# `autodiff`
An auto-differentiation library.
[](https://crates.io/crates/autodiff)
[](https://docs.rs/autodiff/)
[](https://travis-ci.org/elrnv/autodiff)
Currently supported features:
- [x] Forward auto-differentiation
- [ ] Reverse auto-differentiation
To compute a derivative with respect to a variable using this library:
1. create a variable of type `F`, which implements the `Float` trait from the `num-traits` crate.
2. compute your function using this variable as the input.
3. request the derivative from this variable using the `deriv` method.
# Disclaimer
This library is a work in progress and is not ready for production use.
# Examples
The following example differentiates a 1D function defined by a closure.
```rust
// Define a function `f(x) = e^{-0.5*x^2}`.
let f = |x: FT<f64>| (-x * x / F1::cst(2.0)).exp();
// Differentiate `f` at zero.
println!("{}", diff(f, 0.0)); // prints `0`
```
To compute the gradient of a function, use the function `grad` as follows:
```rust
// Define a function `f(x,y) = x*y^2`.
let f = |x: &[FT<f64>]| x[0] * x[1] * x[1];
// Differentiate `f` at `(1,2)`.
let g = grad(f, &vec![1.0, 2.0]);
println!("({}, {})", g[0], g[1]); // prints `(4, 4)`
```
Compute a specific derivative of a multi-variable function:
```rust
// Define a function `f(x,y) = x*y^2`.
let f = |v: &[FT<f64>]| v[0] * v[1] * v[1];
// Differentiate `f` at `(1,2)` with respect to `x` (the first unknown) only.
let v = vec![
F1::var(1.0), // Create a variable.
F1::cst(2.0), // Create a constant.
];
println!("{}", f(&v).deriv()); // prints `4`
```
# Features
Support for `approx`, `cgmath` and `nalgebra` via the `approx`, `cgmath` and `na` feature flags respectively.
# License
This repository is licensed under either of
* Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or https://www.apache.org/licenses/LICENSE-2.0)
* MIT License ([LICENSE-MIT](LICENSE-MIT) or https://opensource.org/licenses/MIT)
at your option.
# Acknowledgements
This library started as a fork of [rust-ad](https://github.com/ibab/rust-ad).