diff --git a/src/linalg/col_piv_qr.rs b/src/linalg/col_piv_qr.rs
new file mode 100644
index 00000000..4fa97482
--- /dev/null
+++ b/src/linalg/col_piv_qr.rs
@@ -0,0 +1,333 @@
+use num::Zero;
+#[cfg(feature = "serde-serialize")]
+use serde::{Deserialize, Serialize};
+
+use crate::allocator::{Allocator, Reallocator};
+use crate::base::{DefaultAllocator, Matrix, MatrixMN, MatrixN, Unit, VectorN};
+use crate::constraint::{SameNumberOfRows, ShapeConstraint};
+use crate::dimension::{Dim, DimMin, DimMinimum, U1};
+use crate::storage::{Storage, StorageMut};
+use crate::ComplexField;
+
+use crate::geometry::Reflection;
+use crate::linalg::{householder, PermutationSequence};
+
+/// The QR decomposition (with column pivoting) of a general matrix.
+#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
+#[cfg_attr(
+    feature = "serde-serialize",
+    serde(bound(serialize = "DefaultAllocator: Allocator<N, R, C> +
+                           Allocator<N, DimMinimum<R, C>>,
+         MatrixMN<N, R, C>: Serialize,
+         PermutationSequence<DimMinimum<R, C>>: Serialize,
+         VectorN<N, DimMinimum<R, C>>: Serialize"))
+)]
+#[cfg_attr(
+    feature = "serde-serialize",
+    serde(bound(deserialize = "DefaultAllocator: Allocator<N, R, C> +
+                           Allocator<N, DimMinimum<R, C>>,
+         MatrixMN<N, R, C>: Deserialize<'de>,
+         PermutationSequence<DimMinimum<R, C>>: Deserialize<'de>,
+         VectorN<N, DimMinimum<R, C>>: Deserialize<'de>"))
+)]
+#[derive(Clone, Debug)]
+pub struct ColPivQR<N: ComplexField, R: DimMin<C>, C: Dim>
+where
+    DefaultAllocator: Allocator<N, R, C>
+        + Allocator<N, DimMinimum<R, C>>
+        + Allocator<(usize, usize), DimMinimum<R, C>>,
+{
+    col_piv_qr: MatrixMN<N, R, C>,
+    p: PermutationSequence<DimMinimum<R, C>>,
+    diag: VectorN<N, DimMinimum<R, C>>,
+}
+
+impl<N: ComplexField, R: DimMin<C>, C: Dim> Copy for ColPivQR<N, R, C>
+where
+    DefaultAllocator: Allocator<N, R, C>
+        + Allocator<N, DimMinimum<R, C>>
+        + Allocator<(usize, usize), DimMinimum<R, C>>,
+    MatrixMN<N, R, C>: Copy,
+    PermutationSequence<DimMinimum<R, C>>: Copy,
+    VectorN<N, DimMinimum<R, C>>: Copy,
+{
+}
+
+impl<N: ComplexField, R: DimMin<C>, C: Dim> ColPivQR<N, R, C>
+where
+    DefaultAllocator: Allocator<N, R, C>
+        + Allocator<N, R>
+        + Allocator<N, DimMinimum<R, C>>
+        + Allocator<(usize, usize), DimMinimum<R, C>>,
+{
+    /// Computes the ColPivQR decomposition using householder reflections.
+    pub fn new(mut matrix: MatrixMN<N, R, C>) -> Self {
+        let (nrows, ncols) = matrix.data.shape();
+        let min_nrows_ncols = nrows.min(ncols);
+        let mut p = PermutationSequence::identity_generic(min_nrows_ncols);
+
+        let mut diag = unsafe { MatrixMN::new_uninitialized_generic(min_nrows_ncols, U1) };
+
+        if min_nrows_ncols.value() == 0 {
+            return ColPivQR {
+                col_piv_qr: matrix,
+                p,
+                diag,
+            };
+        }
+
+        for i in 0..min_nrows_ncols.value() {
+            let piv = matrix.slice_range(i.., i..).icamax_full();
+            let col_piv = piv.1 + i;
+            matrix.swap_columns(i, col_piv);
+            p.append_permutation(i, col_piv);
+
+            householder::clear_column_unchecked(&mut matrix, &mut diag[i], i, 0, None);
+        }
+
+        ColPivQR {
+            col_piv_qr: matrix,
+            p,
+            diag,
+        }
+    }
+
+    /// Retrieves the upper trapezoidal submatrix `R` of this decomposition.
