numra-autodiff 0.1.4

Automatic differentiation for Numra: forward-mode (Dual numbers) and reverse-mode (tape) for gradients and Jacobians.
Documentation

numra-autodiff

Automatic differentiation for the Numra workspace — forward-mode dual numbers and reverse-mode tape, with a bridge module that plugs AD-derived gradients and Jacobians into numra-optim and numra-fit.

Crates.io docs.rs

Forward-mode uses Dual<S> numbers and costs O(n) passes for n inputs (best when outputs ≫ inputs — Jacobians of small systems, ODE sensitivity). Reverse-mode uses a tape and costs O(m) passes for m outputs (best when inputs ≫ outputs — gradients of scalar objectives, the standard optimization case).

Example

use numra_autodiff::{gradient, Dual};

// f(x, y) = x² + y², gradient at (3, 4) should be (6, 8)
let grad = gradient(
    |x: &[Dual<f64>]| x[0] * x[0] + x[1] * x[1],
    &[3.0, 4.0],
);
assert!((grad[0] - 6.0).abs() < 1e-12);
assert!((grad[1] - 8.0).abs() < 1e-12);

For reverse-mode:

use numra_autodiff::reverse::grad;

let g = grad(|x| x[0].clone() * x[0].clone() + x[1].clone() * x[1].clone(), &[3.0, 4.0]);
assert!((g[0] - 6.0).abs() < 1e-12);
assert!((g[1] - 8.0).abs() < 1e-12);

What's in this crate

  • Dual<S> — forward-mode dual numbers
  • gradient, jacobian — forward-mode gradient and Jacobian of closures
  • reverse::Var, reverse::grad — reverse-mode tape-based variables and gradient
  • tape — low-level tape for custom reverse-mode pipelines
  • bridge::gradient_closure, bridge::model_jacobian_closure — adapters that expose AD-derived gradients / Jacobians in the calling-convention used by numra-optim's OptimProblem::gradient and numra-fit's curve_fit_with_jacobian

Composes with

  • numra-optim — supplies gradients for BFGS, L-BFGS, Levenberg-Marquardt, SQP via bridge::gradient_closure
  • numra-fit — analytical Jacobians for nonlinear least-squares residuals via bridge::model_jacobian_closure
  • numra-ocp — gradients for parameter-estimation and optimal-control objectives

See interop workflows for the verified autodiff → optimization → curve-fitting workflow.

Install

[dependencies]
numra-autodiff = "0.1"

Or via the umbrella crate:

[dependencies]
numra = "0.1"

Documentation

License

Numra Academic & Research License (Non-Commercial). Academic and research use is free; commercial use requires a separate license — contact contact@spectralautomata.com. See LICENSE.