NCVX (NonConVeX) is a user-friendly and scalable python software package targeting general nonsmooth NCVX problems with nonsmooth constraints. NCVX is being developed by GLOVEX at the Department of Computer Science & Engineering, University of Minnesota, Twin Cities.

The initial release of NCVX contains the solver PyGRANSO, a PyTorch-enabled port of GRANSO incorporating auto-differentiation, GPU acceleration, tensor input, and support for new QP solvers. As a highlight, PyGRANSO can solve deep learning problems with nontrivial constraints (e.g., constraints with neural networks), the first of its kind.

PyGRANSO: A PyTorch-enabled port of GRANSO with auto-differentiation Documentation | Repository

If you publish work that uses or refers to NCVX, please cite the following two papers:

[1] Buyun Liang, Tim Mitchell, and Ju Sun, NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning, arXiv preprint arXiv:2111.13984 (2021). Available at https://arxiv.org/abs/2111.13984

[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{liang2021ncvx,
    title={{NCVX}: {A} User-Friendly and Scalable Package for Nonconvex 
    Optimization in Machine Learning}, 
    author={Buyun Liang, Tim Mitchell, and Ju Sun},
    year={2021},
    eprint={2111.13984},
    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}
}