H. H. Bauschke and P. L. Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces, vol.408, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00643354

R. Escalante and M. Raydan, Alternating Projection Methods; SIAM: Philadelphia, 2011.

L. Gurin, B. T. Polyak, and È. V. Raik, The method of projections for finding the common point of convex sets, Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, vol.7, pp.1211-1228, 1967.

N. Ottavy, Strong convergence of projection-like methods in Hilbert spaces, J. Optim. Theory Appl, vol.56, pp.433-461, 1988.

P. L. Combettes and H. Puh, Iterations of parallel convex projections in Hilbert spaces, Numer. Funct. Anal. Optim, vol.15, pp.225-243, 1994.

K. M. Grigoriadis, Optimal H ? model reduction via linear matrix inequalities: Continuous-and discrete-time cases, Syst. Control Lett, vol.26, pp.321-333, 1995.

M. Babazadeh and A. Nobakhti, Direct Synthesis of Fixed-Order H ? Controllers, IEEE Trans. Autom. Control, vol.60, pp.2704-2709, 2015.

Z. Li, Y. H. Dai, and H. Gao, Alternating projection method for a class of tensor equations, J. Comput. Appl. Math, vol.346, pp.490-504, 2019.

P. Combettes, The convex feasibility problem in image recovery, Advances in Imaging and Electron Physics

A. Krol, S. Li, L. Shen, and Y. Xu, Preconditioned alternating projection algorithms for maximum a posteriori ECT reconstruction, Inverse Probl, vol.28, p.115005, 2012.

G. T. Herman, Fundamentals of Computerized Tomography: Image Reconstruction From Projections, 2009.

G. Mcgibney, M. Smith, S. Nichols, and A. Crawley, Quantitative evaluation of several partial Fourier reconstruction algorithms used in MRI, Magn. Reson. Med, vol.30, pp.51-59, 1993.

F. Ticozzi, L. Zuccato, P. D. Johnson, and L. Viola, Alternating projections methods for discrete-time stabilization of quantum states, IEEE Trans. Autom. Control, vol.63, pp.819-826, 2017.

D. Drusvyatskiy, C. K. Li, D. C. Pelejo, Y. L. Voronin, and H. Wolkowicz, Projection methods for quantum channel construction, vol.14, pp.3075-3096, 2015.

K. M. Grigoriadis and E. B. Beran, Alternating projection algorithms for linear matrix inequalities problems with rank constraints, Advances in Linear Matrix Inequality Methods in Control, vol.SIAM, pp.251-267, 2000.

J. A. Cadzow, Signal enhancement-a composite property mapping algorithm, IEEE Trans. Acoust. Speech Signal Process, vol.36, pp.49-62, 1988.

H. H. Bauschke, P. L. Combettes, and D. R. Luke, Phase retrieval, error reduction algorithm, and Fienup variants: A view from convex optimization, J. Opt. Soc. Am. A, vol.19, pp.1334-1345, 2002.

M. T. Chu, R. E. Funderlic, and R. J. Plemmons, Structured low rank approximation. Linear Algebra Its Appl, vol.366, pp.157-172, 2003.

I. Markovsky and K. Usevich, Structured low-rank approximation with missing data, SIAM J. Matrix Anal. Appl, vol.34, pp.814-830, 2013.

V. Elser,

P. L. Combettes and H. J. Trussell, Method of successive projections for finding a common point of sets in metric spaces, J. Optim. Theory Appl, vol.67, pp.487-507, 1990.

S. Chretien and P. Bondon, Cyclic projection methods on a class of nonconvex sets, Numer. Funct. Anal. Optim, vol.17, pp.37-56, 1996.

S. Chrétien, Methodes de projection pour l'optimisation ensembliste non convexe, Sciences Po, 1996.

H. H. Bauschke and J. M. Borwein, On projection algorithms for solving convex feasibility problems. SIAM Rev, vol.38, pp.367-426, 1996.

Y. Censor, W. Chen, P. L. Combettes, R. Davidi, and G. T. Herman, On the effectiveness of projection methods for convex feasibility problems with linear inequality constraints, Comput. Optim. Appl, vol.51, pp.1065-1088, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00643783

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms. Phys. D Nonlinear Phenom, vol.60, pp.259-268, 1992.

V. Michael and . Partial, Fourier Reconstruction with POCS, p.13, 2020.

L. Condat, Discrete total variation: New definition and minimization, SIAM J. Imaging Sci, vol.10, pp.1258-1290, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01309685

G. Plonka, D. Potts, G. Steidl, and M. Tasche, Numerical Fourier Analysis: Theory and Applications; Book Manuscript, 2018.

A. Sarray, B. Chrétien, S. Clarkson, P. Cottez, and G. , Enhancing Prony's method by nuclear norm penalization and extension to missing data. Signal Image Video Process, vol.11, pp.1089-1096, 2017.

E. Barton, B. Al-sarray, S. Chrétien, and K. Jagan, Decomposition of Dynamical Signals into Jumps, Oscillatory Patterns, and Possible Outliers. Mathematics, vol.6, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02515951

A. Moitra, Super-resolution, extremal functions and the condition number of Vandermonde matrices, Proceedings of the Forty-Seventh Annual ACM Symposium on Theory Of Computing, pp.821-830, 2015.

S. Chrétien and H. Tyagi, Multi-kernel unmixing and super-resolution using the Modified Matrix Pencil method, J. Fourier Anal. Appl, vol.26, 2020.

F. Bach, On the Unreasonable Effectiveness of Richardson Extrapolation, p.13, 2020.

S. S. Dragomir, A generalisation of the Cassels and Greub-Reinboldt inequalities in inner product spaces, 2003.

C. P. Niculescu, Converses of the Cauchy-Schwartz Inequality in the C*-Framework, RGMIA Research Report Collection, vol.4, 2001.