Move the col_piv_qr method to the decomposition module.

src/linalg/col_piv_qr.rs

@@ -331,16 +331,3 @@ where
         res * self.p.determinant()
     }
 }
-
-impl<N: ComplexField, R: DimMin<C>, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S>
-where
-    DefaultAllocator: Allocator<N, R, C>
-        + Allocator<N, R>
-        + Allocator<N, DimMinimum<R, C>>
-        + Allocator<(usize, usize), DimMinimum<R, C>>,
-{
-    /// Computes the QR decomposition (with column pivoting) of this matrix.
-    pub fn col_piv_qr(self) -> ColPivQR<N, R, C> {
-        ColPivQR::new(self.into_owned())
-    }
-}

src/linalg/decomposition.rs

@@ -1,8 +1,8 @@
 use crate::storage::Storage;
 use crate::{
-    Allocator, Bidiagonal, Cholesky, ComplexField, DefaultAllocator, Dim, DimDiff, DimMin,
+    Allocator, Bidiagonal, Cholesky, ColPivQR, ComplexField, DefaultAllocator, Dim, DimDiff,
-    DimMinimum, DimSub, FullPivLU, Hessenberg, Matrix, Schur, SymmetricEigen, SymmetricTridiagonal,
+    DimMin, DimMinimum, DimSub, FullPivLU, Hessenberg, Matrix, Schur, SymmetricEigen,
-    LU, QR, SVD, U1,
+    SymmetricTridiagonal, LU, QR, SVD, U1,
 };
 
 /// # Rectangular matrix decomposition
@@ -13,8 +13,9 @@ use crate::{
 /// | Decomposition            | Factors             | Details |
 /// | -------------------------|---------------------|--------------|
 /// | QR                       | `Q * R`             | `Q` is an unitary matrix, and `R` is upper-triangular. |
+/// | QR with column pivoting  | `Q * R * P⁻¹`       | `Q` is an unitary matrix, and `R` is upper-triangular. `P` is a permutation matrix. |
 /// | LU with partial pivoting | `P⁻¹ * L * U`       | `L` is lower-triangular with a diagonal filled with `1` and `U` is upper-triangular. `P` is a permutation matrix. |
-/// | LU with full pivoting    | `P⁻¹ * L * U ~ Q⁻¹` | `L` is lower-triangular with a diagonal filled with `1` and `U` is upper-triangular. `P` and `Q` are permutation matrices. |
+/// | LU with full pivoting    | `P⁻¹ * L * U * Q⁻¹` | `L` is lower-triangular with a diagonal filled with `1` and `U` is upper-triangular. `P` and `Q` are permutation matrices. |
 /// | SVD                      | `U * Σ * Vᵀ`        | `U` and `V` are two orthogonal matrices and `Σ` is a diagonal matrix containing the singular values. |
 impl<N: ComplexField, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
     /// Computes the bidiagonalization using householder reflections.
@@ -60,6 +61,18 @@ impl<N: ComplexField, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         QR::new(self.into_owned())
     }
 
+    /// Computes the QR decomposition (with column pivoting) of this matrix.
+    pub fn col_piv_qr(self) -> ColPivQR<N, R, C>
+    where
+        R: DimMin<C>,
+        DefaultAllocator: Allocator<N, R, C>
+            + Allocator<N, R>
+            + Allocator<N, DimMinimum<R, C>>
+            + Allocator<(usize, usize), DimMinimum<R, C>>,
+    {
+        ColPivQR::new(self.into_owned())
+    }
+
     /// Computes the Singular Value Decomposition using implicit shift.
     pub fn svd(self, compute_u: bool, compute_v: bool) -> SVD<N, R, C>
     where