  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.
