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In recent literature, a general two step procedure has been formulated for solving the problem of phase retrieval.
Stochastic Gradient Descent Escapes Saddle Points Efficiently
Jin, C., Netrapalli, P., Ge, R., Kakade, S. M., and Jordan, M. I · 1902
Earlier work this paper cites.
Scalable Solvers of Random Quadratic Equations via Stochastic Truncated Amplitude Flow
Wang, G., Giannakis, G. B., and Chen, J · 1974
Earlier work this paper cites.
Phase Retrieval Algorithms: A Comparison
Fienup, J. R · 1982
Earlier work this paper cites.
Phase Retrieval via Wirtinger Flow: Theory and Algorithms
Candès, E. J., Li, X., and Soltanolkotabi, M · 2007
Earlier work this paper cites.
A Randomized Kaczmarz Algorithm with Exponential Convergence
Strohmer, T., and Vershynin, R · 2009
Earlier work this paper cites.
PhaseLift: Exact and stable signal recovery from magnitude measurements via convex programming
Candès, E. J., Strohmer, T., and Voroninski, V · 2013
Earlier work this paper cites.
Phase retrieval: Stability and recovery guarantees
Eldar, Y. C., and Mendelson, S · 2014
Earlier work this paper cites.
Escaping from saddle points — online stochastic gradient for tensor decomposition
Ge, R., Huang, F., Jin, C., and Yuan, Y · 2015
Earlier work this paper cites.
Phase Retrieval with Application to Optical Imaging: A Contemporary Overview
Shechtman, Y., Eldar, Y. C., Cohen, O., Chapman, H. N., Miao, J., and Segev, M · 2015
Cited alongside, same era.
Solving Systems of Phaseless Equations via Kaczmarz Methods: A Proof of Concept Study
Wei, K · 2015
Cited alongside, same era.
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
Li, C. J., Wang, Z., and Liu, H · 2016
Cited alongside, same era.
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz Algorithm
Needell, D., Srebro, N., and Ward, R · 2016
Cited alongside, same era.
Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations
Zhang, H., Zhou, Y., Liang, Y., and Chi, Y · 2016
Cited alongside, same era.
Stochastic Gradient Descent as Approximate Bayesian Inference
Mandt, S., Hoffman, M. D., and Blei, D. M · 2017
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Solving (Most) of a Set of Quadratic Equalities: Composite Optimization for Robust Phase Retrieval
Duchi, J., and Ruan, F · 2018
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PhaseMax: Convex Phase Retrieval via Basis Pursuit
Goldstein, T., and Studer, C · 2018
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A Mean Field View of the Landscape of Two-layer Neural Networks
Mei, S., Montanari, A., and Nguyen, P. M · 2018
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A Geometric Analysis of Phase Retrieval
Sun, J., Qu, Q., and Wright, J · 2018
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Phase Retrieval via Randomized Kaczmarz: Theoretical Guarantees
Tan, Y. S., and Vershynin, R · 2018
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Phase retrieval meets statistical learning theory: A flexible convex relaxation
Bahmani, S., and Romberg, J · 2017
Cited alongside, same era.
Fourier Phase Retrieval: Uniqueness and Algorithms
Bendory, T., Beinert, R., and Eldar, Y. C · 2017
Cited alongside, same era.
How to Escape Saddle Points Efficiently
Jin, C., Ge, R., Netrapalli, P., Kakade, S. M., and Jordan, M. I · 2017
Cited alongside, same era.
A Convergence Theory for Deep Learning via Over-Parameterization
Allen-Zhu, Z., Li, Y., and Song, Z
Cited in the paper.
Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval
Chen, Y., Chi, Y., Fan, J., and Ma, C
Cited in the paper.
The Nonsmooth Landscape of Phase Retrieval
Davis, D., Drusvyatskiy, D., and Paquette, C
Cited in the paper.
Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains
Dieuleveut, A., Durmus, A., and Bach, F
Cited in the paper.
High-Dimensional Probability
Vershynin, R · 2018
Later among the works it cites.