Convergence of an annealing algorithm
Mathematical Programming: Series A and B
ACM Transactions on Mathematical Software (TOMS)
Cooling schedules for optimal annealing
Mathematics of Operations Research
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subspace pursuit for compressive sensing signal reconstruction
IEEE Transactions on Information Theory
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Solving the traveling salesman problem with annealing-based heuristics: a computational study
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rank Awareness in Joint Sparse Recovery
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Hi-index | 0.01 |
This paper addresses the sparse recovery problem by l"0 minimization, which is of central importance in the compressed sensing theory. We model the problem as a combinatorial optimization problem and present a novel algorithm termed SASR based on simulated annealing (SA) and some greedy pursuit (GP) algorithms. In SASR, the initial solution is designed using the simple thresholding algorithm, and the generating mechanism is designed using the strategies existed in the subspace pursuit algorithm and the compressed sampling matching pursuit algorithm. On both the random Gaussian data and the face recognition task, the numerical simulation results illustrate the efficiency of SASR. Compared with the existing sparse recovery algorithms, SASR is more efficient in finding global optimums and performs relatively fast in some good cases. That is, SASR inherits the advantage of SA in finding global optimums and the advantage of GP in fast speed to some extent.