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nalgebra/tests/linalg/svd.rs

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Rust
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use crate::utils::is_sorted_descending;
use na::{DMatrix, Matrix6};
#[cfg(feature = "proptest-support")]
mod proptest_tests {
macro_rules! gen_tests(
($module: ident, $scalar: expr, $scalar_type: ty) => {
mod $module {
use na::{
DMatrix, DVector, Matrix2, Matrix3, Matrix4,
ComplexField
};
use std::cmp;
#[allow(unused_imports)]
use crate::core::helper::{RandScalar, RandComplex};
use crate::proptest::*;
use proptest::{prop_assert, proptest};
use crate::utils::is_sorted_descending;
proptest! {
#[test]
fn svd(m in dmatrix_($scalar)) {
let svd = m.clone().svd(true, true);
let recomp_m = svd.clone().recompose().unwrap();
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = DMatrix::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
prop_assert!(s.iter().all(|e| *e >= 0.0));
prop_assert!(relative_eq!(&u * ds * &v_t, recomp_m, epsilon = 1.0e-5));
prop_assert!(relative_eq!(m, recomp_m, epsilon = 1.0e-5));
prop_assert!(is_sorted_descending(s.as_slice()));
}
#[test]
fn svd_static_5_3(m in matrix5x3_($scalar)) {
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = Matrix3::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
prop_assert!(s.iter().all(|e| *e >= 0.0));
prop_assert!(relative_eq!(m, &u * ds * &v_t, epsilon = 1.0e-5));
prop_assert!(u.is_orthogonal(1.0e-5));
prop_assert!(v_t.is_orthogonal(1.0e-5));
prop_assert!(is_sorted_descending(s.as_slice()));
}
#[test]
fn svd_static_5_2(m in matrix5x2_($scalar)) {
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = Matrix2::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
prop_assert!(s.iter().all(|e| *e >= 0.0));
prop_assert!(relative_eq!(m, &u * ds * &v_t, epsilon = 1.0e-5));
prop_assert!(u.is_orthogonal(1.0e-5));
prop_assert!(v_t.is_orthogonal(1.0e-5));
prop_assert!(is_sorted_descending(s.as_slice()));
}
#[test]
fn svd_static_3_5(m in matrix3x5_($scalar)) {
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = Matrix3::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
prop_assert!(s.iter().all(|e| *e >= 0.0));
prop_assert!(relative_eq!(m, u * ds * v_t, epsilon = 1.0e-5));
prop_assert!(is_sorted_descending(s.as_slice()));
}
#[test]
fn svd_static_2_5(m in matrix2x5_($scalar)) {
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = Matrix2::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
prop_assert!(s.iter().all(|e| *e >= 0.0));
prop_assert!(relative_eq!(m, u * ds * v_t, epsilon = 1.0e-5));
prop_assert!(is_sorted_descending(s.as_slice()));
}
#[test]
fn svd_static_square(m in matrix4_($scalar)) {
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = Matrix4::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
prop_assert!(s.iter().all(|e| *e >= 0.0));
prop_assert!(relative_eq!(m, u * ds * v_t, epsilon = 1.0e-5));
prop_assert!(u.is_orthogonal(1.0e-5));
prop_assert!(v_t.is_orthogonal(1.0e-5));
prop_assert!(is_sorted_descending(s.as_slice()));
}
#[test]
fn svd_static_square_2x2(m in matrix2_($scalar)) {
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = Matrix2::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
prop_assert!(s.iter().all(|e| *e >= 0.0));
prop_assert!(relative_eq!(m, u * ds * v_t, epsilon = 1.0e-5));
prop_assert!(u.is_orthogonal(1.0e-5));
prop_assert!(v_t.is_orthogonal(1.0e-5));
prop_assert!(is_sorted_descending(s.as_slice()));
}
#[test]
fn svd_static_square_3x3(m in matrix3_($scalar)) {
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = Matrix3::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
prop_assert!(s.iter().all(|e| *e >= 0.0));
prop_assert!(relative_eq!(m, u * ds * v_t, epsilon = 1.0e-5));
prop_assert!(u.is_orthogonal(1.0e-5));
prop_assert!(v_t.is_orthogonal(1.0e-5));
prop_assert!(is_sorted_descending(s.as_slice()));
}
#[test]
fn svd_pseudo_inverse(m in dmatrix_($scalar)) {
let svd = m.clone().svd(true, true);
let pinv = svd.pseudo_inverse(1.0e-10).unwrap();
if m.nrows() > m.ncols() {
prop_assert!((pinv * m).is_identity(1.0e-5))
} else {
prop_assert!((m * pinv).is_identity(1.0e-5))
}
}
#[test]
fn svd_solve(n in PROPTEST_MATRIX_DIM, nb in PROPTEST_MATRIX_DIM) {
let n = cmp::max(1, cmp::min(n, 10));
let nb = cmp::min(nb, 10);
let m = DMatrix::<$scalar_type>::new_random(n, n).map(|e| e.0);
let svd = m.clone().svd(true, true);
if svd.rank(1.0e-7) == n {
let b1 = DVector::<$scalar_type>::new_random(n).map(|e| e.0);
let b2 = DMatrix::<$scalar_type>::new_random(n, nb).map(|e| e.0);
let sol1 = svd.solve(&b1, 1.0e-7).unwrap();
let sol2 = svd.solve(&b2, 1.0e-7).unwrap();
let recomp = svd.recompose().unwrap();
prop_assert!(relative_eq!(m, recomp, epsilon = 1.0e-6));
prop_assert!(relative_eq!(&m * &sol1, b1, epsilon = 1.0e-6));
prop_assert!(relative_eq!(&m * &sol2, b2, epsilon = 1.0e-6));
}
}
}
}
}
);
gen_tests!(complex, complex_f64(), RandComplex<f64>);
gen_tests!(f64, PROPTEST_F64, RandScalar<f64>);
}
// Test proposed on the issue #176 of rulinalg.