+    #[inline]
+    pub fn r(&self) -> MatrixMN<N, DimMinimum<R, C>, C>
+    where
+        DefaultAllocator: Allocator<N, DimMinimum<R, C>, C>,
+    {
+        let (nrows, ncols) = self.col_piv_qr.data.shape();
+        let mut res = self
+            .col_piv_qr
+            .rows_generic(0, nrows.min(ncols))
+            .upper_triangle();
+        res.set_partial_diagonal(self.diag.iter().map(|e| N::from_real(e.modulus())));
+        res
+    }
+
+    /// Retrieves the upper trapezoidal submatrix `R` of this decomposition.
+    ///
+    /// This is usually faster than `r` but consumes `self`.
+    #[inline]
+    pub fn unpack_r(self) -> MatrixMN<N, DimMinimum<R, C>, C>
+    where
+        DefaultAllocator: Reallocator<N, R, C, DimMinimum<R, C>, C>,
+    {
+        let (nrows, ncols) = self.col_piv_qr.data.shape();
+        let mut res = self
+            .col_piv_qr
+            .resize_generic(nrows.min(ncols), ncols, N::zero());
+        res.fill_lower_triangle(N::zero(), 1);
+        res.set_partial_diagonal(self.diag.iter().map(|e| N::from_real(e.modulus())));
+        res
+    }
+
+    /// Computes the orthogonal matrix `Q` of this decomposition.
+    pub fn q(&self) -> MatrixMN<N, R, DimMinimum<R, C>>
+    where
+        DefaultAllocator: Allocator<N, R, DimMinimum<R, C>>,
+    {
+        let (nrows, ncols) = self.col_piv_qr.data.shape();
+
+        // NOTE: we could build the identity matrix and call q_mul on it.
+        // Instead we don't so that we take in account the matrix sparseness.
+        let mut res = Matrix::identity_generic(nrows, nrows.min(ncols));
+        let dim = self.diag.len();
+
+        for i in (0..dim).rev() {
+            let axis = self.col_piv_qr.slice_range(i.., i);
+            // TODO: sometimes, the axis might have a zero magnitude.
+            let refl = Reflection::new(Unit::new_unchecked(axis), N::zero());
+
+            let mut res_rows = res.slice_range_mut(i.., i..);
+            refl.reflect_with_sign(&mut res_rows, self.diag[i].signum());
+        }
+
+        res
+    }
+    /// Retrieves the column permutation of this decomposition.
+    #[inline]
+    pub fn p(&self) -> &PermutationSequence<DimMinimum<R, C>> {
+        &self.p
+    }
+
+    /// Unpacks this decomposition into its two matrix factors.
+    pub fn unpack(
+        self,
+    ) -> (
+        MatrixMN<N, R, DimMinimum<R, C>>,
+        MatrixMN<N, DimMinimum<R, C>, C>,
+        PermutationSequence<DimMinimum<R, C>>,
+    )
+    where
+        DimMinimum<R, C>: DimMin<C, Output = DimMinimum<R, C>>,
+        DefaultAllocator: Allocator<N, R, DimMinimum<R, C>>
+            + Reallocator<N, R, C, DimMinimum<R, C>, C>
+            + Allocator<(usize, usize), DimMinimum<R, C>>,
+    {
+        (self.q(), self.r(), self.p)
+    }
+
+    #[doc(hidden)]
+    pub fn col_piv_qr_internal(&self) -> &MatrixMN<N, R, C> {
+        &self.col_piv_qr
+    }
+
+    /// 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<N, R2, C2, S2>)
+    where
+        S2: StorageMut<N, R2, C2>,
+    {
+        let dim = self.diag.len();
+
+        for i in 0..dim {
+            let axis = self.col_piv_qr.slice_range(i.., i);
+            let refl = Reflection::new(Unit::new_unchecked(axis), N::zero());
+
+            let mut rhs_rows = rhs.rows_range_mut(i..);
+            refl.reflect_with_sign(&mut rhs_rows, self.diag[i].signum().conjugate());
+        }
+    }
+}
+
+impl<N: ComplexField, D: DimMin<D, Output = D>> ColPivQR<N, D, D>
+where
+    DefaultAllocator:
+        Allocator<N, D, D> + Allocator<N, D> + Allocator<(usize, usize), DimMinimum<D, D>>,
+{
+    /// Solves the linear system `self * x = b`, where `x` is the unknown to be determined.
+    ///
+    /// Returns `None` if `self` is not invertible.
