Phase Retrieval with Application to Optical Imaging: A contemporary overview, IEEE Signal Processing Magazine, vol.32, issue.3, pp.87-109, 2015. ,
DOI : 10.1109/MSP.2014.2352673
A practical algorithm for the determination of phase from image and diffraction plane pictures, Optik, vol.35, pp.237-246, 1972. ,
Phase retrieval algorithms: a comparison, Applied Optics, vol.21, issue.15, pp.2758-2769, 1982. ,
DOI : 10.1364/AO.21.002758
URL : http://digitus.itk.ppke.hu/~matyi/optika/Phase_Diversity/AO82_PRComparison1.pdf
Array imaging using intensity-only measurements, Inverse Problems, vol.27, issue.1, 2011. ,
DOI : 10.1088/0266-5611/27/1/015005
URL : http://math.stanford.edu/~papanico/pubftp/mri_V10.pdf
PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming, Communications on Pure and Applied Mathematics, vol.38, issue.5, pp.1241-1274, 2013. ,
DOI : 10.1109/9.554402
Solving Quadratic Equations via PhaseLift When There Are About as Many Equations as Unknowns, Foundations of Computational Mathematics, vol.66, issue.1, pp.1017-1026, 2014. ,
DOI : 10.1017/CBO9780511794308.006
Phase retrieval from coded diffraction patterns, Applied and Computational Harmonic Analysis, vol.39, issue.2, pp.277-299, 2015. ,
DOI : 10.1016/j.acha.2014.09.004
A Partial Derandomization of PhaseLift Using Spherical Designs, Journal of Fourier Analysis and Applications, vol.12, issue.2, pp.229-266, 2015. ,
DOI : 10.1007/s10208-011-9099-z
Estimating a signal from a magnitude spectrogram via convex optimization, Audio Engineering Society 133rd Convention, 2012. ,
Phase recovery, MaxCut and complex semidefinite programming, Mathematical Programming, pp.47-81, 2015. ,
DOI : 10.1137/04061341X
URL : https://hal.archives-ouvertes.fr/hal-00907535
The local convexity of solving systems of quadratic equations, 2015. ,
Phase Retrieval Using Alternating Minimization, Advances in Neural Information Processing Systems, pp.1796-2804, 2013. ,
DOI : 10.1109/TSP.2015.2448516
URL : http://arxiv.org/pdf/1306.0160.pdf
Phase Retrieval via Wirtinger Flow: Theory and Algorithms, IEEE Transactions on Information Theory, vol.61, issue.4, 1985. ,
DOI : 10.1109/TIT.2015.2399924
Solving random quadratic systems of equations is nearly as easy as solving linear systems Phase retrieval via incremental truncated Wirtinger flow ,
Reshaped Wirtinger flow for solving quadratic systems of equations, Advances in Neural Information Processing Systems 29, 2016. ,
Solving random systems of quadratic equations via truncated generalized gradient flow, 2016. ,
DOI : 10.1109/tit.2017.2756858
On Local Convergence of the Method of Alternating Projections, Foundations of Computational Mathematics, vol.11, issue.3, pp.425-455, 2016. ,
DOI : 10.1007/978-1-4612-2008-4
Phase Retrieval with One or Two Diffraction Patterns by Alternating Projections with the Null Initialization, Journal of Fourier Analysis and Applications, vol.13, issue.3, 2016. ,
DOI : 10.4310/CMS.2015.v13.n4.a10
Algorithms and theory for clustering and nonconvex quadratic programming, 2014. ,
A geometric analysis of phase retrieval, 2016. ,
Phase retrieval with random gaussian sensing vectors by alternating projections, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01645081
On signal reconstruction without phase, Applied and Computational Harmonic Analysis, vol.20, issue.3, pp.345-356, 2006. ,
DOI : 10.1016/j.acha.2005.07.001
URL : https://doi.org/10.1016/j.acha.2005.07.001
An algebraic characterization of injectivity in phase retrieval, Applied and Computational Harmonic Analysis, vol.38, issue.2, pp.346-356, 2015. ,
DOI : 10.1016/j.acha.2014.06.005
Local Operator Theory, Random Matrices and Banach Spaces, Handbook of the geometry of Banach spaces, pp.317-366, 2001. ,
DOI : 10.1016/S1874-5849(01)80010-3
Gauss-Newton method for phase retrieval, 2016. ,
Spectral initialization for nonconvex estimation: High-dimensional limit and phase transitions, 2017 IEEE International Symposium on Information Theory (ISIT), 2017. ,
DOI : 10.1109/ISIT.2017.8007083
Global optimality of local search for low rank matrix recovery, Advances in Neural Information Processing Systems 29, 2016. ,
Matrix completion has no spurious local minimum No spurious local minima in nonconvex low rank problems: a unified geometric analysis, Advances in Neural Information Processing Systems 29 to appear in the Proceedings of the 34nd International Conference on Machine Learning, 2016. ,
Nonconvex Phase Synchronization, SIAM Journal on Optimization, vol.26, issue.4, pp.2355-2377, 2016. ,
DOI : 10.1137/16M105808X
An elementary proof of a theorem of Johnson and Lindenstrauss, Random Structures and Algorithms, vol.15, issue.1, pp.60-65, 2003. ,
DOI : 10.1002/rsa.10073