#[test]
#[rustfmt::skip]
fn svd_singular() {
let m = DMatrix::from_row_slice(24, 24, &[
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 1.0, 1.0,
0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0]);
let svd = m.clone().svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = DMatrix::from_diagonal(&s);
assert!(s.iter().all(|e| *e >= 0.0));
assert!(is_sorted_descending(s.as_slice()));
assert!(u.is_orthogonal(1.0e-5));
assert!(v_t.is_orthogonal(1.0e-5));
assert_relative_eq!(m, &u * ds * &v_t, epsilon = 1.0e-5);
}
// Same as the previous test but with one additional row.
#[test]
#[rustfmt::skip]
fn svd_singular_vertical() {
let m = DMatrix::from_row_slice(25, 24, &[
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 1.0, 1.0,
0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0]);
let svd = m.clone().svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = DMatrix::from_diagonal(&s);
assert!(s.iter().all(|e| *e >= 0.0));
assert!(is_sorted_descending(s.as_slice()));
assert_relative_eq!(m, &u * ds * &v_t, epsilon = 1.0e-5);
}
// Same as the previous test but with one additional column.
#[test]
#[rustfmt::skip]
fn svd_singular_horizontal() {
let m = DMatrix::from_row_slice(24, 25, &[
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 1.0, 1.0, 1.0,
0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]);
let svd = m.clone().svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = DMatrix::from_diagonal(&s);
assert!(s.iter().all(|e| *e >= 0.0));
assert!(is_sorted_descending(s.as_slice()));
assert_relative_eq!(m, &u * ds * &v_t, epsilon = 1.0e-5);
}
#[test]
fn svd_zeros() {
let m = DMatrix::from_element(10, 10, 0.0);
let svd = m.clone().svd(true, true);
assert_eq!(Ok(m), svd.recompose());
}
#[test]
fn svd_identity() {
let m = DMatrix::<f64>::identity(10, 10);
let svd = m.clone().svd(true, true);
assert_eq!(Ok(m), svd.recompose());
let m = DMatrix::<f64>::identity(10, 15);
let svd = m.clone().svd(true, true);
assert_eq!(Ok(m), svd.recompose());
let m = DMatrix::<f64>::identity(15, 10);
let svd = m.clone().svd(true, true);
assert_eq!(Ok(m), svd.recompose());
}
#[test]
#[rustfmt::skip]
fn svd_with_delimited_subproblem() {
let mut m = DMatrix::<f64>::from_element(10, 10, 0.0);
m[(0, 0)] = 1.0; m[(0, 1)] = 2.0;
m[(1, 1)] = 0.0; m[(1, 2)] = 3.0;
m[(2, 2)] = 4.0; m[(2, 3)] = 5.0;
m[(3, 3)] = 6.0; m[(3, 4)] = 0.0;
m[(4, 4)] = 8.0; m[(3, 5)] = 9.0;
m[(5, 5)] = 10.0; m[(3, 6)] = 11.0;
m[(6, 6)] = 12.0; m[(3, 7)] = 12.0;
m[(7, 7)] = 14.0; m[(3, 8)] = 13.0;
m[(8, 8)] = 16.0; m[(3, 9)] = 17.0;
m[(9, 9)] = 18.0;
let svd = m.clone().svd(true, true);
assert_relative_eq!(m, svd.recompose().unwrap(), epsilon = 1.0e-7);
// Rectangular versions.
