wolfe_bfgs 0.1.2

A dense BFGS optimization algorithm in Rust, with a Strong Wolfe line search and initial Hessian scaling.
Documentation

A Robust BFGS Implementation in Rust

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A pure Rust implementation of the dense BFGS optimization algorithm for unconstrained nonlinear problems. This library provides a powerful solver built upon the principles and best practices outlined in Nocedal & Wright's Numerical Optimization.

This work is a rewrite of the original bfgs crate by Paul Kernfeld.

Features

  • Strong Wolfe Line Search: Guarantees stability and positive-definiteness of the Hessian approximation by satisfying the Strong Wolfe conditions. This ensures the crucial curvature condition s_k^T y_k > 0 holds, making the algorithm reliable even for challenging functions.
  • Initial Hessian Scaling: Implements the well-regarded scaling heuristic (Eq. 6.20 from Nocedal & Wright) to produce a well-scaled initial Hessian, often improving the rate of convergence.
  • Ergonomic API: Uses the builder pattern for clear and flexible configuration of the solver.
  • Robust Error Handling: Provides descriptive errors for common failure modes, such as line search failures or reaching the iteration limit, enabling better diagnostics.
  • Dense BFGS Implementation: Stores the full n x n inverse Hessian approximation, suitable for small- to medium-scale optimization problems.

Usage

First, add this to your Cargo.toml:

[dependencies]
wolfe_bfgs = "0.1.0"

Example: Minimizing the Rosenbrock Function

Here is an example of minimizing the 2D Rosenbrock function, a classic benchmark for optimization algorithms.

use wolfe_bfgs::{Bfgs, BfgsSolution};
use ndarray::{array, Array1};

// 1. Define the objective function and its gradient.
// The function must return a tuple: (value, gradient).
let rosenbrock = |x: &Array1<f64>| -> (f64, Array1<f64>) {
    let a = 1.0;
    let b = 100.0;
    let f = (a - x[0]).powi(2) + b * (x[1] - x[0].powi(2)).powi(2);
    let g = array![
        -2.0 * (a - x[0]) - 4.0 * b * (x[1] - x[0].powi(2)) * x[0],
        2.0 * b * (x[1] - x[0].powi(2)),
    ];
    (f, g)
};

// 2. Set the initial guess.
let x0 = array![-1.2, 1.0];

// 3. Configure and run the solver.
let solution = Bfgs::new(x0, rosenbrock)
    .with_tolerance(1e-6)
    .with_max_iterations(100)
    .run()
    .expect("BFGS failed to solve");

println!(
    "Found minimum f({:.3}) = {:.4} in {} iterations.",
    solution.final_point, solution.final_value, solution.iterations
);

// The known minimum is at [1.0, 1.0].
assert!((solution.final_point[0] - 1.0).abs() < 1e-5);
assert!((solution.final_point[1] - 1.0).abs() < 1e-5);

Algorithm Details

This crate implements the standard, dense BFGS algorithm. It is not a limited-memory (L-BFGS) implementation. The implementation closely follows the methods described in Numerical Optimization (2nd ed.) by Nocedal and Wright:

  • BFGS Update: The inverse Hessian H_k is updated using the standard formula (Eq. 6.17).
  • Line Search: A line search satisfying the Strong Wolfe conditions is implemented according to Algorithm 3.5 from the text. This involves a bracketing phase followed by a zoom phase (Algorithm 3.6) that uses cubic interpolation for efficient refinement.
  • Initial Hessian: A scaling heuristic is used to initialize the inverse Hessian before the first update (Eq. 6.20).

Testing and Validation

This library is tested against a suite of standard optimization benchmarks. The results are validated against scipy.optimize.minimize(method='BFGS') via a Python test harness.

To run the full test suite:

cargo test --release

To run the benchmarks:

cargo bench

Acknowledgements

This crate is a fork and rewrite of the original bfgs crate by Paul Kernfeld. The new version changes the API and replaces the original grid-search-based line search with an implementation based on the Strong Wolfe conditions.

License

Licensed under either of:

at your option.