Add dedicated implementations of SVD for 2x2 and 3x3 real matrices.

benches/linalg/svd.rs

@@ -1,4 +1,18 @@
-use na::{Matrix4, SVD};
+use na::{Matrix2, Matrix3, Matrix4, SVD};
+
+fn svd_decompose_2x2_f32(bh: &mut criterion::Criterion) {
+    let m = Matrix2::<f32>::new_random();
+    bh.bench_function("svd_decompose_2x2", move |bh| {
+        bh.iter(|| std::hint::black_box(SVD::new_unordered(m.clone(), true, true)))
+    });
+}
+
+fn svd_decompose_3x3_f32(bh: &mut criterion::Criterion) {
+    let m = Matrix3::<f32>::new_random();
+    bh.bench_function("svd_decompose_3x3", move |bh| {
+        bh.iter(|| std::hint::black_box(SVD::new_unordered(m.clone(), true, true)))
+    });
+}
 
 fn svd_decompose_4x4(bh: &mut criterion::Criterion) {
     let m = Matrix4::<f64>::new_random();
@@ -114,6 +128,8 @@ fn pseudo_inverse_200x200(bh: &mut criterion::Criterion) {
 
 criterion_group!(
     svd,
+    svd_decompose_2x2_f32,
+    svd_decompose_3x3_f32,
     svd_decompose_4x4,
     svd_decompose_10x10,
     svd_decompose_100x100,

src/linalg/mod.rs

@@ -24,6 +24,8 @@ mod qr;
 mod schur;
 mod solve;
 mod svd;
+mod svd2;
+mod svd3;
 mod symmetric_eigen;
 mod symmetric_tridiagonal;
 mod udu;

src/linalg/qr.rs

@@ -147,6 +147,11 @@ where
         &self.qr
     }
 
+    #[must_use]
+    pub(crate) fn diag_internal(&self) -> &OVector<T, DimMinimum<R, C>> {
+        &self.diag
+    }
+
     /// Multiplies the provided matrix by the transpose of the `Q` matrix of this decomposition.
     pub fn q_tr_mul<R2: Dim, C2: Dim, S2>(&self, rhs: &mut Matrix<T, R2, C2, S2>)
     // TODO: do we need a static constraint on the number of rows of rhs?

src/linalg/svd.rs

@@ -1,5 +1,6 @@
 #[cfg(feature = "serde-serialize-no-std")]
 use serde::{Deserialize, Serialize};
+use std::any::TypeId;
 
 use approx::AbsDiffEq;
 use num::{One, Zero};
@@ -9,7 +10,7 @@ use crate::base::{DefaultAllocator, Matrix, Matrix2x3, OMatrix, OVector, Vector2
 use crate::constraint::{SameNumberOfRows, ShapeConstraint};
 use crate::dimension::{Dim, DimDiff, DimMin, DimMinimum, DimSub, U1};
 use crate::storage::Storage;
-use crate::RawStorage;
+use crate::{Matrix2, Matrix3, RawStorage, U2, U3};
 use simba::scalar::{ComplexField, RealField};
 
 use crate::linalg::givens::GivensRotation;
@@ -118,6 +119,25 @@ where
         );
         let (nrows, ncols) = matrix.shape_generic();
         let min_nrows_ncols = nrows.min(ncols);
+
+        if TypeId::of::<OMatrix<T, R, C>>() == TypeId::of::<Matrix2<T::RealField>>()
+            && TypeId::of::<Self>() == TypeId::of::<SVD<T::RealField, U2, U2>>()
+        {
+            // SAFETY: the reference transmutes are OK since we checked that the types match exactly.
+            let matrix: &Matrix2<T::RealField> = unsafe { std::mem::transmute(&matrix) };
+            let result = super::svd2::svd2(matrix, compute_u, compute_v);
+            let typed_result: &Self = unsafe { std::mem::transmute(&result) };
+            return Some(typed_result.clone());
+        } else if TypeId::of::<OMatrix<T, R, C>>() == TypeId::of::<Matrix3<T::RealField>>()
+            && TypeId::of::<Self>() == TypeId::of::<SVD<T::RealField, U3, U3>>()
+        {
+            // SAFETY: the reference transmutes are OK since we checked that the types match exactly.
+            let matrix: &Matrix3<T::RealField> = unsafe { std::mem::transmute(&matrix) };
+            let result = super::svd3::svd3(matrix, compute_u, compute_v, eps, max_niter);
+            let typed_result: &Self = unsafe { std::mem::transmute(&result) };
+            return Some(typed_result.clone());
+        }
+
         let dim = min_nrows_ncols.value();
 
