Jaxopt Save

Hardware accelerated, batchable and differentiable optimizers in JAX.

Project README

JAXopt

Installation | Documentation | Examples | Cite us

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.

Installation

To install the latest release of JAXopt, use the following command:

$ pip install jaxopt

To install the development version, use the following command instead:

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

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

$ python setup.py install

Cite us

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}
}

Disclaimer

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

Open Source Agenda is not affiliated with "Jaxopt" Project. README Source: google/jaxopt
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