diff --git a/nalgebra-lapack/src/lu.rs b/nalgebra-lapack/src/lu.rs
index ad76e245..dc21c12b 100644
--- a/nalgebra-lapack/src/lu.rs
+++ b/nalgebra-lapack/src/lu.rs
@@ -214,7 +214,7 @@ where
         }
     }
 
-    /// Solves the linear system `self.conjugate_transpose() * x = b`, where `x` is the unknown to
+    /// Solves the linear system `self.adjoint() * x = b`, where `x` is the unknown to
     /// be determined.
     pub fn solve_conjugate_transpose<R2: Dim, C2: Dim, S2>(
         &self,
@@ -249,7 +249,7 @@ where
         self.generic_solve_mut(b'T', b)
     }
 
-    /// Solves in-place the linear system `self.conjugate_transpose() * x = b`, where `x` is the unknown to
+    /// Solves in-place the linear system `self.adjoint() * x = b`, where `x` is the unknown to
     /// be determined.
     ///
     /// Returns `false` if no solution was found (the decomposed matrix is singular).
diff --git a/src/base/blas.rs b/src/base/blas.rs
index 7dce36b6..210db8cd 100644
--- a/src/base/blas.rs
+++ b/src/base/blas.rs
@@ -11,7 +11,7 @@ use base::constraint::{
 };
 use base::dimension::{Dim, Dynamic, U1, U2, U3, U4};
 use base::storage::{Storage, StorageMut};
-use base::{DefaultAllocator, Matrix, Scalar, SquareMatrix, Vector};
+use base::{DefaultAllocator, Matrix, Scalar, SquareMatrix, Vector, DVectorSlice};
 
 
 // FIXME: find a way to avoid code duplication just for complex number support.
@@ -32,11 +32,11 @@ impl<N: Complex, D: Dim, S: Storage<N, D>> Vector<N, D, S> {
     pub fn icamax(&self) -> usize {
         assert!(!self.is_empty(), "The input vector must not be empty.");
 
-        let mut the_max = unsafe { self.vget_unchecked(0).asum() };
+        let mut the_max = unsafe { self.vget_unchecked(0).norm1() };
         let mut the_i = 0;
 
         for i in 1..self.nrows() {
-            let val = unsafe { self.vget_unchecked(i).asum() };
+            let val = unsafe { self.vget_unchecked(i).norm1() };
 
             if val > the_max {
                 the_max = val;
@@ -211,12 +211,12 @@ impl<N: Complex, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
     pub fn icamax_full(&self) -> (usize, usize) {
         assert!(!self.is_empty(), "The input matrix must not be empty.");
 
-        let mut the_max = unsafe { self.get_unchecked((0, 0)).asum() };
+        let mut the_max = unsafe { self.get_unchecked((0, 0)).norm1() };
         let mut the_ij = (0, 0);
 
         for j in 0..self.ncols() {
             for i in 0..self.nrows() {
-                let val = unsafe { self.get_unchecked((i, j)).asum() };
+                let val = unsafe { self.get_unchecked((i, j)).norm1() };
 
                 if val > the_max {
                     the_max = val;
@@ -263,13 +263,11 @@ impl<N: Scalar + PartialOrd + Signed, R: Dim, C: Dim, S: Storage<N, R, C>> Matri
     }
 }
 
-impl<N: Complex, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
-    /// The dot product between two complex or real vectors or matrices (seen as vectors).
-    ///
-    /// This is the same as `.dot` except that the conjugate of each component of `self` is taken
-    /// before performing the products.
-    #[inline]
-    pub fn cdot<R2: Dim, C2: Dim, SB>(&self, rhs: &Matrix<N, R2, C2, SB>) -> N
+impl<N, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S>
+where N: Scalar + Zero + ClosedAdd + ClosedMul
+{
+    #[inline(always)]
+    fn dotx<R2: Dim, C2: Dim, SB>(&self, rhs: &Matrix<N, R2, C2, SB>, conjugate: impl Fn(N) -> N) -> N
         where
             SB: Storage<N, R2, C2>,
             ShapeConstraint: DimEq<R, R2> + DimEq<C, C2>,
@@ -283,27 +281,27 @@ impl<N: Complex, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         // because the `for` loop below won't be very efficient on those.
         if (R::is::<U2>() || R2::is::<U2>()) && (C::is::<U1>() || C2::is::<U1>()) {
             unsafe {
-                let a = self.get_unchecked((0, 0)).conjugate() * *rhs.get_unchecked((0, 0));
-                let b = self.get_unchecked((1, 0)).conjugate() * *rhs.get_unchecked((1, 0));
+                let a = conjugate(*self.get_unchecked((0, 0))) * *rhs.get_unchecked((0, 0));
+                let b = conjugate(*self.get_unchecked((1, 0))) * *rhs.get_unchecked((1, 0));
 
