10 releases

✓ Uses Rust 2018 edition

0.1.9 Nov 7, 2019
0.1.8 Nov 5, 2019
0.1.6 Aug 28, 2019
0.1.5 Jan 17, 2019
0.1.4 Nov 10, 2018

#71 in Rust patterns

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Used in 2 crates

MIT/Apache

36KB
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autodiff

An auto-differentiation library.

On crates.io On docs.rs Build status

Currently supported features:

  • 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.

    // Define a function `f(x) = e^{-0.5*x^2}`.
    let f = |x: F| (-x * x / F::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:

    // Define a function `f(x,y) = x*y^2`.
    let f = |x: &[F]| 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:

     // Define a function `f(x,y) = x*y^2`.
     let f = |v: &[F]| v[0] * v[1] * v[1];
 
     // Differentiate `f` at `(1,2)` with respect to `x` (the first unknown) only.
     let v = vec![
         F::var(1.0), // Create a variable.
         F::cst(2.0), // Create a constant.
     ];
     println!("{}", f(&v).deriv()); // prints `4`

License

This repository is licensed under either of

at your option.

Acknowledgements

This library started as a fork of rust-ad.

Dependencies

~140KB