GraHTP: A Provable Newton-like Algorithm for Sparse Phase Retrieval
<|reference_start|>GraHTP: A Provable Newton-like Algorithm for Sparse Phase Retrieval: This paper investigates the sparse phase retrieval problem, which aims to recover a sparse signal from a system of quadratic measurements. In this work, we propose a novel non-convex algorithm, termed Gradient Hard Thresholding Pursuit (GraHTP), for sparse phase retrieval with complex sensing vectors. GraHTP is theoretically provable and exhibits high efficiency, achieving a quadratic convergence rate after a finite number of iterations, while maintaining low computational complexity per iteration. Numerical experiments further demonstrate GraHTP's superior performance compared to state-of-the-art algorithms.<|reference_end|>
@article{dai2024grahtp:,
    title={GraHTP: A Provable Newton-like Algorithm for Sparse Phase Retrieval},
    author={Licheng Dai, Xiliang Lu, Juntao You},
    journal={arXiv preprint arXiv:2410.04034},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04034},
    primaryClass={math.NA cs.NA}
}