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Pptimization with autodiff

Project description

# autoptim: automatic differentiation + optimization

## Short presentation
Autoptim is a small Python package that blends Pytorch's automatic differentiation in `scipy.optimize.minimize`.

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

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)

>>> [0.99999913 0.99999825]

It comes with the following features:

- **Minimal Pytorch use**: The user only needs to write the objective function in a Pytorch -compatible way. 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.

## Installation

## Dependencies
- numpy>=1.12
- scipy>=0.18.0
- Pytorch>=0.4.1

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