+    pub fn solve<R2: Dim, C2: Dim, S2>(
+        &self,
+        b: &Matrix<N, R2, C2, S2>,
+    ) -> Option<MatrixMN<N, R2, C2>>
+    where
+        S2: StorageMut<N, R2, C2>,
+        ShapeConstraint: SameNumberOfRows<R2, D>,
+        DefaultAllocator: Allocator<N, R2, C2>,
+    {
+        let mut res = b.clone_owned();
+
+        if self.solve_mut(&mut res) {
+            Some(res)
+        } else {
+            None
+        }
+    }
+
+    /// Solves the linear system `self * x = b`, where `x` is the unknown to be determined.
+    ///
+    /// If the decomposed matrix is not invertible, this returns `false` and its input `b` is
+    /// overwritten with garbage.
+    pub fn solve_mut<R2: Dim, C2: Dim, S2>(&self, b: &mut Matrix<N, R2, C2, S2>) -> bool
+    where
+        S2: StorageMut<N, R2, C2>,
+        ShapeConstraint: SameNumberOfRows<R2, D>,
+    {
+        assert_eq!(
+            self.col_piv_qr.nrows(),
+            b.nrows(),
+            "ColPivQR solve matrix dimension mismatch."
+        );
+        assert!(
+            self.col_piv_qr.is_square(),
+            "ColPivQR solve: unable to solve a non-square system."
+        );
+
+        self.q_tr_mul(b);
+        let solved = self.solve_upper_triangular_mut(b);
+        self.p.inv_permute_rows(b);
+
+        solved
+    }
+
+    // TODO: duplicate code from the `solve` module.
+    fn solve_upper_triangular_mut<R2: Dim, C2: Dim, S2>(
+        &self,
+        b: &mut Matrix<N, R2, C2, S2>,
+    ) -> bool
+    where
+        S2: StorageMut<N, R2, C2>,
+        ShapeConstraint: SameNumberOfRows<R2, D>,
+    {
+        let dim = self.col_piv_qr.nrows();
+
+        for k in 0..b.ncols() {
+            let mut b = b.column_mut(k);
+            for i in (0..dim).rev() {
+                let coeff;
+
+                unsafe {
+                    let diag = self.diag.vget_unchecked(i).modulus();
+
+                    if diag.is_zero() {
+                        return false;
+                    }
+
+                    coeff = b.vget_unchecked(i).unscale(diag);
+                    *b.vget_unchecked_mut(i) = coeff;
+                }
+
+                b.rows_range_mut(..i)
+                    .axpy(-coeff, &self.col_piv_qr.slice_range(..i, i), N::one());
+            }
+        }
+
+        true
+    }
+
+    /// Computes the inverse of the decomposed matrix.
+    ///
+    /// Returns `None` if the decomposed matrix is not invertible.
+    pub fn try_inverse(&self) -> Option<MatrixN<N, D>> {
+        assert!(
+            self.col_piv_qr.is_square(),
+            "ColPivQR inverse: unable to compute the inverse of a non-square matrix."
+        );
+
+        // TODO: is there a less naive method ?
+        let (nrows, ncols) = self.col_piv_qr.data.shape();
+        let mut res = MatrixN::identity_generic(nrows, ncols);
+
+        if self.solve_mut(&mut res) {
+            Some(res)
+        } else {
+            None
+        }
+    }
+
+    /// Indicates if the decomposed matrix is invertible.
+    pub fn is_invertible(&self) -> bool {
+        assert!(
+            self.col_piv_qr.is_square(),
+            "ColPivQR: unable to test the invertibility of a non-square matrix."
+        );
+
+        for i in 0..self.diag.len() {
+            if self.diag[i].is_zero() {
+                return false;
+            }
+        }
+
+        true
+    }
+
+    /// Computes the determinant of the decomposed matrix.
+    pub fn determinant(&self) -> N {
+        let dim = self.col_piv_qr.nrows();
+        assert!(
+            self.col_piv_qr.is_square(),
+            "ColPivQR determinant: unable to compute the determinant of a non-square matrix."
+        );
+
+        let mut res = N::one();
+        for i in 0..dim {
+            res *= unsafe { *self.diag.vget_unchecked(i) };
+        }
+
+        res * self.p.determinant()
+    }
+}
diff --git a/src/linalg/decomposition.rs b/src/linalg/decomposition.rs
index 67cc4c6a..7890748c 100644
--- a/src/linalg/decomposition.rs
+++ b/src/linalg/decomposition.rs
@@ -1,8 +1,8 @@
 use crate::storage::Storage;
 use crate::{
-    Allocator, Bidiagonal, Cholesky, ComplexField, DefaultAllocator, Dim, DimDiff, DimMin,
-    DimMinimum, DimSub, FullPivLU, Hessenberg, Matrix, Schur, SymmetricEigen, SymmetricTridiagonal,
-    LU, QR, SVD, U1,
+    Allocator, Bidiagonal, Cholesky, ColPivQR, ComplexField, DefaultAllocator, Dim, DimDiff,
+    DimMin, DimMinimum, DimSub, FullPivLU, Hessenberg, Matrix, Schur, SymmetricEigen,
+    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
diff --git a/src/linalg/mod.rs b/src/linalg/mod.rs
index c602bfa7..df9ae7de 100644
--- a/src/linalg/mod.rs
+++ b/src/linalg/mod.rs
@@ -8,6 +8,7 @@ mod determinant;
 // TODO: this should not be needed. However, the exp uses
 // explicit float operations on `f32` and `f64`. We need to
 // get rid of these to allow exp to be used on a no-std context.