                 return a + b;
             }
         }
         if (R::is::<U3>() || R2::is::<U3>()) && (C::is::<U1>() || C2::is::<U1>()) {
             unsafe {
-                let a = self.get_unchecked((0, 0)).conjugate() * *rhs.get_unchecked((0, 0));
-                let b = self.get_unchecked((1, 0)).conjugate() * *rhs.get_unchecked((1, 0));
-                let c = self.get_unchecked((2, 0)).conjugate() * *rhs.get_unchecked((2, 0));
+                let a = conjugate(*self.get_unchecked((0, 0))) * *rhs.get_unchecked((0, 0));
+                let b = conjugate(*self.get_unchecked((1, 0))) * *rhs.get_unchecked((1, 0));
+                let c = conjugate(*self.get_unchecked((2, 0))) * *rhs.get_unchecked((2, 0));
 
                 return a + b + c;
             }
         }
         if (R::is::<U4>() || R2::is::<U4>()) && (C::is::<U1>() || C2::is::<U1>()) {
             unsafe {
-                let mut a = self.get_unchecked((0, 0)).conjugate() * *rhs.get_unchecked((0, 0));
-                let mut b = self.get_unchecked((1, 0)).conjugate() * *rhs.get_unchecked((1, 0));
-                let c = self.get_unchecked((2, 0)).conjugate() * *rhs.get_unchecked((2, 0));
-                let d = self.get_unchecked((3, 0)).conjugate() * *rhs.get_unchecked((3, 0));
+                let mut a = conjugate(*self.get_unchecked((0, 0))) * *rhs.get_unchecked((0, 0));
+                let mut b = conjugate(*self.get_unchecked((1, 0))) * *rhs.get_unchecked((1, 0));
+                let c = conjugate(*self.get_unchecked((2, 0))) * *rhs.get_unchecked((2, 0));
+                let d = conjugate(*self.get_unchecked((3, 0))) * *rhs.get_unchecked((3, 0));
 
                 a += c;
                 b += d;
@@ -343,14 +341,14 @@ impl<N: Complex, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
             acc7 = N::zero();
 
             while self.nrows() - i >= 8 {
-                acc0 += unsafe { self.get_unchecked((i + 0, j)).conjugate() * *rhs.get_unchecked((i + 0, j)) };
-                acc1 += unsafe { self.get_unchecked((i + 1, j)).conjugate() * *rhs.get_unchecked((i + 1, j)) };
-                acc2 += unsafe { self.get_unchecked((i + 2, j)).conjugate() * *rhs.get_unchecked((i + 2, j)) };
-                acc3 += unsafe { self.get_unchecked((i + 3, j)).conjugate() * *rhs.get_unchecked((i + 3, j)) };
-                acc4 += unsafe { self.get_unchecked((i + 4, j)).conjugate() * *rhs.get_unchecked((i + 4, j)) };
-                acc5 += unsafe { self.get_unchecked((i + 5, j)).conjugate() * *rhs.get_unchecked((i + 5, j)) };
-                acc6 += unsafe { self.get_unchecked((i + 6, j)).conjugate() * *rhs.get_unchecked((i + 6, j)) };
-                acc7 += unsafe { self.get_unchecked((i + 7, j)).conjugate() * *rhs.get_unchecked((i + 7, j)) };
+                acc0 += unsafe { conjugate(*self.get_unchecked((i + 0, j))) * *rhs.get_unchecked((i + 0, j)) };
+                acc1 += unsafe { conjugate(*self.get_unchecked((i + 1, j))) * *rhs.get_unchecked((i + 1, j)) };
+                acc2 += unsafe { conjugate(*self.get_unchecked((i + 2, j))) * *rhs.get_unchecked((i + 2, j)) };
+                acc3 += unsafe { conjugate(*self.get_unchecked((i + 3, j))) * *rhs.get_unchecked((i + 3, j)) };
+                acc4 += unsafe { conjugate(*self.get_unchecked((i + 4, j))) * *rhs.get_unchecked((i + 4, j)) };
+                acc5 += unsafe { conjugate(*self.get_unchecked((i + 5, j))) * *rhs.get_unchecked((i + 5, j)) };
+                acc6 += unsafe { conjugate(*self.get_unchecked((i + 6, j))) * *rhs.get_unchecked((i + 6, j)) };
+                acc7 += unsafe { conjugate(*self.get_unchecked((i + 7, j))) * *rhs.get_unchecked((i + 7, j)) };
                 i += 8;
             }
 
