Abstract
Recently, iteratively reweighted methods have attracted much interest in compressed sensing, outperforming their unweighted counterparts in most cases. In these methods, decision variables and weights are optimized alternatingly, or decision variables are optimized under heuristically chosen weights. In this paper, we present a novel weighted l1-norm minimization problem for the sparsest solution of underdetermined linear equations. We propose an iteratively weighted thresholding method for this problem, wherein decision variables and weights are optimized simultaneously. Furthermore, we prove that the iteration process will converge eventually. Using the homotopy technique, we enhance the performance of the iteratively weighted thresholding method. Finally, extensive computational experiments show that our method performs better in terms of both running time and recovery accuracy compared with some state-of-the-art methods.
This is a preview of subscription content, access via your institution.
References
- 1
Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci, 2009, 2: 183–202
- 2
Bi S, Liu X, Pan S. Exact penalty decomposition method for zero-norm minimization based on MPEC formulation. SIAM J Sci Comput, 2014, 36: 1451–1477
- 3
Blumensath T, Davies M E. Iterative thresholding for sparse approximations. J Fourier Anal Appl, 2008, 14: 629–654
- 4
Candès E J, Romberg J, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Comm Pure Appl Math, 2006, 59: 1207–1223
- 5
Candès E J, Wakin M B, Boyd S P. Enhancing sparsity by reweighted l1 minimization. J Fourier Anal Appl, 2008, 14: 877–905
- 6
Chartrand R, Yin W. Iteratively reweighted algorithms for compressive sensing. In: IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2008: 3869–3872
- 7
Chen X, Zhou W. Convergence of reweighted l1 minimization algorithm for l2-lp minimization. Comput Optim Appl, 2014, 59: 47–61
- 8
Chen Y C, Wang M D. Worst-case hardness of approximation for sparse optimization with L0 norm. http://www.optimization-online.org/DBFILE/2016/02/5334.pdf, 2016
- 9
Dai W, Milenkovic O. Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans Inform Theory, 2009, 55: 2230–2249
- 10
Donoho D L, Tsaig Y, Drori I, et al. Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit. IEEE Trans Inform Theory, 2012, 58: 1094–1121
- 11
Foucart S, Lai M. Sparsest solutions of underdetermined linear systems via lq-minimization for 0 < q ⩾ 1. Appl Comput Harmon Anal, 2009, 26: 395–407
- 12
Foucat S, Rauhut H. A Mathematical Introduction to Compressive Sensing, Chapter 9: Sparse Recovery with Random Matrices. Basel: Birkhäuser, 2013
- 13
Hale E T, Yin W, Zhang Y. Fixed-point continuation for l1-minimization: Methodology and convergence. SIAM J Optim, 2008, 19: 1107–1130
- 14
Jiao Y, Jin B, Lu X. A primal dual active set with continuation algorithm for the l0-regularized optimization problem. Appl Comput Harmon Anal, 2015, 39: 400–426
- 15
Khajehnejad M A, Xu W, Avestimehr A S, et al. Analyzing weighted l1-norm minimization for sparse recovery with nonuniform sparse models. IEEE Trans Signal Process, 2011, 59: 1985–2001
- 16
Lu Z. Iterative hard thresholding methods for l0 regularized convex cone programming. Math Program, 2014, 147: 125–154
- 17
Lu Z, Zhang Y. Sparse approximation via penalty decomposition methods. SIAM J Optim, 2012, 23: 2448–2478
- 18
Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process, 1993, 41: 3397–3415
- 19
Natarajan B K. Sparse approximate solutions to linear systems. SIAM J Comput, 1995, 24: 227–234
- 20
Needell D, Tropp J A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Appl Comput Harmon Anal, 2009, 26: 301–321
- 21
Needell D, Vershynin R. Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. Found Comput Math, 2009, 9: 317–334
- 22
Rao B D, Kreutz-Delgado K. An affine scaling methodology for best basis selection. IEEE Trans Signal Process, 1999, 47: 187–200
- 23
Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inform Theory, 2007, 53: 4655–4666
- 24
Wang Y, Yin W. Sparse signal reconstruction via iterative support detection. SIAM J Imaging Sci, 2010, 3: 462–491
- 25
Wen Z, Yin W, Goldfard D, et al. A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization, and continuation. SIAM J Sci Comput, 2010, 32: 1832–1857
- 26
Xiao L, Zhang T. A proximal-gradient homotopy method for the sparse least-squares problem. SIAM J Optim, 2013, 23: 1062–1091
- 27
Xu W, Khajehnejad M A, Avestimehr A S, et al. Breaking through the thresholds: An analysis for iterative reweighted l1 minimization via the Grassmann angle framework. In: IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2010, 1–5
- 28
Xu Z, Chang X, Xu F, et al. L1/2 regularization: A thresholding representation theory and a fast solver. IEEE Trans Neural Netw Learn Syst, 2012, 23: 1013–1027
- 29
Yang J, Zhang Y. Alternating direction algorithms for l1-problems in compressive sensing. SIAM J Sci Comput, 2011, 33: 250–278
- 30
Zhao Y B, Li D. Reweighted l1-minimization for sparse solutions to underdetermined linear systems. SIAM J Optim, 2012, 22: 1065–1088
- 31
Zhao Y B, Kočvara M. A new computational method for the sparsest solutions to systems of linear equations. SIAM J Optim, 2015, 25: 1110–1134
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61672005 and 11571074). The authors thank the anonymous reviewers for their helpful suggestions and comments, which helped improve the quality of this manuscript.
Author information
Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhu, W., Huang, Z., Chen, J. et al. Iteratively weighted thresholding homotopy method for the sparse solution of underdetermined linear equations. Sci. China Math. 64, 639–664 (2021). https://doi.org/10.1007/s11425-018-9467-7
Received:
Accepted:
Published:
Issue Date:
Keywords
- sparse optimization
- weighted thresholding method
- homotopy method
MSC(2010)
- 15A06
- 15A29
- 65K05
- 90C25
- 90C26
- 90C59