basin 1.5.0

Numerical optimization in pure Rust, with pluggable linear-algebra backends and WASM support.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89

//! ndarray-backend smoke tests for [`CmaInject`] with Nelder-Mead
//! inner. Convergence on Rosenbrock 2-D plus the eval-aggregation
//! lower bound confirm the per-backend trait wiring (the Jacobi
//! eigensolver reached through `as_standard_layout()` on `Array2`)
//! works through the composition boundary; the deeper algorithmic
//! invariants are covered by the nalgebra mirror test
//! (`tests/cma_inject_nalgebra.rs`).

#![cfg(feature = "ndarray")]

use basin::problems::{Rosenbrock, Sphere};
use basin::{CmaEs, CmaEsState, CmaInject, Executor, NelderMead};
use ndarray::{Array1, Array2};

/// Rosenbrock 2-D from `(-1, 1)`: injecting Nelder-Mead refinements
/// must not break CMA's convergence on the ndarray backend.
#[test]
fn converges_on_rosenbrock_2d() {
    let m0 = Array1::from_vec(vec![-1.0, 1.0]);

    let cma = CmaEs::<Array1<f64>, Array2<f64>>::new(17);
    let solver = CmaInject::with_inner_solver(cma, NelderMead::adaptive())
        .with_k(1)
        .with_inner_max_iter(30);

    let result = Executor::new(
        Rosenbrock::<Array1<f64>>::new(),
        solver,
        CmaEsState::<Array1<f64>, Array2<f64>>::new(m0, 0.3),
    )
    .max_iter(200)
    .run()
    .unwrap();

    let p = result.param();
    assert!(
        (p[0] - 1.0).abs() < 1e-3 && (p[1] - 1.0).abs() < 1e-3,
        "rosenbrock 2-D iterate = ({}, {}), expected ≈ (1, 1) within 1e-3",
        p[0],
        p[1]
    );
}

/// Inner Nelder-Mead `cost_evals` roll into the outer state's counter
/// (CONTRIBUTING.md "Solver composition" rule 1); same lower bound as
/// the nalgebra mirror.
#[test]
fn aggregates_inner_cost_evals_into_outer() {
    let m0 = Array1::from_elem(5, 1.0);
    let n = 5usize;
    let outer_iters: u64 = 20;
    let inner_iters: u64 = 30;
    let k: usize = 1;

    // Vanilla CMA-ES baseline.
    let vanilla = Executor::new(
        Sphere::<Array1<f64>>::new(),
        CmaEs::<Array1<f64>, Array2<f64>>::new(7),
        CmaEsState::<Array1<f64>, Array2<f64>>::new(m0.clone(), 0.3),
    )
    .max_iter(outer_iters)
    .run()
    .unwrap();

    // Memetic variant on the same seed and outer budget.
    let cma = CmaEs::<Array1<f64>, Array2<f64>>::new(7);
    let solver = CmaInject::with_inner_solver(cma, NelderMead::adaptive())
        .with_k(k)
        .with_inner_max_iter(inner_iters);

    let memetic = Executor::new(
        Sphere::<Array1<f64>>::new(),
        solver,
        CmaEsState::<Array1<f64>, Array2<f64>>::new(m0, 0.3),
    )
    .max_iter(outer_iters)
    .run()
    .unwrap();

    let min_extra = (outer_iters.saturating_sub(1)) * (k as u64) * (n as u64 + 2);
    assert!(
        memetic.cost_evals() >= vanilla.cost_evals() + min_extra,
        "memetic cost_evals = {} should exceed vanilla {} by at least \
         {} (outer iters × k × (n+2) for NM init + re-eval)",
        memetic.cost_evals(),
        vanilla.cost_evals(),
        min_extra
    );
}