basin 0.7.0

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

use basin::problems::BoothBoxed;
use basin::{
    Backtracking, BasicState, Executor, ProjectedGradientDescent, ProjectedGradientTolerance,
    TerminationReason,
};
use ndarray::Array1;

#[test]
fn slack_bounds_recover_unconstrained_minimum() {
    let lower = Array1::from(vec![-5.0, -5.0]);
    let upper = Array1::from(vec![5.0, 5.0]);
    let problem = BoothBoxed::<Array1<f64>>::new(lower, upper);
    let initial = Array1::from(vec![0.0, 0.0]);

    let result = Executor::new(
        problem,
        ProjectedGradientDescent::with_line_search(Backtracking::new()),
        BasicState::new(initial),
    )
    .max_iter(2000)
    .run();

    assert!(
        (result.param()[0] - 1.0).abs() < 1e-4,
        "x[0] = {}",
        result.param()[0]
    );
    assert!(
        (result.param()[1] - 3.0).abs() < 1e-4,
        "x[1] = {}",
        result.param()[1]
    );
}

#[test]
fn tight_bounds_converge_to_box_corner() {
    let lower = Array1::from(vec![-1.0, -1.0]);
    let upper = Array1::from(vec![1.0, 1.0]);
    let problem = BoothBoxed::<Array1<f64>>::new(lower, upper);
    let initial = Array1::from(vec![0.0, 0.0]);

    let result = Executor::new(
        problem,
        ProjectedGradientDescent::with_line_search(Backtracking::new()),
        BasicState::new(initial),
    )
    .max_iter(2000)
    .run();

    assert!(
        (result.param()[0] - 1.0).abs() < 1e-6,
        "x[0] = {}",
        result.param()[0]
    );
    assert!(
        (result.param()[1] - 1.0).abs() < 1e-6,
        "x[1] = {}",
        result.param()[1]
    );
}

#[test]
fn infeasible_initial_param_is_projected_at_init() {
    let lower = Array1::from(vec![-1.0, -1.0]);
    let upper = Array1::from(vec![1.0, 1.0]);
    let problem = BoothBoxed::<Array1<f64>>::new(lower, upper);
    let initial = Array1::from(vec![10.0, 10.0]);

    let result = Executor::new(
        problem,
        ProjectedGradientDescent::new(0.01),
        BasicState::new(initial),
    )
    .max_iter(0)
    .run();

    assert_eq!(result.reason, TerminationReason::MaxIter);
    assert_eq!(result.param()[0], 1.0);
    assert_eq!(result.param()[1], 1.0);
}

#[test]
fn projected_gradient_tolerance_triggers_at_corner_minimum() {
    let lower = Array1::from(vec![-1.0, -1.0]);
    let upper = Array1::from(vec![1.0, 1.0]);
    let problem = BoothBoxed::<Array1<f64>>::new(lower.clone(), upper.clone());
    let initial = Array1::from(vec![0.0, 0.0]);

    let result = Executor::new(
        problem,
        ProjectedGradientDescent::with_line_search(Backtracking::new()),
        BasicState::new(initial),
    )
    .max_iter(2000)
    .terminate_on(ProjectedGradientTolerance::new(lower, upper, 1e-7))
    .run();

    assert_eq!(result.reason, TerminationReason::ProjectedGradientTolerance);
}