Compressive Phase Retrieval via Reweighted Amplitude Flow
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Gang Wang | Georgios B. Giannakis | Jie Chen | Liang Zhang | G. Giannakis | G. Wang | Liang Zhang | Jie Chen
[1] H. Sahinoglou,et al. On phase retrieval of finite-length sequences using the initial time sample , 1991 .
[2] Yuxin Chen,et al. Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems , 2015, NIPS.
[3] Emmanuel J. Candès,et al. PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming , 2011, ArXiv.
[4] Yonina C. Eldar,et al. Phase Retrieval: An Overview of Recent Developments , 2015, ArXiv.
[5] Feng Ruan,et al. Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval , 2017, Information and Inference: A Journal of the IMA.
[6] J. Tropp,et al. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.
[7] Alexandre d'Aspremont,et al. Phase recovery, MaxCut and complex semidefinite programming , 2012, Math. Program..
[8] Yonina C. Eldar,et al. Phase Retrieval via Matrix Completion , 2011, SIAM Rev..
[9] Volkan Cevher,et al. Model-Based Compressive Sensing , 2008, IEEE Transactions on Information Theory.
[10] Gang Wang,et al. Solving large-scale systems of random quadratic equations via stochastic truncated amplitude flow , 2016, 2017 25th European Signal Processing Conference (EUSIPCO).
[11] Chinmay Hegde,et al. Sample-Efficient Algorithms for Recovering Structured Signals From Magnitude-Only Measurements , 2017, IEEE Transactions on Information Theory.
[12] Vahid Tarokh,et al. Sparse Signal Recovery from a Mixture of Linear and Magnitude-Only Measurements , 2015, IEEE Signal Processing Letters.
[13] Yonina C. Eldar,et al. GESPAR: Efficient Phase Retrieval of Sparse Signals , 2013, IEEE Transactions on Signal Processing.
[14] Andrea Montanari,et al. Fundamental Limits of Weak Recovery with Applications to Phase Retrieval , 2017, COLT.
[15] Gang Wang,et al. Randomized Block Frank–Wolfe for Convergent Large-Scale Learning , 2016, IEEE Transactions on Signal Processing.
[16] Xiaodong Li,et al. Phase Retrieval via Wirtinger Flow: Theory and Algorithms , 2014, IEEE Transactions on Information Theory.
[17] J R Fienup,et al. Reconstruction of an object from the modulus of its Fourier transform. , 1978, Optics letters.
[18] Tengyao Wang,et al. A useful variant of the Davis--Kahan theorem for statisticians , 2014, 1405.0680.
[19] Vladislav Voroninski,et al. An Elementary Proof of Convex Phase Retrieval in the Natural Parameter Space via the Linear Program PhaseMax , 2016, ArXiv.
[20] Xiaodong Li,et al. Optimal Rates of Convergence for Noisy Sparse Phase Retrieval via Thresholded Wirtinger Flow , 2015, ArXiv.
[21] Allen Y. Yang,et al. CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem , 2012, NIPS.
[22] Gang Wang,et al. Solving Most Systems of Random Quadratic Equations , 2017, NIPS.
[23] Yonina C. Eldar,et al. Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow , 2016, IEEE Transactions on Information Theory.
[24] Yuejie Chi,et al. Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations , 2016 .
[25] J R Fienup,et al. Phase retrieval algorithms: a comparison. , 1982, Applied optics.
[26] Gang Wang,et al. Solving large-scale systems of random quadratic equations via stochastic truncated amplitude flow , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).
[27] Gang Wang,et al. Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow , 2016, NIPS.
[28] Mahdi Soltanolkotabi,et al. Structured Signal Recovery From Quadratic Measurements: Breaking Sample Complexity Barriers via Nonconvex Optimization , 2017, IEEE Transactions on Information Theory.
[29] Vladislav Voroninski,et al. Compressed Sensing from Phaseless Gaussian Measurements via Linear Programming in the Natural Parameter Space , 2016, ArXiv.
[30] Chinmay Hegde,et al. Towards Sample-Optimal Methods for Solving Random Quadratic Equations with Structure , 2018, 2018 IEEE International Symposium on Information Theory (ISIT).
[31] Chinmay Hegde,et al. Phase Retrieval Using Structured Sparsity: A Sample Efficient Algorithmic Framework , 2017, ArXiv.
[32] Yingbin Liang,et al. A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms , 2017, J. Mach. Learn. Res..
[33] Yuxin Chen,et al. Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution , 2017, Found. Comput. Math..
[34] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[35] Yuxin Chen,et al. Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion , 2018, ICML.
[36] R. Gerchberg. A practical algorithm for the determination of phase from image and diffraction plane pictures , 1972 .
[37] Gang Wang,et al. Sparse Phase Retrieval via Truncated Amplitude Flow , 2016, IEEE Transactions on Signal Processing.
[38] Prateek Jain,et al. Phase Retrieval Using Alternating Minimization , 2013, IEEE Transactions on Signal Processing.
[39] Justin Romberg,et al. Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation , 2016, AISTATS.
[40] Tom Goldstein,et al. PhaseMax: Convex Phase Retrieval via Basis Pursuit , 2016, IEEE Transactions on Information Theory.
[41] Roman Vershynin,et al. Introduction to the non-asymptotic analysis of random matrices , 2010, Compressed Sensing.