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[ALMT14] D. Amelunxen, M. Lotz, M.B. McCoy, and J.A. Tropp, Living on the Edge: Phase Transitions in Convex Programs with Random Data, Inf. Inference 3 (2014), no. 3, 224–294.10.1093/imaiai/iau005Search in Google Scholar

[BF85] R.L. Burden and J.D. Faires, Numerical Analysis (3rd ed.), PWS Publishers, 1985.Search in Google Scholar

[CRPW12] V. Chandrasekaran, B. Recht, P.A. Parrilo, and A.S. Willsky, The Convex Geometry of Linear Inverse Problems, Found. Comput. Math. 12 (2012), no. 6, 805–849.10.1007/s10208-012-9135-7Search in Google Scholar

[DJM13] D.L. Donoho, I. Johnstone, and A. Montanari, Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising, IEEE Trans. Inf, Theory 59 (2013), no. 6, 3396–3433.10.1109/TIT.2013.2239356Search in Google Scholar

[FM14] R. Foygel and L. Mackey, Corrupted Sensing: Novel Guarantees for Separating Structured Signals, IEEE Trans. Inf. Theory 60 (2014), no. 2, 1223–1247.10.1109/TIT.2013.2293654Search in Google Scholar

[FR13] S. Foucart and H. Rauhut, A Mathematical Introduction to Compressive Sensing, Birkhäuser, 2013.10.1007/978-0-8176-4948-7Search in Google Scholar

[Gil17] J.C. Gilbert, On the Solution Uniqueness Characterization in the L1 Norm and Polyhedral Gauge Recovery, J Optim. Theory Appl. 172 (2017), no. 1, 70–101.10.1007/s10957-016-1004-0Search in Google Scholar

[HK99] G.T. Herman and A. Kuba, Discrete Tomography: Foundations, Algorithms, and Applications, Applied and Numerical Harmonic Analysis, Birkhäuser, 1999.10.1007/978-1-4612-1568-4Search in Google Scholar

[HSH12] P.C. Hansen and M. Saxild-Hansen, AIR Tools – A MATLAB Package of Algebraic Iterative Reconstruction Methods, J. Comput. Appl. Math. 236 (2012), no. 8, 2167–2178.10.1016/j.cam.2011.09.039Search in Google Scholar

[JKL15] J.S. Jørgensen, C. Kruschel, and D.A Lorenz, Testable Uniqueness Conditions for Empirical Assessment of Undersampling Levels in Total Variation-Regularized X-ray CT, Inverse Probl. Sci. Eng. 23 (2015), no. 8, 1283–1305.10.1080/17415977.2014.986724Search in Google Scholar

[NDEG13] S. Nam, M.E. Davies, M. Elad, and R. Gribonval, The Cosparse Analysis Model and Algorithms, Appl. Comput. Harmon. Anal. 34 (2013), no. 1, 30–56.10.1016/j.acha.2012.03.006Search in Google Scholar

[OH16] S. Oymak and B. Hassibi, Sharp MSE Bounds for Proximal Denoising, Found. Comput. Math. 16 (2016), no. 4, 965–1029.10.1007/s10208-015-9278-4Search in Google Scholar

[PS14] S. Petra and C. Schnörr, Average Case Recovery Analysis of Tomographic Compressive Sensing, Linear Algebra Appl. 441 (2014), 168–198.10.1016/j.laa.2013.06.034Search in Google Scholar

[RLH14] S. Roux, H. Leclerc, and F. Hild, Efficient Binary Tomographic Reconstruction, J. Math. Imaging Vision 49 (2014), no. 2, 335–351.10.1007/s10851-013-0465-0Search in Google Scholar

[Roc70] R.T. Rockafellar, Convex Analysis, Princeton University Press, 1970.10.1515/9781400873173Search in Google Scholar

[RW09] R.T. Rockafellar and R.J.B. Wets, Variational Analysis, Springer, 2009.Search in Google Scholar

[Van14] R.J. Vanderbei, Linear Programming: Foundations and Extensions, Springer, 2014.10.1007/978-1-4614-7630-6Search in Google Scholar

[ZMY16] H Zhang, Y. Ming, and W. Yin, One Condition for Solution Uniqueness and Robustness of both l1-Synthesis and l1-Analysis Minimizations, Adv. Comput. Math. 42 (2016), no. 6, 1381–1399.10.1007/s10444-016-9467-ySearch in Google Scholar

[ZXCL16] B. Zhang, W. Xu, J.F. Cai, and L. Lai Lai, Precise Phase Transition of Total Variation Minimization, 2016 IEEE ICASSP, 2016, pp. 4518–4522.10.1109/ICASSP.2016.7472532Search in Google Scholar

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Mathematics, General Mathematics