# JAXopt
[**Installation**](#installation)
| [**Documentation**](https://jaxopt.github.io)
| [**Examples**](https://github.com/google/jaxopt/tree/main/examples)
| [**Cite us**](#citeus)
Hardware accelerated, batchable and differentiable optimizers in
[JAX](https://github.com/google/jax).
- **Hardware accelerated:** our implementations run on GPU and TPU, in addition
to CPU.
- **Batchable:** multiple instances of the same optimization problem can be
automatically vectorized using JAX's vmap.
- **Differentiable:** optimization problem solutions can be differentiated with
respect to their inputs either implicitly or via autodiff of unrolled
algorithm iterations.
## Installation<a id="installation"></a>
To install the latest release of JAXopt, use the following command:
```bash
$ pip install jaxopt
```
To install the **development** version, use the following command instead:
```bash
$ pip install git+https://github.com/google/jaxopt
```
Alternatively, it can be installed from sources with the following command:
```bash
$ python setup.py install
```
## Cite us<a id="citeus"></a>
Our implicit differentiation framework is described in this
[paper](https://arxiv.org/abs/2105.15183). To cite it:
```
@article{jaxopt_implicit_diff,
title={Efficient and Modular Implicit Differentiation},
author={Blondel, Mathieu and Berthet, Quentin and Cuturi, Marco and Frostig, Roy
and Hoyer, Stephan and Llinares-L{\'o}pez, Felipe and Pedregosa, Fabian
and Vert, Jean-Philippe},
journal={arXiv preprint arXiv:2105.15183},
year={2021}
}
```
## Disclaimer
JAXopt is an open source project maintained by a dedicated team in Google Research, but is not an official Google product.
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"description": "# JAXopt\n\n[**Installation**](#installation)\n| [**Documentation**](https://jaxopt.github.io)\n| [**Examples**](https://github.com/google/jaxopt/tree/main/examples)\n| [**Cite us**](#citeus)\n\nHardware accelerated, batchable and differentiable optimizers in\n[JAX](https://github.com/google/jax).\n\n- **Hardware accelerated:** our implementations run on GPU and TPU, in addition\n to CPU.\n- **Batchable:** multiple instances of the same optimization problem can be\n automatically vectorized using JAX's vmap.\n- **Differentiable:** optimization problem solutions can be differentiated with\n respect to their inputs either implicitly or via autodiff of unrolled\n algorithm iterations.\n\n## Installation<a id=\"installation\"></a>\n\nTo install the latest release of JAXopt, use the following command:\n\n```bash\n$ pip install jaxopt\n```\n\nTo install the **development** version, use the following command instead:\n\n```bash\n$ pip install git+https://github.com/google/jaxopt\n```\n\nAlternatively, it can be installed from sources with the following command:\n\n```bash\n$ python setup.py install\n```\n\n## Cite us<a id=\"citeus\"></a>\n\nOur implicit differentiation framework is described in this\n[paper](https://arxiv.org/abs/2105.15183). To cite it:\n\n```\n@article{jaxopt_implicit_diff,\n title={Efficient and Modular Implicit Differentiation},\n author={Blondel, Mathieu and Berthet, Quentin and Cuturi, Marco and Frostig, Roy \n and Hoyer, Stephan and Llinares-L{\\'o}pez, Felipe and Pedregosa, Fabian \n and Vert, Jean-Philippe},\n journal={arXiv preprint arXiv:2105.15183},\n year={2021}\n}\n```\n\n## Disclaimer\n\nJAXopt is an open source project maintained by a dedicated team in Google Research, but is not an official Google product.\n",
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