  Phase retrieval(PR) problem is a kind of ill-condition inverse problem which
is arising in various of applications. Based on the Wirtinger flow(WF) method,
a reweighted Wirtinger flow(RWF) method is proposed to deal with PR problem.
RWF finds the global optimum by solving a series of sub-PR problems with
changing weights. Theoretical analyses illustrate that the RWF has a geometric
convergence from a deliberate initialization when the weights are bounded by 1
and $\frac{10}{9}$. Numerical testing shows RWF has a lower sampling complexity
compared with WF. As an essentially adaptive truncated Wirtinger flow(TWF)
method, RWF performs better than TWF especially when the ratio between sampling
number $m$ and length of signal $n$ is small.