+mod col_piv_qr;
 mod decomposition;
 #[cfg(feature = "std")]
 mod exp;
@@ -31,6 +32,7 @@ mod symmetric_tridiagonal;
 
 pub use self::bidiagonal::*;
 pub use self::cholesky::*;
+pub use self::col_piv_qr::*;
 pub use self::convolution::*;
 #[cfg(feature = "std")]
 pub use self::exp::*;
diff --git a/src/linalg/qr.rs b/src/linalg/qr.rs
index f404aa5a..fdf6b70a 100644
--- a/src/linalg/qr.rs
+++ b/src/linalg/qr.rs
@@ -60,8 +60,8 @@ where
             return QR { qr: matrix, diag };
         }
 
-        for ite in 0..min_nrows_ncols.value() {
-            householder::clear_column_unchecked(&mut matrix, &mut diag[ite], ite, 0, None);
+        for i in 0..min_nrows_ncols.value() {
+            householder::clear_column_unchecked(&mut matrix, &mut diag[i], i, 0, None);
         }
 
         QR { qr: matrix, diag }
diff --git a/tests/linalg/col_piv_qr.rs b/tests/linalg/col_piv_qr.rs
new file mode 100644
index 00000000..e9ffba86
--- /dev/null
+++ b/tests/linalg/col_piv_qr.rs
@@ -0,0 +1,161 @@
+#[cfg_attr(rustfmt, rustfmt_skip)]
+
+use na::Matrix4;
+
+#[test]
+fn col_piv_qr() {
+    let m = Matrix4::new(
+        1.0, -1.0, 2.0, 1.0, -1.0, 3.0, -1.0, -1.0, 3.0, -5.0, 5.0, 3.0, 1.0, 2.0, 1.0, -2.0,
+    );
+    let col_piv_qr = m.col_piv_qr();
+    assert!(relative_eq!(
+        col_piv_qr.determinant(),
+        0.0,
+        epsilon = 1.0e-7
+    ));
+
+    let (q, r, p) = col_piv_qr.unpack();
+
+    let mut qr = q * r;
+    p.inv_permute_columns(&mut qr);
+
+    assert!(relative_eq!(m, qr, epsilon = 1.0e-7));
+}
+
+#[cfg(feature = "arbitrary")]
+mod quickcheck_tests {
+    macro_rules! gen_tests(
+        ($module: ident, $scalar: ty) => {
+            mod $module {
+                use na::{DMatrix, DVector, Matrix3x5, Matrix4, Matrix4x3, Matrix5x3, Vector4};
+                use std::cmp;
+                #[allow(unused_imports)]
+                use crate::core::helper::{RandScalar, RandComplex};
+
+                quickcheck! {
+                    fn col_piv_qr(m: DMatrix<$scalar>) -> bool {
+                        let m = m.map(|e| e.0);
+                        let col_piv_qr = m.clone().col_piv_qr();
+                        let (q, r, p) = col_piv_qr.unpack();
+                        let mut qr = &q * &r;
+                        p.inv_permute_columns(&mut qr);
+
+                        println!("m: {}", m);
+                        println!("col_piv_qr: {}", &q * &r);
+
+                        relative_eq!(m, &qr, epsilon = 1.0e-7) &&
+                        q.is_orthogonal(1.0e-7)
+                    }
+
+                    fn col_piv_qr_static_5_3(m: Matrix5x3<$scalar>) -> bool {
+                        let m = m.map(|e| e.0);
+                        let col_piv_qr = m.col_piv_qr();
+                        let (q, r, p) = col_piv_qr.unpack();
+                        let mut qr = q * r;
+                        p.inv_permute_columns(&mut qr);
+
+                        relative_eq!(m, qr, epsilon = 1.0e-7) &&
+                        q.is_orthogonal(1.0e-7)
+                    }
+
+                    fn col_piv_qr_static_3_5(m: Matrix3x5<$scalar>) -> bool {
+                        let m = m.map(|e| e.0);
+                        let col_piv_qr = m.col_piv_qr();
+                        let (q, r, p) = col_piv_qr.unpack();
+                        let mut qr = q * r;
+                        p.inv_permute_columns(&mut qr);
+
+                        relative_eq!(m, qr, epsilon = 1.0e-7) &&
+                        q.is_orthogonal(1.0e-7)
+                    }
+
+                    fn col_piv_qr_static_square(m: Matrix4<$scalar>) -> bool {
+                        let m = m.map(|e| e.0);
+                        let col_piv_qr = m.col_piv_qr();
+                        let (q, r, p) = col_piv_qr.unpack();
+                        let mut qr = q * r;
+                        p.inv_permute_columns(&mut qr);
+
+                        println!("{}{}{}{}", q, r, qr, m);
+
+                        relative_eq!(m, qr, epsilon = 1.0e-7) &&
+                        q.is_orthogonal(1.0e-7)
+                    }
+
+                    fn col_piv_qr_solve(n: usize, nb: usize) -> bool {
+                        if n != 0 && nb != 0 {
+                            let n  = cmp::min(n, 50);  // To avoid slowing down the test too much.
