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, Ying Cui, Tim Mitchell, and Ju Sun, Optimization for Robustness Evaluation beyond* :math:`\ell_p` *Metrics, arXiv preprint arXiv:2210.00621 (2022).* Available at https://arxiv.org/abs/2210.00621 BibTex:: @article{liang2022optimization, title={Optimization for Robustness Evaluation beyond $\ell_p$ Metrics}, author={Hengyue Liang, Buyun Liang, Ying Cui, Tim Mitchell, and Ju Sun}, year={2022}, eprint={2210.00621}, archivePrefix={arXiv}, primaryClass={cs.LG} }