solving l 0 minimization, it is more likely to obtain a sub-optimal solution. In this paper, we propose an intelligent greedy pursuit (IGP) algorithm to solve the l 0 minimization essentially. Firstly, we propose a novel optimization function for the sparse reconstruction problem with the sparsity level unknown as a prior. Then, a two-cycle optimization algorithm is designed, whose object is to estimate the support collection and its corresponding coefficients intelligently and accurately by searching for the global optimal solution. To this end, we take advantage of intelligent optimization algorithm in global searching and solving combinatorial optimization problems to guide the intelligent estimation. Also, the principle of estimation is designed by the matching strategies of greedy algorithm which performs quite well in reconstruction speed. The so-called IGP model is as simple as greedy algorithm, while it has been proved through experiments that the performance of IGP for signal reconstruction and image reconstruction outperforms the state-of-the-art reconstruction algorithms. Moreover, IGP can reconstruct signal accurately with a relatively small measurement rate.

Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction

“…In this section, numerical experiments on simulated signals, benchmark problems and images are provided to compare the reconstruction quality between the proposed ADEA and other state-of-the-art algorithms, i.e. basis pursuit (BP) [10], orthogonal matching pursuit (OMP) [7], Homotopy [27], fast iterative shrinkage-thresholding algorithm (FISTA) [42], StEMO [30] and LBEA [44]. Among them, BP, OMP, Homotopy and FISTA belong to single-objective SR algorithms, and StEMO, LBEA and ADEA are MOSR algorithms.…”

An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing

“…Gaussian random matrix [ 1 ], partial Fourier matrix [ 3 ], Bernoulli random matrix [ 2 ], and so on can be used as measurement matrix. Greedy pursuit algorithms [ 4 – 7 ], l 1 minimization algorithms [ 8 – 10 ], and intelligent optimal algorithms [ 11 – 13 ] are proposed to recover x from y .…”