Skip to content
/ autodiff Public

Auto differentiation library

License

Apache-2.0, MIT licenses found

Licenses found

Apache-2.0
LICENSE-APACHE
MIT
LICENSE-MIT
Notifications You must be signed in to change notification settings

elrnv/autodiff

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

66 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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: 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:

    // 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:

     // 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

at your option.

Acknowledgements

This library started as a fork of rust-ad.

About

Auto differentiation library

Resources

License

Apache-2.0, MIT licenses found

Licenses found

Apache-2.0
LICENSE-APACHE
MIT
LICENSE-MIT

Stars

Watchers

Forks

Packages

No packages published

Languages