A Sparse Recovery Algorithm Based on Arithmetic Optimization
TL;DR: Wang et al. as discussed by the authors proposed a new sparse recovery algorithm based on arithmetic optimization algorithm and combine the ideas of greedy tracking method, which can not only obtain more effective recovery, but also run faster under general conditions of observation number.
read more
Abstract: At present, the sparse recovery problem is mainly solved by convx optimization algorithm and greedy tracking method. However, the former has defects in recovery efficiency and the latter in recovery ability, and neither of them can obtain effective recovery under large sparsity or small observation degree. In this paper, we propose a new sparse recovery algorithm based on arithmetic optimization algorithm and combine the ideas of greedy tracking method. The proposed algorithm uses arithmetic optimization algorithm to solve the sparse coefficient of the signal in the transform domain, so as to reconstruct the original signal. At the same time, the greedy tracking technique is combined to design the initial position of the operator before solving, so that it can be searched better. Experiments show that compared with other methods, the proposed algorithm can not only obtain more effective recovery, but also run faster under general conditions of observation number. At the same time, It can also recover the signal better in the presence of noise.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
References
The Arithmetic Optimization Algorithm
Laith Abualigah,Ali Diabat,Ali Diabat,Seyedali Mirjalili,Mohamed Abd Elaziz,Mohamed Abd Elaziz,Amir H. Gandomi +6 more
TL;DR: Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms.
2.2K
Computational Methods for Sparse Solution of Linear Inverse Problems
Joel A. Tropp,Stephen J. Wright +1 more
- 29 Apr 2010
TL;DR: This paper surveys the major practical algorithms for sparse approximation with specific attention to computational issues, to the circumstances in which individual methods tend to perform well, and to the theoretical guarantees available.
Compressed Sensing Framework for Heart Sound Acquisition in Internet of Medical Things
TL;DR: The proposed approach uses compressed sensing for signal sampling, and a two-stage reconstruction is developed for reconstruction, on which a peak detection technique is developed to identify whether there is a peak in current segment and, if so, its location.
104
Model-Assisted Compressed Sensing for Vibration-Based Structural Health Monitoring
TL;DR: This work explores the feasibility of the rakeness-based compressed sensing approach to tune the sensing mechanism on the second-order statistics of measured data and proposes a novel model-assisted variant (MRak-CS), which is built on a synthetic derivation of the spectral profile of the structure by pivoting on numerical priors.
51
A hybrid simulated annealing thresholding algorithm for compressed sensing
Xu Fengmin,Wang Shanhe +1 more
TL;DR: The experiments and applications show that the proposed hybrid algorithm is global convergence and can be accepted as a solver for signal and image reconstruction problems.
27