Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Undersampled Phase Retrieval via Majorization-Minimization   In the undersampled phase retrieval problem, the goal is to recover an
$N$-dimensional complex signal $\mathbf{x}$ from only $M<N$ noisy intensity
measurements without phase information. This problem has drawn a lot of
attention to reduce the number of required measurements since a recent theory
established that $M\approx4N$ intensity measurements are necessary and
sufficient to recover a generic signal $\mathbf{x}$. In this paper, we propose
to exploit the sparsity in the original signal and develop low-complexity
algorithms with superior performance based on the majorization-minimization
(MM) framework. The proposed algorithms are preferred to existing benchmark
methods since at each iteration a simple surrogate problem is solved with a
closed-form solution that monotonically decreases the original objective
function. Experimental results validate that our algorithms outperform existing
up-to-date methods in terms of recovery probability and accuracy, under the
same settings.

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The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int