@@ -360,17 +358,14 @@ impl<N: Complex, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
             res += acc3 + acc7;
 
             for k in i..self.nrows() {
-                res += unsafe { self.get_unchecked((k, j)).conjugate() * *rhs.get_unchecked((k, j)) }
+                res += unsafe { conjugate(*self.get_unchecked((k, j))) * *rhs.get_unchecked((k, j)) }
             }
         }
 
         res
     }
-}
 
-impl<N, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S>
-where N: Scalar + Zero + ClosedAdd + ClosedMul
-{
+
     /// The dot product between two vectors or matrices (seen as vectors).
     ///
     /// Note that this is **not** the matrix multiplication as in, e.g., numpy. For matrix
@@ -396,97 +391,36 @@ where N: Scalar + Zero + ClosedAdd + ClosedMul
         SB: Storage<N, R2, C2>,
         ShapeConstraint: DimEq<R, R2> + DimEq<C, C2>,
     {
-        assert!(
-            self.nrows() == rhs.nrows(),
-            "Dot product dimensions mismatch."
-        );
+        self.dotx(rhs, |e| e)
+    }
 
-        // So we do some special cases for common fixed-size vectors of dimension lower than 8
-        // because the `for` loop below won't be very efficient on those.
-        if (R::is::<U2>() || R2::is::<U2>()) && (C::is::<U1>() || C2::is::<U1>()) {
-            unsafe {
-                let a = *self.get_unchecked((0, 0)) * *rhs.get_unchecked((0, 0));
-                let b = *self.get_unchecked((1, 0)) * *rhs.get_unchecked((1, 0));
-
-                return a + b;
-            }
-        }
-        if (R::is::<U3>() || R2::is::<U3>()) && (C::is::<U1>() || C2::is::<U1>()) {
-            unsafe {
-                let a = *self.get_unchecked((0, 0)) * *rhs.get_unchecked((0, 0));
-                let b = *self.get_unchecked((1, 0)) * *rhs.get_unchecked((1, 0));
-                let c = *self.get_unchecked((2, 0)) * *rhs.get_unchecked((2, 0));
-
-                return a + b + c;
-            }
-        }
-        if (R::is::<U4>() || R2::is::<U4>()) && (C::is::<U1>() || C2::is::<U1>()) {
-            unsafe {
-                let mut a = *self.get_unchecked((0, 0)) * *rhs.get_unchecked((0, 0));
-                let mut b = *self.get_unchecked((1, 0)) * *rhs.get_unchecked((1, 0));
-                let c = *self.get_unchecked((2, 0)) * *rhs.get_unchecked((2, 0));
-                let d = *self.get_unchecked((3, 0)) * *rhs.get_unchecked((3, 0));
-
-                a += c;
-                b += d;
-
-                return a + b;
-            }
-        }
-
-        // All this is inspired from the "unrolled version" discussed in:
-        // http://blog.theincredibleholk.org/blog/2012/12/10/optimizing-dot-product/
-        //
-        // And this comment from bluss:
-        // https://users.rust-lang.org/t/how-to-zip-two-slices-efficiently/2048/12
-        let mut res = N::zero();
-
-        // We have to define them outside of the loop (and not inside at first assignment)
