@Article{AAM-38-1,
author = {Jian-Feng, Cai and Meng, Huang and Li, Dong and Yang, Wang},
title = {The Global Landscape of Phase Retrieval II: Quotient Intensity Models},
journal = {Annals of Applied Mathematics},
year = {2022},
volume = {38},
number = {1},
pages = {62--114},
abstract = {<p style="text-align: justify;">A fundamental problem in phase retrieval is to reconstruct an unknown signal from a set of magnitude-only measurements. In this work we introduce three novel quotient intensity models (QIMs) based on a deep modification of the traditional intensity-based models. A remarkable feature of&nbsp; the new loss functions is that the corresponding geometric landscape is benign under the optimal sampling complexity.&nbsp; When the measurements $ a_i\in \mathbb{R}^n$ are Gaussian random vectors and the number of measurements $m\ge Cn$, the QIMs admit no spurious local minimizers with high probability, i.e., the target solution $x$ is the unique local minimizer (up to a global phase) and the loss function has a negative directional curvature around each saddle point. Such benign geometric landscape allows the gradient descent methods to find the global solution $x$ (up to a global phase) without spectral initialization.</p>},
issn = {},
doi = {https://doi.org/10.4208/aam.OA-2021-0010},
url = {https://global-sci.com/article/72628/the-global-landscape-of-phase-retrieval-ii-quotient-intensity-models}
}