forked from M-Labs/nalgebra
Fix numerical issue on SVD with near-identity matrix (#1369)
* fix: Normalize the column once more The column may not be normalized if the `factor` is on a scale of 1e-40. Possibly, f32 just runs out of precision. There is likely a better solution to the problem. * chore: Add test that fails before fix * chore: add comment providing details on the householder fix. * chore: rename regression test --------- Co-authored-by: Sébastien Crozet <sebcrozet@dimforge.com>
This commit is contained in:
parent
749a9fee17
commit
c475c4000c
@ -34,6 +34,17 @@ pub fn reflection_axis_mut<T: ComplexField, D: Dim, S: StorageMut<T, D>>(
|
|||||||
|
|
||||||
if !factor.is_zero() {
|
if !factor.is_zero() {
|
||||||
column.unscale_mut(factor.sqrt());
|
column.unscale_mut(factor.sqrt());
|
||||||
|
|
||||||
|
// Normalize again, making sure the vector is unit-sized.
|
||||||
|
// If `factor` had a very small value, the first normalization
|
||||||
|
// (dividing by `factor.sqrt()`) might end up with a slightly
|
||||||
|
// non-unit vector (especially when using 32-bits float).
|
||||||
|
// Decompositions strongly rely on that unit-vector property,
|
||||||
|
// so we run a second normalization (that is much more numerically
|
||||||
|
// stable since the norm is close to 1) to ensure it has a unit
|
||||||
|
// size.
|
||||||
|
let _ = column.normalize_mut();
|
||||||
|
|
||||||
(-signed_norm, true)
|
(-signed_norm, true)
|
||||||
} else {
|
} else {
|
||||||
// TODO: not sure why we don't have a - sign here.
|
// TODO: not sure why we don't have a - sign here.
|
||||||
|
@ -1,4 +1,4 @@
|
|||||||
use na::DMatrix;
|
use na::{DMatrix, Matrix3};
|
||||||
|
|
||||||
#[cfg(feature = "proptest-support")]
|
#[cfg(feature = "proptest-support")]
|
||||||
mod proptest_tests {
|
mod proptest_tests {
|
||||||
@ -116,6 +116,31 @@ fn symmetric_eigen_singular_24x24() {
|
|||||||
);
|
);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
// Regression test for #1368
|
||||||
|
#[test]
|
||||||
|
fn very_small_deviation_from_identity_issue_1368() {
|
||||||
|
let m = Matrix3::<f32>::new(
|
||||||
|
1.0,
|
||||||
|
3.1575704e-23,
|
||||||
|
8.1146196e-23,
|
||||||
|
3.1575704e-23,
|
||||||
|
1.0,
|
||||||
|
1.7471054e-22,
|
||||||
|
8.1146196e-23,
|
||||||
|
1.7471054e-22,
|
||||||
|
1.0,
|
||||||
|
);
|
||||||
|
|
||||||
|
for v in m
|
||||||
|
.try_symmetric_eigen(f32::EPSILON, 0)
|
||||||
|
.unwrap()
|
||||||
|
.eigenvalues
|
||||||
|
.into_iter()
|
||||||
|
{
|
||||||
|
assert_relative_eq!(*v, 1.);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
// #[cfg(feature = "arbitrary")]
|
// #[cfg(feature = "arbitrary")]
|
||||||
// quickcheck! {
|
// quickcheck! {
|
||||||
// TODO: full eigendecomposition is not implemented yet because of its complexity when some
|
// TODO: full eigendecomposition is not implemented yet because of its complexity when some
|
||||||
|
Loading…
Reference in New Issue
Block a user