-        // otherwise vectorization won't kick in for some reason.
-        let mut acc0;
-        let mut acc1;
-        let mut acc2;
-        let mut acc3;
-        let mut acc4;
-        let mut acc5;
-        let mut acc6;
-        let mut acc7;
-
-        for j in 0..self.ncols() {
-            let mut i = 0;
-
-            acc0 = N::zero();
-            acc1 = N::zero();
-            acc2 = N::zero();
-            acc3 = N::zero();
-            acc4 = N::zero();
-            acc5 = N::zero();
-            acc6 = N::zero();
-            acc7 = N::zero();
-
-            while self.nrows() - i >= 8 {
-                acc0 += unsafe { *self.get_unchecked((i + 0, j)) * *rhs.get_unchecked((i + 0, j)) };
-                acc1 += unsafe { *self.get_unchecked((i + 1, j)) * *rhs.get_unchecked((i + 1, j)) };
-                acc2 += unsafe { *self.get_unchecked((i + 2, j)) * *rhs.get_unchecked((i + 2, j)) };
-                acc3 += unsafe { *self.get_unchecked((i + 3, j)) * *rhs.get_unchecked((i + 3, j)) };
-                acc4 += unsafe { *self.get_unchecked((i + 4, j)) * *rhs.get_unchecked((i + 4, j)) };
-                acc5 += unsafe { *self.get_unchecked((i + 5, j)) * *rhs.get_unchecked((i + 5, j)) };
-                acc6 += unsafe { *self.get_unchecked((i + 6, j)) * *rhs.get_unchecked((i + 6, j)) };
-                acc7 += unsafe { *self.get_unchecked((i + 7, j)) * *rhs.get_unchecked((i + 7, j)) };
-                i += 8;
-            }
-
-            res += acc0 + acc4;
-            res += acc1 + acc5;
-            res += acc2 + acc6;
-            res += acc3 + acc7;
-
-            for k in i..self.nrows() {
-                res += unsafe { *self.get_unchecked((k, j)) * *rhs.get_unchecked((k, j)) }
-            }
-        }
-
-        res
+    /// The dot product between two vectors or matrices (seen as vectors).
+    ///
+    /// Note that this is **not** the matrix multiplication as in, e.g., numpy. For matrix
+    /// multiplication, use one of: `.gemm`, `.mul_to`, `.mul`, the `*` operator.
+    ///
+    /// # Examples:
+    ///
+    /// ```
+    /// # use nalgebra::{Vector3, Matrix2x3};
+    /// let vec1 = Vector3::new(1.0, 2.0, 3.0);
+    /// let vec2 = Vector3::new(0.1, 0.2, 0.3);
+    /// assert_eq!(vec1.dot(&vec2), 1.4);
+    ///
+    /// let mat1 = Matrix2x3::new(1.0, 2.0, 3.0,
+    ///                           4.0, 5.0, 6.0);
+    /// let mat2 = Matrix2x3::new(0.1, 0.2, 0.3,
+    ///                           0.4, 0.5, 0.6);
+    /// assert_eq!(mat1.dot(&mat2), 9.1);
+    /// ```
+    #[inline]
+    pub fn dotc<R2: Dim, C2: Dim, SB>(&self, rhs: &Matrix<N, R2, C2, SB>) -> N
+        where
+            N: Complex,
+            SB: Storage<N, R2, C2>,
+            ShapeConstraint: DimEq<R, R2> + DimEq<C, C2>,
+    {
+        self.dotx(rhs, Complex::conjugate)
     }
 
     /// The dot product between the transpose of `self` and `rhs`.
@@ -643,40 +577,15 @@ where
         }
     }
 
