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Hardware accelerated, batchable and differentiable optimizers in 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.

JAXopt can be installed with pip directly from github, with the following command:

$ pip install git+https://github.com/google/jaxopt

Alternatively, it can be be installed from sources with the following command:

$ python setup.py install

Our implicit differentiation framework is described in this paper. 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}
}

JAXopt is an open source project maintained by a dedicated team in Google Research, but is not an official Google product.