[1] Chen S, Billings S, Luo W. Orthogonal least squares methods and their application to non-linear system identification. Int J Control, 1989, 50(5):1873-1896 [2] Foucart S. Stability and robustness of weak orthogonal matching pursuits//Recent Adv Harmonic Anal and App. Springer, 2013:395-405 [3] Geng P, Chen W, Ge H. Perturbation analysis of orthogonal least squares. Canadian Mathematical Bulletin, 2019, 62(4):780-797 [4] Herzet C, Soussen C, Idier J, Gribonval R. Exact recovery conditions for sparse representations with partial support information. IEEE Trans Inform Theory, 2013, 59(11):7509-7524 [5] Herzet C, Drémeau A, Soussen C. Relaxed recovery conditions for OMP/OLS by exploiting both coherence and decay. IEEE Transactions on Information Theory, 2015, 62(1):459-470 [6] Wang J, Li P. Recovery of sparse signals using multiple orthogonal least squares. IEEE Trans. Signal Process, 2017, 65(8):2049-2062 [7] Li H, Zhang J, Zou J. Improving the bound on the restricted isometry property constant in multiple orthogonal least squares. IET Signal Processing, 2018, 12(5):666-671 [8] Kim J, Shim B. A Near-Optimal Restricted Isometry Condition of Multiple Orthogonal Least Squares. IEEE Access, 2019, 7:46822-46830 [9] Wen J, Wang J, Zhang Q. Nearly optimal bounds for orthogonal least squares. IEEE Trans Signal Process, 2017, 65(20):5347-5356 [10] Chen W, Li Y. Stable recovery of signals with the high order D-RIP condition. Acta Matematica Scientia, 2016, 36(6):1721-1730 [11] Abdillahi-Ali D, Azzaout N, Guillin A, Le Mailloux G, Matsui T. Penalized least square in sparse setting with convex penalty and non Gaussian errors. Acta Matematica Scientia, 2021, 41(6):2198-2216 [12] Li H, Liu G. An improved analysis for support recovery with orthogonal matching pursuit under general perturbations. IEEE Access, 2018, 6:18856-18867 [13] Pati Y, Rezaiifar R, Krishnaprasad P. Orthogonal matching pursuit:Recursive function approximation with applications to wavelet decomposition//Proc 27th Annu. Asilomar Conf Signals, Systems, and Computers. IEEE, Pacific Grove, CA, 1993, 1:40-44 [14] Chen W, Ge H. A sharp recovery condition for block sparse signals by block orthogonal multi-matching pursuit. Science China Mathematics, 2017, 60(7):1325-1340 [15] Dan W, Wang R. Robustness of orthogonal matching pursuit under restricted isometry property. Science China Mathematics, 2014, 57(3):627-634 [16] Zhang T. Sparse recovery with orthogonal matching pursuit under RIP. IEEE Transactions on Information Theory, 2011, 57(9):6215-6221 [17] Wang J, Li P, Shim B. Exact recovery of sparse signals using orthogonal matching pursuit:How many iterations do we need?. IEEE Transactions on Signal Processing, 2016, 64(16):4194-4202 [18] Candes E, Tao T. Decoding by linear programming. IEEE Trans Inf Theory, 2005, 51(12):4203-4215 [19] Baraniuk R, Davenport M, DeVore R, Wakin M. A simple proof of the restricted isometry property for random matrices. Construct Approx, 2008, 28(3):253-263 [20] Blumensath T, Davies M. On the difference between orthogonal matching pursuit and orthogonal least squares//Technical Report. Southampton:University of Southampton, 2007. https://eprints.soton.ac.uk/142469/1/BDOMPvsOLS07.pdf. [21] Soussen C, Gribonval R, Idier J, Herzet C. Joint k-step analysis of orthogonal matching pursuit and orthogonal least squares. IEEE Trans Inf Theory, 2013, 59(5):3158-3174 [22] Wang J, Kwon S, Li P, Shim B. Recovery of sparse signals via generalized orthogonal matching pursuit:A new analysis. IEEE Transactions on Signal Process, 2016, 64(4):1076-1089 [23] Kim J, Wang J, Nguyen L, Shim B. Joint sparse recovery using signal space matching pursuit. IEEE Transactions on Information Theory, 2020, 66(8):5072-5096 [24] Kim J, Wang J, Shim B. Optimal restricted isometry condition of normalized sampling matrices for exact sparse recovery with orthogonal least squares. IEEE Transaction on Signal Processing, 2021, 69(1):1521-1536 |