ZORO

A Python implementation of the ZORO algorithm, as introduced in Zeroth-order regularized optimization (ZORO): Approximately sparse gradients and adaptive sampling by Cai, McKenzie, Yin and Zhang. This paper is accepted to SIAM Journal on Optimization (SIOPT). Preprint available here .

ZORO, and its adaptive variant AdaZORO, are implemented as classes in optimizers.py. We use the BaseOptimizer class as in this Repo.

Requirements

Python 3.5+. For proximal operators the pyproximal package is required.

Examples

See Test.py, Test_Prox.py and Test_Ada.py for examples of using ZORO.

Questions?

Feel free to contact us at dmckenzie@mines.edu or hqcai@ucf.edu.

Recommended citation

If you find this code useful please use the following citation:

  @article{cai2022zeroth,
    title={Zeroth-order regularized optimization (ZORO): Approximately sparse gradients and adaptive sampling},
    author={Cai, HanQin and Mckenzie, Daniel and Yin, Wotao and Zhang, Zhenliang},
    journal={SIAM Journal on Optimization},
    volume={32},
    number={2},
    pages={687--714},
    year={2022},
    publisher={SIAM}
  }