         let m_amax = matrix.camax();

src/linalg/svd2.rs

@@ -0,0 +1,43 @@
+use crate::{Matrix2, RealField, Vector2, SVD, U2};
+
+// Implementation of the 2D SVD from https://ieeexplore.ieee.org/document/486688
+// See also https://scicomp.stackexchange.com/questions/8899/robust-algorithm-for-2-times-2-svd
+pub fn svd2<T: RealField>(m: &Matrix2<T>, compute_u: bool, compute_v: bool) -> SVD<T, U2, U2> {
+    let half: T = crate::convert(0.5);
+    let one: T = crate::convert(1.0);
+
+    let e = (m.m11.clone() + m.m22.clone()) * half.clone();
+    let f = (m.m11.clone() - m.m22.clone()) * half.clone();
+    let g = (m.m21.clone() + m.m12.clone()) * half.clone();
+    let h = (m.m21.clone() - m.m12.clone()) * half.clone();
+    let q = (e.clone() * e.clone() + h.clone() * h.clone()).sqrt();
+    let r = (f.clone() * f.clone() + g.clone() * g.clone()).sqrt();
+    let sx = q.clone() + r.clone();
+    let sy = q - r;
+    let sy_sign = if sy < T::zero() { -one.clone() } else { one };
+    let singular_values = Vector2::new(sx, sy * sy_sign.clone());
+
+    if compute_u || compute_v {
+        let a1 = g.atan2(f);
+        let a2 = h.atan2(e);
+        let theta = (a2.clone() - a1.clone()) * half.clone();
+        let phi = (a2 + a1) * half;
+        let (st, ct) = theta.sin_cos();
+        let (sp, cp) = phi.sin_cos();
+
+        let u = Matrix2::new(cp.clone(), -sp.clone(), sp, cp);
+        let v_t = Matrix2::new(ct.clone(), -st.clone(), st * sy_sign.clone(), ct * sy_sign);
+
+        SVD {
+            u: if compute_u { Some(u) } else { None },
+            singular_values,
+            v_t: if compute_v { Some(v_t) } else { None },
+        }
+    } else {
+        SVD {
+            u: None,
+            singular_values,
+            v_t: None,
+        }
+    }
+}

src/linalg/svd3.rs

@@ -0,0 +1,25 @@
+use crate::{Matrix3, SVD, U3};
+use simba::scalar::RealField;
+
+// For the 3x3 case, on the GPU, it is much more efficient to compute the SVD
+// using an eigendecomposition followed by a QR decomposition.
+pub fn svd3<T: RealField>(
+    m: &Matrix3<T>,
+    compute_u: bool,
+    compute_v: bool,
+    eps: T,
+    niter: usize,
+) -> Option<SVD<T, U3, U3>> {
+    let s = m.tr_mul(&m);
+    let v = s.try_symmetric_eigen(eps, niter)?.eigenvectors;
+    let b = m * &v;
+
+    let qr = b.qr();
+    let singular_values = qr.diag_internal().map(|e| e.abs());
+
+    Some(SVD {
+        u: if compute_u { Some(qr.q()) } else { None },
+        singular_values,
+        v_t: if compute_v { Some(v.transpose()) } else { None },
+    })
+}

tests/linalg/svd.rs

@@ -97,6 +97,18 @@ mod proptest_tests {
                         prop_assert!(v_t.is_orthogonal(1.0e-5));
                     }
 
+                    #[test]
+                    fn svd_static_square_3x3(m in matrix3_($scalar)) {
+                        let svd = m.svd(true, true);
+                        let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
+                        let ds = Matrix3::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
+
+                        prop_assert!(s.iter().all(|e| *e >= 0.0));
+                        prop_assert!(relative_eq!(m, u * ds * v_t, epsilon = 1.0e-5));
+                        prop_assert!(u.is_orthogonal(1.0e-5));
+                        prop_assert!(v_t.is_orthogonal(1.0e-5));
+                    }
+
                     #[test]
                     fn svd_pseudo_inverse(m in dmatrix_($scalar)) {
                         let svd = m.clone().svd(true, true);