A simple, pure python algorithmic differentiation package

# pyADiff: A simple, pure python algorithmic differentiation package `pyADiff` is a (yet) very basic algorithmic differentiation package, which implements forward and adjoint/reverse mode differentiation. If you are looking for a fully-featured and faster library, have a look at google/jax, autograd or dco/c++ (or many more), but if you are interested in a package where you are able to quickly "look under the hood", you may be right here.

## Motivation

My motivation to start this project arose from curiosity while listening to the lecture "Computational Differentiation" by Prof. Naumann at RWTH Aachen University. So basically I tried to understand the concepts from the lecture by implementing them by myself. In the end I was (positively) surprised with the outcome and decided to bundle it in a python package. Additionaly this gave me the chance to learn about python packaging, distributing, documentation, ...

## Basic Usage

Suppose we want to compute the gradient of the function

f(x₀, x₁) = 2 x₀ x₁².

This is a rather trivial task, because by simple calculus, the gradient is:

∇f(x₀, x₁) = (2 x₁², 4 x₀ x₁)

Nevertheless we use this example illustrate the use of `pyADiff`.

```import pyADiff as ad
# define the function f
def f(x):
return 2.*x*x**2.

x = [0.5, 2.0]
# Call the function f and the gradient function df
y = f(x)
dy = df(x)

print("f({}) = {}".format(x, y))  # prints f([0.5, 2.0]) = 4.0
print("f'({}) = {}".format(x, dy))  # prints f'([0.5, 2.0]) = [8. 4.]
```

Which corresponds to the evaluation of the analytic gradient.

∇f(0.5, 2) = (2*2², 4*0.5*2) = (8, 4)

For more sophisticated examples see the Documentation or have a look at the .ipynb notebooks

## Installation

TODO

### Installation from source

This will clone the repository and install the `pyADiff` package using the `setup.py` script.

```> git clone https://github.com/tam724/pyADiff
```

## References

### Algorithmic Differentiation:

• Uwe Naumann, Lecture Computational Differentiation, RWTH Aachen

## Project details

This version 0.1.1