https://github.com/pierreablin/autoptim

Automatic differentiation + optimization
https://github.com/pierreablin/autoptim

autodiff numpy optimization

Last synced: 2 months ago
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Automatic differentiation + optimization

• Host: GitHub
• URL: https://github.com/pierreablin/autoptim
• Owner: pierreablin
• License: mit
• Created: 2019-02-16T11:39:49.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-03-21T15:39:40.000Z (almost 6 years ago)
• Last Synced: 2024-11-09T10:48:03.424Z (2 months ago)
• Topics: autodiff, numpy, optimization
• Language: Python
• Homepage:
• Size: 24.4 KB
• Stars: 104
• Watchers: 2
• Forks: 9
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# autoptim: automatic differentiation + optimization

Do you have a new machine learning model that you want to optimize, and do not want to bother computing the gradients? Autoptim is for you.

### Warning:

As of version 0.3, `pytorch` has been replaced with `autograd` for automatic differentiation. It makes the interfacing with numpy even simpler.

## Short presentation

Autoptim is a small Python package that blends `autograd`'s automatic differentiation in `scipy.optimize.minimize`.

The gradients are computed under the hood using automatic differentiation; the user only provides the objective function:

```python

import numpy as np

from autoptim import minimize

def rosenbrock(x):

    return (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2

x0 = np.zeros(2)

x_min, _ = minimize(rosenbrock, x0)

print(x_min)

>>> [0.99999913 0.99999825]

```

It comes with the following features:

- **Natural interfacing with Numpy**: The objective function is written in standard Numpy. The input/ output of `autoptim.minimize` are Numpy arrays.

- **Smart input processing**: `scipy.optimize.minimize` is only meant to deal with one-dimensional arrays as input. In `autoptim`, variables can be multi-dimensional arrays or lists of arrays.

- **Preconditioning**: Preconditioning is a simple way to accelerate minimization, by doing a change of variables. `autoptim` makes preconditioning straightforward.

### Disclaimer

This package is meant to be as easy to use as possible. As so, some compromises on the speed of minimization are made.

## Installation

  To install, use `pip`:

  ```

  pip install autoptim

  ```

## Dependencies

- numpy>=1.12

- scipy>=0.18.0

- autograd >= 1.2

## Examples

Several examples can be found in `autoptim/tutorials`