-    /// Computes `self = alpha * a * x + beta * self`, where `a` is a **symmetric** matrix, `x` a
-    /// vector, and `alpha, beta` two scalars.
-    ///
-    /// If `beta` is zero, `self` is never read. If `self` is read, only its lower-triangular part
-    /// (including the diagonal) is actually read.
-    ///
-    /// # Examples:
-    ///
-    /// ```
-    /// # use nalgebra::{Matrix2, Vector2};
-    /// let mat = Matrix2::new(1.0, 2.0,
-    ///                        2.0, 4.0);
-    /// let mut vec1 = Vector2::new(1.0, 2.0);
-    /// let vec2 = Vector2::new(0.1, 0.2);
-    /// vec1.gemv_symm(10.0, &mat, &vec2, 5.0);
-    /// assert_eq!(vec1, Vector2::new(10.0, 20.0));
-    ///
-    ///
-    /// // The matrix upper-triangular elements can be garbage because it is never
-    /// // read by this method. Therefore, it is not necessary for the caller to
-    /// // fill the matrix struct upper-triangle.
-    /// let mat = Matrix2::new(1.0, 9999999.9999999,
-    ///                        2.0, 4.0);
-    /// let mut vec1 = Vector2::new(1.0, 2.0);
-    /// vec1.gemv_symm(10.0, &mat, &vec2, 5.0);
-    /// assert_eq!(vec1, Vector2::new(10.0, 20.0));
-    /// ```
-    #[inline]
-    pub fn gemv_symm<D2: Dim, D3: Dim, SB, SC>(
+
+    #[inline(always)]
+    fn xgemv<D2: Dim, D3: Dim, SB, SC>(
         &mut self,
         alpha: N,
         a: &SquareMatrix<N, D2, SB>,
         x: &Vector<N, D3, SC>,
         beta: N,
+        dotc: impl Fn(&DVectorSlice<N, SB::RStride, SB::CStride>, &DVectorSlice<N, SC::RStride, SC::CStride>) -> N,
     ) where
         N: One,
         SB: Storage<N, D2, D2>,
@@ -687,83 +596,6 @@ where
         let dim2 = a.nrows();
         let dim3 = x.nrows();
 
-        assert!(
-            a.is_square(),
-            "Symmetric gemv: the input matrix must be square."
-        );
-        assert!(
-            dim2 == dim3 && dim1 == dim2,
-            "Symmetric gemv: dimensions mismatch."
-        );
-
-        if dim2 == 0 {
-            return;
-        }
-
-        // FIXME: avoid bound checks.
-        let col2 = a.column(0);
-        let val = unsafe { *x.vget_unchecked(0) };
-        self.axpy(alpha * val, &col2, beta);
-        self[0] += alpha * x.rows_range(1..).dot(&a.slice_range(1.., 0));
-
-        for j in 1..dim2 {
-            let col2 = a.column(j);
-            let dot = x.rows_range(j..).dot(&col2.rows_range(j..));
-
-            let val;
-            unsafe {
-                val = *x.vget_unchecked(j);
-                *self.vget_unchecked_mut(j) += alpha * dot;
-            }
-            self.rows_range_mut(j + 1..)
-                .axpy(alpha * val, &col2.rows_range(j + 1..), N::one());
-        }
-    }
-
-    /// Computes `self = alpha * a * x + beta * self`, where `a` is a **symmetric** matrix, `x` a
-    /// vector, and `alpha, beta` two scalars.
-    ///
-    /// If `beta` is zero, `self` is never read. If `self` is read, only its lower-triangular part
-    /// (including the diagonal) is actually read.
-    ///
-    /// # Examples:
-    ///
-    /// ```
-    /// # use nalgebra::{Matrix2, Vector2};
-    /// let mat = Matrix2::new(1.0, 2.0,
-    ///                        2.0, 4.0);
-    /// let mut vec1 = Vector2::new(1.0, 2.0);
-    /// let vec2 = Vector2::new(0.1, 0.2);
-    /// vec1.gemv_symm(10.0, &mat, &vec2, 5.0);
-    /// assert_eq!(vec1, Vector2::new(10.0, 20.0));
-    ///
-    ///
-    /// // The matrix upper-triangular elements can be garbage because it is never
-    /// // read by this method. Therefore, it is not necessary for the caller to
-    /// // fill the matrix struct upper-triangle.
-    /// let mat = Matrix2::new(1.0, 9999999.9999999,
-    ///                        2.0, 4.0);
-    /// let mut vec1 = Vector2::new(1.0, 2.0);
-    /// vec1.gemv_symm(10.0, &mat, &vec2, 5.0);
-    /// assert_eq!(vec1, Vector2::new(10.0, 20.0));
-    /// ```
-    #[inline]
-    pub fn cgemv_symm<D2: Dim, D3: Dim, SB, SC>(
-        &mut self,
-        alpha: N,
-        a: &SquareMatrix<N, D2, SB>,
-        x: &Vector<N, D3, SC>,
-        beta: N,
-    ) where
-        N: Complex,
-        SB: Storage<N, D2, D2>,
-        SC: Storage<N, D3>,
-        ShapeConstraint: DimEq<D, D2> + AreMultipliable<D2, D2, D3, U1>,
-    {
-        let dim1 = self.nrows();
-        let dim2 = a.nrows();
-        let dim3 = x.nrows();
-
         assert!(
             a.is_square(),
             "Symmetric cgemv: the input matrix must be square."
@@ -781,11 +613,11 @@ where
         let col2 = a.column(0);
         let val = unsafe { *x.vget_unchecked(0) };
         self.axpy(alpha * val, &col2, beta);
-        self[0] += alpha * a.slice_range(1.., 0).cdot(&x.rows_range(1..));
+        self[0] += alpha * dotc(&a.slice_range(1.., 0), &x.rows_range(1..));
 
