Abstract:In this work we propose a nonconvex two-stage stochastic alternating minimizing (SAM) method for sparse phase retrieval. The proposed algorithm is guaranteed to have an exact recovery from O(s log n) samples if provided the initial guess is in a local neighbour of the ground truth. Thus, the proposed algorithm is two-stage, first we estimate a desired initial guess (e.g. via a spectral method), and then we introduce a randomized alternating minimization strategy for local refinement. Also, the hard-thresholdin… Show more
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