ncvx_documentation

NCVX documentation page: https://ncvx.org

https://github.com/sun-umn/ncvx_documentation

Science Score: 52.0%

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Repository

NCVX documentation page: https://ncvx.org

Basic Info

Host: GitHub
Owner: sun-umn
Language: Python
Default Branch: main
Homepage: https://ncvx.org
Size: 15.4 MB

Statistics

Stars: 2
Watchers: 3
Forks: 0
Open Issues: 0
Releases: 0

Created over 4 years ago · Last pushed 11 months ago

Metadata Files

Readme Citation

README.md

Documentation Page: https://ncvx.org

Source Code https://github.com/sun-umn/PyGRANSO

Contact: Buyun Liang [https://buyunliang.org] byliang at seas dot upenn dot edu

Owner

Name: GLOVEX @ UMN
Login: sun-umn
Kind: organization
Location: United States of America

Website: https://glovex.umn.edu/
Repositories: 4
Profile: https://github.com/sun-umn

Citation (citation.rst)

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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