         for j in 1..dim2 {
             let col2 = a.column(j);
-            let dot = col2.rows_range(j..).cdot(&x.rows_range(j..));
+            let dot = dotc(&col2.rows_range(j..), &x.rows_range(j..));
 
             let val;
             unsafe {
@@ -797,6 +629,111 @@ where
         }
     }
 
+    /// Computes `self = alpha * a * x + beta * self`, where `a` is a **symmetric** matrix, `x` a
+    /// vector, and `alpha, beta` two scalars. DEPRECATED: use `sygemv` instead.
+    #[inline]
+    #[deprecated(note = "This is renamed `sygemv` to match the original BLAS terminology.")]
+    pub fn gemv_symm<D2: Dim, D3: Dim, SB, SC>(
+        &mut self,
+        alpha: N,
+        a: &SquareMatrix<N, D2, SB>,
+        x: &Vector<N, D3, SC>,
+        beta: N,
+    ) where
+        N: One,
+        SB: Storage<N, D2, D2>,
+        SC: Storage<N, D3>,
+        ShapeConstraint: DimEq<D, D2> + AreMultipliable<D2, D2, D3, U1>,
+    {
+        self.sygemv(alpha, a, x, beta)
+    }
+
+    /// Computes `self = alpha * a * x + beta * self`, where `a` is a **symmetric** matrix, `x` a
+    /// vector, and `alpha, beta` two scalars.
+    ///
+    /// If `beta` is zero, `self` is never read. If `self` is read, only its lower-triangular part
+    /// (including the diagonal) is actually read.
+    ///
+    /// # Examples:
+    ///
+    /// ```
+    /// # use nalgebra::{Matrix2, Vector2};
+    /// let mat = Matrix2::new(1.0, 2.0,
+    ///                        2.0, 4.0);
+    /// let mut vec1 = Vector2::new(1.0, 2.0);
+    /// let vec2 = Vector2::new(0.1, 0.2);
+    /// vec1.sygemv(10.0, &mat, &vec2, 5.0);
+    /// assert_eq!(vec1, Vector2::new(10.0, 20.0));
+    ///
+    ///
+    /// // The matrix upper-triangular elements can be garbage because it is never
+    /// // read by this method. Therefore, it is not necessary for the caller to
+    /// // fill the matrix struct upper-triangle.
+    /// let mat = Matrix2::new(1.0, 9999999.9999999,
+    ///                        2.0, 4.0);
+    /// let mut vec1 = Vector2::new(1.0, 2.0);
+    /// vec1.sygemv(10.0, &mat, &vec2, 5.0);
+    /// assert_eq!(vec1, Vector2::new(10.0, 20.0));
+    /// ```
+    #[inline]
+    pub fn sygemv<D2: Dim, D3: Dim, SB, SC>(
+        &mut self,
+        alpha: N,
+        a: &SquareMatrix<N, D2, SB>,
+        x: &Vector<N, D3, SC>,
+        beta: N,
+    ) where
+        N: One,
+        SB: Storage<N, D2, D2>,
+        SC: Storage<N, D3>,
+        ShapeConstraint: DimEq<D, D2> + AreMultipliable<D2, D2, D3, U1>,
+    {
+        self.xgemv(alpha, a, x, beta, |a, b| a.dot(b))
+    }
+
+    /// Computes `self = alpha * a * x + beta * self`, where `a` is an **hermitian** matrix, `x` a
+    /// vector, and `alpha, beta` two scalars.
+    ///