let mut m = DMatrix::<f64>::from_element(15, 10, 0.0);
m[(0, 0)] = 1.0; m[(0, 1)] = 2.0;
m[(1, 1)] = 0.0; m[(1, 2)] = 3.0;
m[(2, 2)] = 4.0; m[(2, 3)] = 5.0;
m[(3, 3)] = 6.0; m[(3, 4)] = 0.0;
m[(4, 4)] = 8.0; m[(3, 5)] = 9.0;
m[(5, 5)] = 10.0; m[(3, 6)] = 11.0;
m[(6, 6)] = 12.0; m[(3, 7)] = 12.0;
m[(7, 7)] = 14.0; m[(3, 8)] = 13.0;
m[(8, 8)] = 16.0; m[(3, 9)] = 17.0;
m[(9, 9)] = 18.0;
let svd = m.clone().svd(true, true);
assert_relative_eq!(m, svd.recompose().unwrap(), epsilon = 1.0e-7);
let svd = m.transpose().svd(true, true);
assert_relative_eq!(m.transpose(), svd.recompose().unwrap(), epsilon = 1.0e-7);
}
#[test]
#[rustfmt::skip]
fn svd_fail() {
let m = Matrix6::new(
0.9299319121545955, 0.9955870335651049, 0.8824725266413644, 0.28966880207132295, 0.06102723649846409, 0.9311880746048009,
0.5938395242304351, 0.8398522876024204, 0.06672831951963198, 0.9941213119963099, 0.9431846038057834, 0.8159885168706427,
0.9121962883152357, 0.6471119669367571, 0.4823309702814407, 0.6420516076705516, 0.7731203925207113, 0.7424069470756647,
0.07311092531259344, 0.5579247949052946, 0.14518764691585773, 0.03502980663114896, 0.7991329455957719, 0.4929930019965745,
0.12293810556077789, 0.6617084679545999, 0.9002240700227326, 0.027153062135304884, 0.3630189466989524, 0.18207502727558866,
0.843196731466686, 0.08951878746549924, 0.7533450877576973, 0.009558876499740077, 0.9429679490873482, 0.9355764454129878);
// Check unordered ...
let svd = m.clone().svd_unordered(true, true);
let recomp = svd.recompose().unwrap();
assert_relative_eq!(m, recomp, epsilon = 1.0e-5);
// ... and ordered SVD.
let svd = m.clone().svd(true, true);
let recomp = svd.recompose().unwrap();
assert_relative_eq!(m, recomp, epsilon = 1.0e-5);
}
#[test]
#[rustfmt::skip]
fn svd3_fail() {
// NOTE: this matrix fails the special case done for 3x3 SVDs.
// It was found on an actual application using SVD as part of the minimization of a
// quadratic error function.
let m = nalgebra::matrix![
0.0, 1.0, 0.0;
0.0, 1.7320508075688772, 0.0;
0.0, 0.0, 0.0
];
// Check unordered ...
let svd = m.svd_unordered(true, true);
let recomp = svd.recompose().unwrap();
assert_relative_eq!(m, recomp, epsilon = 1.0e-5);
// ... and ordered SVD.
let svd = m.svd(true, true);
let recomp = svd.recompose().unwrap();
assert_relative_eq!(m, recomp, epsilon = 1.0e-5);
}
#[test]
fn svd_err() {
let m = DMatrix::from_element(10, 10, 0.0);
let svd = m.clone().svd(false, false);
assert_eq!(
Err("SVD recomposition: U and V^t have not been computed."),
svd.clone().recompose()
);
assert_eq!(
Err("SVD pseudo inverse: the epsilon must be non-negative."),
svd.clone().pseudo_inverse(-1.0)
);
}
#[test]
#[rustfmt::skip]
fn svd_sorted() {
let reference = nalgebra::matrix![
1.0, 2.0, 3.0, 4.0;
5.0, 6.0, 7.0, 8.0;
9.0, 10.0, 11.0, 12.0
];
let mut svd = nalgebra::SVD {
singular_values: nalgebra::matrix![1.72261225; 2.54368356e+01; 5.14037515e-16],
u: Some(nalgebra::matrix![
-0.88915331, -0.20673589, 0.40824829;
-0.25438183, -0.51828874, -0.81649658;
0.38038964, -0.82984158, 0.40824829
]),
v_t: Some(nalgebra::matrix![
0.73286619, 0.28984978, -0.15316664, -0.59618305;
-0.40361757, -0.46474413, -0.52587069, -0.58699725;
0.44527162, -0.83143156, 0.32704826, 0.05911168
]),
};
assert_relative_eq!(
svd.recompose().expect("valid SVD"),
reference,
epsilon = 1.0e-5
);
svd.sort_by_singular_values();
// Ensure successful sorting
assert_relative_eq!(svd.singular_values.x, 2.54368356e+01, epsilon = 1.0e-5);
// Ensure that the sorted components represent the same decomposition
assert_relative_eq!(
svd.recompose().expect("valid SVD"),
reference,
epsilon = 1.0e-5
);
}
#[test]
fn dynamic_square_matrix_polar_decomposition() {
let m = DMatrix::<f64>::new_random(10, 10);
let svd = m.clone().svd(true, true);
let (p,u) = svd.to_polar().unwrap();
assert_relative_eq!(m, &p*&u, epsilon = 1.0e-5);
// unitary check
assert_eq!(true, u.is_orthogonal(1.0e-5));
// hermitian check
assert_relative_eq!(p, p.adjoint(), epsilon = 1.0e-5);
}
#[test]
fn dynamic_rectangular_matrix_polar_decomposition() {
let m = DMatrix::<f64>::new_random(7, 5);
let svd = m.clone().svd(true, true);
let (p,u) = svd.to_polar().unwrap();
assert_relative_eq!(m, &p*&u, epsilon = 1.0e-5);
// semi-unitary check
assert_eq!(true, (u.is_orthogonal(1.0e-5)));
// hermitian check
assert_relative_eq!(p, p.adjoint(), epsilon = 1.0e-5);
}
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