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arXiv · 1906.05967

A stochastic alternating minimizing method for sparse phase retrieval

Jianfeng Cai·Yuling Jiao·Xiliang Lu·Juntao You
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Abstract

Sparse phase retrieval plays an important role in many fields of applied science and thus attracts lots of attention. In this paper, we propose a \underline{sto}chastic alte\underline{r}nating \underline{m}inimizing method for \underline{sp}arse ph\underline{a}se \underline{r}etrieval (\textit{StormSpar}) algorithm which {emprically} is able to recover $n$-dimensional $s$-sparse signals from only $O(s\,\mathrm{log}\, n)$ number of measurements without a desired initial value required by many existing methods. In \textit{StormSpar}, the hard-thresholding pursuit (HTP) algorithm is employed to solve the sparse constraint least square sub-problems. The main competitive feature of \textit{StormSpar} is that it converges globally requiring optimal order of number of samples with random initialization. Extensive numerical experiments are given to validate the proposed algorithm.

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Jianfeng Cai, Yuling Jiao, Xiliang Lu, Juntao You. 2019-06-14. A stochastic alternating minimizing method for sparse phase retrieval. https://arxiv.org/abs/1906.05967

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