+    /// If `beta` is zero, `self` is never read. If `self` is read, only its lower-triangular part
+    /// (including the diagonal) is actually read.
+    ///
+    /// # Examples:
+    ///
+    /// ```
+    /// # use nalgebra::{Matrix2, Vector2};
+    /// let mat = Matrix2::new(1.0, 2.0,
+    ///                        2.0, 4.0);
+    /// let mut vec1 = Vector2::new(1.0, 2.0);
+    /// let vec2 = Vector2::new(0.1, 0.2);
+    /// vec1.sygemv(10.0, &mat, &vec2, 5.0);
+    /// assert_eq!(vec1, Vector2::new(10.0, 20.0));
+    ///
+    ///
+    /// // The matrix upper-triangular elements can be garbage because it is never
+    /// // read by this method. Therefore, it is not necessary for the caller to
+    /// // fill the matrix struct upper-triangle.
+    /// let mat = Matrix2::new(1.0, 9999999.9999999,
+    ///                        2.0, 4.0);
+    /// let mut vec1 = Vector2::new(1.0, 2.0);
+    /// vec1.sygemv(10.0, &mat, &vec2, 5.0);
+    /// assert_eq!(vec1, Vector2::new(10.0, 20.0));
+    /// ```
+    #[inline]
+    pub fn hegemv<D2: Dim, D3: Dim, SB, SC>(
+        &mut self,
+        alpha: N,
+        a: &SquareMatrix<N, D2, SB>,
+        x: &Vector<N, D3, SC>,
+        beta: N,
+    ) where
+        N: Complex,
+        SB: Storage<N, D2, D2>,
+        SC: Storage<N, D3>,
+        ShapeConstraint: DimEq<D, D2> + AreMultipliable<D2, D2, D3, U1>,
+    {
+        self.xgemv(alpha, a, x, beta, |a, b| a.dotc(b))
+    }
+
     /// Computes `self = alpha * a.transpose() * x + beta * self`, where `a` is a matrix, `x` a vector, and
     /// `alpha, beta` two scalars.
     ///
@@ -855,33 +792,17 @@ where
     }
 }
 
-// FIXME: duplicate code
 impl<N, R1: Dim, C1: Dim, S: StorageMut<N, R1, C1>> Matrix<N, R1, C1, S>
-    where N: Complex + Zero + ClosedAdd + ClosedMul
+where N: Scalar + Zero + ClosedAdd + ClosedMul
 {
-    /// Computes `self = alpha * x * y.transpose() + beta * self`.
-    ///
-    /// If `beta` is zero, `self` is never read.
-    ///
-    /// # Examples:
-    ///
-    /// ```
-    /// # use nalgebra::{Matrix2x3, Vector2, Vector3};
-    /// let mut mat = Matrix2x3::repeat(4.0);
-    /// let vec1 = Vector2::new(1.0, 2.0);
-    /// let vec2 = Vector3::new(0.1, 0.2, 0.3);
-    /// let expected = vec1 * vec2.transpose() * 10.0 + mat * 5.0;
-    ///
-    /// mat.ger(10.0, &vec1, &vec2, 5.0);
-    /// assert_eq!(mat, expected);
-    /// ```
-    #[inline]
-    pub fn gerc<D2: Dim, D3: Dim, SB, SC>(
+    #[inline(always)]
+    fn gerx<D2: Dim, D3: Dim, SB, SC>(
         &mut self,
         alpha: N,
         x: &Vector<N, D2, SB>,
         y: &Vector<N, D3, SC>,
         beta: N,
+        conjugate: impl Fn(N) -> N,
     ) where
         N: One,
         SB: Storage<N, D2>,
@@ -899,15 +820,11 @@ impl<N, R1: Dim, C1: Dim, S: StorageMut<N, R1, C1>> Matrix<N, R1, C1, S>
 
