nalgebra/tests/linalg/lu.rs

152 lines
4.2 KiB
Rust

use na::Matrix3;
#[test]
fn lu_simple() {
let m = Matrix3::new(
2.0, -1.0, 0.0,
-1.0, 2.0, -1.0,
0.0, -1.0, 2.0);
let lu = m.lu();
assert_eq!(lu.determinant(), 4.0);
let (p, l, u) = lu.unpack();
let mut lu = l * u;
p.inv_permute_rows(&mut lu);
assert!(relative_eq!(m, lu, epsilon = 1.0e-7));
}
#[test]
fn lu_simple_with_pivot() {
let m = Matrix3::new(
0.0, -1.0, 2.0,
-1.0, 2.0, -1.0,
2.0, -1.0, 0.0);
let lu = m.lu();
assert_eq!(lu.determinant(), -4.0);
let (p, l, u) = lu.unpack();
let mut lu = l * u;
p.inv_permute_rows(&mut lu);
assert!(relative_eq!(m, lu, epsilon = 1.0e-7));
}
#[cfg(feature = "arbitrary")]
mod quickcheck_tests {
use std::cmp;
use na::{DMatrix, Matrix4, Matrix4x3, Matrix5x3, Matrix3x5, DVector, Vector4};
quickcheck! {
fn lu(m: DMatrix<f64>) -> bool {
let mut m = m;
if m.len() == 0 {
m = DMatrix::new_random(1, 1);
}
let lu = m.clone().lu();
let (p, l, u) = lu.unpack();
let mut lu = l * u;
p.inv_permute_rows(&mut lu);
relative_eq!(m, lu, epsilon = 1.0e-7)
}
fn lu_static_3_5(m: Matrix3x5<f64>) -> bool {
let lu = m.lu();
let (p, l, u) = lu.unpack();
let mut lu = l * u;
p.inv_permute_rows(&mut lu);
relative_eq!(m, lu, epsilon = 1.0e-7)
}
fn lu_static_5_3(m: Matrix5x3<f64>) -> bool {
let lu = m.lu();
let (p, l, u) = lu.unpack();
let mut lu = l * u;
p.inv_permute_rows(&mut lu);
relative_eq!(m, lu, epsilon = 1.0e-7)
}
fn lu_static_square(m: Matrix4<f64>) -> bool {
let lu = m.lu();
let (p, l, u) = lu.unpack();
let mut lu = l * u;
p.inv_permute_rows(&mut lu);
relative_eq!(m, lu, epsilon = 1.0e-7)
}
fn lu_solve(n: usize, nb: usize) -> bool {
if n != 0 && nb != 0 {
let n = cmp::min(n, 50); // To avoid slowing down the test too much.
let nb = cmp::min(nb, 50); // To avoid slowing down the test too much.
let m = DMatrix::<f64>::new_random(n, n);
let lu = m.clone().lu();
let b1 = DVector::new_random(n);
let b2 = DMatrix::new_random(n, nb);
let sol1 = lu.solve(&b1);
let sol2 = lu.solve(&b2);
return (sol1.is_none() || relative_eq!(&m * sol1.unwrap(), b1, epsilon = 1.0e-6)) &&
(sol2.is_none() || relative_eq!(&m * sol2.unwrap(), b2, epsilon = 1.0e-6))
}
return true;
}
fn lu_solve_static(m: Matrix4<f64>) -> bool {
let lu = m.lu();
let b1 = Vector4::new_random();
let b2 = Matrix4x3::new_random();
let sol1 = lu.solve(&b1);
let sol2 = lu.solve(&b2);
return (sol1.is_none() || relative_eq!(&m * sol1.unwrap(), b1, epsilon = 1.0e-6)) &&
(sol2.is_none() || relative_eq!(&m * sol2.unwrap(), b2, epsilon = 1.0e-6))
}
fn lu_inverse(n: usize) -> bool {
let n = cmp::max(1, cmp::min(n, 15)); // To avoid slowing down the test too much.
let m = DMatrix::<f64>::new_random(n, n);
let mut l = m.lower_triangle();
let mut u = m.upper_triangle();
// Ensure the matrix is well conditioned for inversion.
l.fill_diagonal(1.0);
u.fill_diagonal(1.0);
let m = l * u;
let m1 = m.clone().lu().try_inverse().unwrap();
let id1 = &m * &m1;
let id2 = &m1 * &m;
return id1.is_identity(1.0e-5) && id2.is_identity(1.0e-5);
}
fn lu_inverse_static(m: Matrix4<f64>) -> bool {
let lu = m.lu();
if let Some(m1) = lu.try_inverse() {
let id1 = &m * &m1;
let id2 = &m1 * &m;
id1.is_identity(1.0e-5) && id2.is_identity(1.0e-5)
}
else {
true
}
}
}
}