Metadata-Version: 2.1
Name: chop-pytorch
Version: 0.0.2
Summary: Continuous and constrained optimization with PyTorch
Home-page: http://pypi.python.org/pypi/chop-pytorch
Author: Geoffrey Negiar
Author-email: geoffrey_negiar@berkeley.edu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: torch

# pytorCH OPtimize: a library for continuous optimization built on PyTorch

...with applications to adversarially attacking and training neural networks.

[![Build Status](https://travis-ci.org/openopt/chop.svg?branch=master)](https://travis-ci.org/openopt/chop)
[![Coverage Status](https://coveralls.io/repos/github/openopt/chop/badge.svg?branch=master)](https://coveralls.io/github/openopt/chop?branch=master)
[![DOI](https://zenodo.org/badge/310693245.svg)](https://zenodo.org/badge/latestdoi/310693245)

:warning: This library is in early development, API might change without notice. The examples will be kept up to date. :warning:

## Stochastic Algorithms

We define stochastic optimizers in the `chop.stochastic` module. These follow PyTorch Optimizer conventions, similar to the `torch.optim` module.

## Full Gradient Algorithms

We also define full-gradient algorithms which operate on a batch of optimization problems in the `chop.optim` module. These are used for adversarial attacks, using the `chop.Adversary` wrapper.

## Examples:

  See `examples` directory and our [webpage](http://openo.pt/chop/auto_examples/index.html).

## Tests

Run the tests with `pytests tests`.

## Citing

If this software is useful to your research, please consider citing it as

```
@article{chop,
  author       = {Geoffrey Negiar, Fabian Pedregosa},
  title        = {CHOP: continuous optimization built on Pytorch},
  year         = 2020,
  url          = {http://github.com/openopt/chop}
}
```

## Affiliations

Geoffrey Négiar is in the Mahoney lab and the El Ghaoui lab at UC Berkeley.

Fabian Pedregosa is at Google Research.


