Module autodiff::forward_autodiff[−][src]

This module defines forward automatic differentiation. This mode of autodiff is most efficient when computing deriviatives with more inputs than outputs. It is also useful for computing Jacobian products.

Examples

The following exmaple shows how to compute a Jacobian product and evaluate the function at the same time.

use autodiff::*;
// Define a function `f(x,y) = (x*y^2, x/y)`.
let f = |v: &[F1]| vec![v[0] * v[1] * v[1], v[0]/v[1]];

// Compute the Jacobian of `f` at `x = (1,2)` multiplied by a vector `p = (3,4)`.
let xp = vec![
    F1::new(1.0, 3.0),
    F1::new(2.0, 4.0),
];
let jp = f(&xp);
println!("({}, {})", jp[0].value(), jp[1].value()); // prints `(4.0, 0.5)`
println!("({}, {})", jp[0].deriv(), jp[1].deriv()); // prints `(28.0, 0.5)`

Structs

F	A generic forward differentiation `Dual` number.

Traits

ReduceOrder

A Helper trait to drop the highest order derivative.

Functions

diff	Evaluates the derivative of `f` at `x0`
grad	Evaluates the gradient of `f` at `x0`

Type Definitions