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Implementations of quasi-hyperbolic optimization algorithms.

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qhoptim: Quasi-hyperbolic optimization

This repository contains PyTorch and TensorFlow implementations of the quasi-hyperbolic momentum (QHM) and quasi-hyperbolic Adam (QHAdam) optimization algorithms from Facebook AI Research.

Quickstart

Use this one-liner for installation:

```\$ pip install qhoptim
```

Then, you can instantiate the optimizers in PyTorch:

```>>> from qhoptim.pyt import QHM, QHAdam

# something like this for QHM
>>> optimizer = QHM(model.parameters(), lr=1.0, nu=0.7, momentum=0.999)

# or something like this for QHAdam
...     model.parameters(), lr=1e-3, nus=(0.7, 1.0), betas=(0.995, 0.999))```

Or in TensorFlow:

```>>> from qhoptim.tf import QHMOptimizer, QHAdamOptimizer

# something like this for QHM
>>> optimizer = QHMOptimizer(
...     learning_rate=1.0, nu=0.7, momentum=0.999)

# or something like this for QHAdam
...     learning_rate=1e-3, nu1=0.7, nu2=1.0, beta1=0.995, beta2=0.999)```

Documentation

Please refer to the documentation for installation instructions, usage information, and a Python API reference.

Direct link to installation instructions: here.

Reference

QHM and QHAdam were proposed in the ICLR 2019 paper "Quasi-hyperbolic momentum and Adam for deep learning". We recommend reading the paper for both theoretical insights into and empirical analyses of the algorithms.

If you find the algorithms useful in your research, we ask that you cite the paper as follows:

```@inproceedings{ma2019qh,
title={Quasi-hyperbolic momentum and Adam for deep learning},
author={Jerry Ma and Denis Yarats},
booktitle={International Conference on Learning Representations},
year={2019}
}```

Contributing

Bugfixes and contributions are very much appreciated! Please see `CONTRIBUTING.rst` for more information.

This source code is licensed under the MIT license found in the `LICENSE` file in the root directory of this source tree.

Implementations of quasi-hyperbolic optimization algorithms.

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