hyperdual 0.3.7

Fully-featured Dual Number implementation with features for automatic differentiation of multivariate vectorial functions
Documentation

Dual Numbers

Fully-featured Dual Number implementation with features for automatic differentiation of multivariate vectorial functions into gradients.

Usage

extern crate hyperdual;

use hyperdual::{Dual, Hyperdual, Float, differentiate, U3};

fn main() {
// find partial derivative at x=4.0
let univariate = differentiate(4.0f64, |x| x.sqrt() + Dual::from_real(1.0));
assert!((univariate - 0.25).abs() < 1e-16, "wrong derivative");

// find the partial derivatives of a multivariate function
let x: Hyperdual<f64, U3> = Hyperdual::from_slice(&[4.0, 1.0, 0.0]);
let y: Hyperdual<f64, U3> = Hyperdual::from_slice(&[5.0, 0.0, 1.0]);

let multivariate = x * x + (x * y).sin() + y.powi(3);
assert!((multivariate[0] - 141.91294525072763).abs() < 1e-13, "f(4, 5) incorrect");
assert!((multivariate[1] - 10.04041030906696).abs() < 1e-13, "df/dx(4, 5) incorrect");
assert!((multivariate[2] - 76.63232824725357).abs() < 1e-13, "df/dy(4, 5) incorrect");
}
Previous Work