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Citing PyGRANSO

If you publish work that uses or refers to PyGRANSO, please cite the following two papers, which respectively introduced PyGRANSO and GRANSO:

[1] Buyun Liang, Tim Mitchell, and Ju Sun, NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning, arXiv preprint arXiv:2210.00973 (2022). Available at https://arxiv.org/abs/2210.00973

[2] Frank E. Curtis, Tim Mitchell, and Michael L. Overton, A BFGS-SQP method for nonsmooth, nonconvex, constrained optimization and its evaluation using relative minimization profiles, Optimization Methods and Software, 32(1):148-181, 2017. Available at https://dx.doi.org/10.1080/10556788.2016.1208749

BibTex:

@article{liang2022ncvx,
    title={{NCVX}: {A} General-Purpose Optimization Solver for Constrained Machine and Deep Learning},
    author={Buyun Liang, Tim Mitchell, and Ju Sun},
    year={2022},
    eprint={2210.00973},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

@article{curtis2017bfgssqp,
    title={A {BFGS-SQP} method for nonsmooth, nonconvex, constrained optimization and its evaluation using relative minimization profiles},
    author={Frank E. Curtis, Tim Mitchell, and Michael L. Overton},
    journal={Optimization Methods and Software},
    volume={32},
    number={1},
    pages={148--181},
    year={2017},
    publisher={Taylor \& Francis}
}

If you publish work that uses or refers to PyGRANSO as a universal DL-robustness evaluation solver, please cite the following paper:

[3] Hengyue Liang, Buyun Liang, Le Peng, Ying Cui, Tim Mitchell, and Ju Sun, Optimization for Adversarial Robustness Evaluations and Implications from the Solution Patterns. arXiv preprint arXiv: 2303.13401 (2023). Available at https://arxiv.org/pdf/2303.13401

BibTex:

@article{liang2023optimization,
    title={Optimization and optimizers for adversarial robustness},
    author={Hengyue Liang, Buyun Liang, Le Peng, Ying Cui, Tim Mitchell, and Ju Sun},
    year={2023},
    eprint={2303.13401},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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