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Phase Retrieval via Accelerated Gradient Descent

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Artificial Intelligence and Security (ICAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11634))

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Abstract

Phase retrieval, as a non-convex optimization problem arises in many areas of signal processing, is to recover the missed signal phase. Based on the Truncated Wirtinger Flow (TWF), where the updating can be regarded as stochastic gradient descent, we present the Accelerated Wirtinger Flow (AWF), which updates the iterative process with accelerated steepest descent. WF algorithm solves the problem by two steps: (a) initialization signal with truncated spectral initialization method provided in TWF and (b) a series of updates this initial estimate by iteratively applying a novel update rule, AWF. Meanwhile, according to the Amplitude Flow objective, the proximal gradient method is suggested.

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Authors and Affiliations

  1. School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China

    Yi Qin

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  1. Yi Qin
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Correspondence to Yi Qin .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Qin, Y. (2019). Phase Retrieval via Accelerated Gradient Descent. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11634. Springer, Cham. https://doi.org/10.1007/978-3-030-24271-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-24271-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24270-1

  • Online ISBN: 978-3-030-24271-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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