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Using Black-box Compression Algorithms for Phase Retrieval
[article]
2020
arXiv
pre-print
Compressive phase retrieval refers to the problem of recovering a structured n-dimensional complex-valued vector from its phase-less under-determined linear measurements. The non-linearity of measurements makes designing theoretically-analyzable efficient phase retrieval algorithms challenging. As a result, to a great extent, algorithms designed in this area are developed to take advantage of simple structures such as sparsity and its convex generalizations. The goal of this paper is to move
arXiv:1712.03278v3
fatcat:rbxh65xepffjzfhxpoyqvbldfi