+                            let nb = cmp::min(nb, 50); // To avoid slowing down the test too much.
+                            let m  = DMatrix::<$scalar>::new_random(n, n).map(|e| e.0);
+
+                            let col_piv_qr = m.clone().col_piv_qr();
+                            let b1 = DVector::<$scalar>::new_random(n).map(|e| e.0);
+                            let b2 = DMatrix::<$scalar>::new_random(n, nb).map(|e| e.0);
+
+                            if col_piv_qr.is_invertible() {
+                                let sol1 = col_piv_qr.solve(&b1).unwrap();
+                                let sol2 = col_piv_qr.solve(&b2).unwrap();
+
+                                return relative_eq!(&m * sol1, b1, epsilon = 1.0e-6) &&
+                                    relative_eq!(&m * sol2, b2, epsilon = 1.0e-6)
+                            }
+                        }
+
+                        return true;
+                    }
+
+                    fn col_piv_qr_solve_static(m: Matrix4<$scalar>) -> bool {
+                         let m = m.map(|e| e.0);
+                         let col_piv_qr = m.col_piv_qr();
+                         let b1 = Vector4::<$scalar>::new_random().map(|e| e.0);
+                         let b2 = Matrix4x3::<$scalar>::new_random().map(|e| e.0);
+
+                         if col_piv_qr.is_invertible() {
+                             let sol1 = col_piv_qr.solve(&b1).unwrap();
+                             let sol2 = col_piv_qr.solve(&b2).unwrap();
+
+                             relative_eq!(m * sol1, b1, epsilon = 1.0e-6) &&
+                             relative_eq!(m * sol2, b2, epsilon = 1.0e-6)
+                         }
+                         else {
+                             false
+                         }
+                    }
+
+                    fn col_piv_qr_inverse(n: usize) -> bool {
+                        let n = cmp::max(1, cmp::min(n, 15)); // To avoid slowing down the test too much.
+                        let m = DMatrix::<$scalar>::new_random(n, n).map(|e| e.0);
+
+                        if let Some(m1) = m.clone().col_piv_qr().try_inverse() {
+                            let id1 = &m  * &m1;
+                            let id2 = &m1 * &m;
+
+                            id1.is_identity(1.0e-5) && id2.is_identity(1.0e-5)
+                        }
+                        else {
+                            true
+                        }
+                    }
+
+                    fn col_piv_qr_inverse_static(m: Matrix4<$scalar>) -> bool {
+                        let m  = m.map(|e| e.0);
+                        let col_piv_qr = m.col_piv_qr();
+
+                        if let Some(m1) = col_piv_qr.try_inverse() {
+                            let id1 = &m  * &m1;
+                            let id2 = &m1 * &m;
+
+                            id1.is_identity(1.0e-5) && id2.is_identity(1.0e-5)
+                        }
+                        else {
+                            true
+                        }
+                    }
+                }
+            }
+        }
+    );
+
+    gen_tests!(complex, RandComplex<f64>);
+    gen_tests!(f64, RandScalar<f64>);
+}
diff --git a/tests/linalg/mod.rs b/tests/linalg/mod.rs
index 7fc01396..66a0c06a 100644
--- a/tests/linalg/mod.rs
+++ b/tests/linalg/mod.rs
@@ -1,6 +1,7 @@
 mod balancing;
 mod bidiagonal;
 mod cholesky;
+mod col_piv_qr;
 mod convolution;
 mod eigen;
 mod exp;