AAD

This crate provides tools for implementing adjoint(a.k.a. reverse-mode) automatic differentiation in Rust. It enables gradient computation for scalar values through a flexible and extensible API.

• User-Friendly Design: Equations can be manipulated as seamlessly as primitive floating-point types.
  • This design draws heavy inspiration from the rustograd library.
• High Performance: The library is designed to be both efficient and scalable, with minimal overhead.
  • Benchmarks show it is up to 9x faster compared to rustograd.

Quick Start

Here's an example of how to use the library:

use aad::core::tape::Tape;

fn main() {
    let tape = Tape::default();

    let x = tape.var(2.0);
    let y = tape.var(3.0);

    let z = (x + y) * x.sin();

    println!("{}", z.value());

    z.backward();

    println!("Gradient of x: {}", x.grad());
    println!("Gradient of y: {}", y.grad());
}

License

This project is licensed under the MIT License.