basin 1.4.0

Numerical optimization in pure Rust, with pluggable linear-algebra backends and WASM support.
Documentation 
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#![cfg(feature = "ndarray")]

//! Bfgs convergence over the ndarray backend (`Array1<f64>` and `Array2<f64>`).
//!
//! Mirrors `tests/bfgs.rs` (nalgebra), `tests/bfgs_vec.rs` (`Vec<f64>`), and
//! `tests/bfgs_faer.rs` (faer): the same generic `Solver` impl drives
//! ndarray's dense inverse-Hessian via `MatVec` + `GeneralRankOneUpdate` on
//! `Array2<f64>`.

use basin::problems::Rosenbrock;
use basin::{Bfgs, Executor, GradientTolerance, NdarrayQuasiNewtonState, TerminationReason};
use ndarray::array;

#[test]
fn bfgs_converges_on_rosenbrock() {
    let problem = Rosenbrock::<ndarray::Array1<f64>>::default();
    let initial = array![-1.2, 1.0];

    let result = Executor::new(problem, Bfgs::new(), NdarrayQuasiNewtonState::new(initial))
        .max_iter(100)
        .run()
        .unwrap();

    assert!(
        result.cost() < 1e-8,
        "expected near-zero cost, got {}",
        result.cost()
    );
    assert!(
        (result.param()[0] - 1.0).abs() < 1e-4,
        "x[0] = {}",
        result.param()[0]
    );
    assert!(
        (result.param()[1] - 1.0).abs() < 1e-4,
        "x[1] = {}",
        result.param()[1]
    );
}

#[test]
fn bfgs_terminates_on_gradient_tolerance() {
    let problem = Rosenbrock::<ndarray::Array1<f64>>::default();
    let initial = array![-1.2, 1.0];

    let result = Executor::new(problem, Bfgs::new(), NdarrayQuasiNewtonState::new(initial))
        .max_iter(200)
        .terminate_on(GradientTolerance(1e-6))
        .run()
        .unwrap();

    assert_eq!(result.reason, TerminationReason::GradientTolerance);
    assert!(result.cost() < 1e-10, "cost = {}", result.cost());
}