         for j in 0..ncols1 {
             // FIXME: avoid bound checks.
-            let val = unsafe { y.vget_unchecked(j).conjugate() };
+            let val = unsafe { conjugate(*y.vget_unchecked(j)) };
             self.column_mut(j).axpy(alpha * val, x, beta);
         }
     }
-}
 
-impl<N, R1: Dim, C1: Dim, S: StorageMut<N, R1, C1>> Matrix<N, R1, C1, S>
-where N: Scalar + Zero + ClosedAdd + ClosedMul
-{
     /// Computes `self = alpha * x * y.transpose() + beta * self`.
     ///
     /// If `beta` is zero, `self` is never read.
@@ -937,20 +854,39 @@ where N: Scalar + Zero + ClosedAdd + ClosedMul
         SC: Storage<N, D3>,
         ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,
     {
-        let (nrows1, ncols1) = self.shape();
-        let dim2 = x.nrows();
-        let dim3 = y.nrows();
+        self.gerx(alpha, x, y, beta, |e| e)
+    }
 
-        assert!(
-            nrows1 == dim2 && ncols1 == dim3,
-            "ger: dimensions mismatch."
-        );
-
-        for j in 0..ncols1 {
-            // FIXME: avoid bound checks.
-            let val = unsafe { *y.vget_unchecked(j) };
-            self.column_mut(j).axpy(alpha * val, x, beta);
-        }
+    /// Computes `self = alpha * x * y.transpose() + beta * self`.
+    ///
+    /// If `beta` is zero, `self` is never read.
+    ///
+    /// # Examples:
+    ///
+    /// ```
+    /// # use nalgebra::{Matrix2x3, Vector2, Vector3};
+    /// let mut mat = Matrix2x3::repeat(4.0);
+    /// let vec1 = Vector2::new(1.0, 2.0);
+    /// let vec2 = Vector3::new(0.1, 0.2, 0.3);
+    /// let expected = vec1 * vec2.transpose() * 10.0 + mat * 5.0;
+    ///
+    /// mat.ger(10.0, &vec1, &vec2, 5.0);
+    /// assert_eq!(mat, expected);
+    /// ```
+    #[inline]
+    pub fn gerc<D2: Dim, D3: Dim, SB, SC>(
+        &mut self,
+        alpha: N,
+        x: &Vector<N, D2, SB>,
+        y: &Vector<N, D3, SC>,
+        beta: N,
+    ) where
+        N: Complex,
+        SB: Storage<N, D2>,
+        SC: Storage<N, D3>,
+        ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,
+    {
+        self.gerx(alpha, x, y, beta, Complex::conjugate)
     }
 
     /// Computes `self = alpha * a * b + beta * self`, where `a, b, self` are matrices.
@@ -1146,6 +1082,42 @@ where N: Scalar + Zero + ClosedAdd + ClosedMul
 impl<N, R1: Dim, C1: Dim, S: StorageMut<N, R1, C1>> Matrix<N, R1, C1, S>
 where N: Scalar + Zero + ClosedAdd + ClosedMul
 {
+    #[inline(always)]
+    fn sygerx<D2: Dim, D3: Dim, SB, SC>(
+        &mut self,
+        alpha: N,
+        x: &Vector<N, D2, SB>,
+        y: &Vector<N, D3, SC>,
+        beta: N,
+        conjugate: impl Fn(N) -> N,
+    ) where
+        N: One,
+        SB: Storage<N, D2>,
+        SC: Storage<N, D3>,
+        ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,
+    {
+        let dim1 = self.nrows();
+        let dim2 = x.nrows();
+        let dim3 = y.nrows();
+
+        assert!(
+            self.is_square(),
+            "Symmetric ger: the input matrix must be square."
+        );
+        assert!(dim1 == dim2 && dim1 == dim3, "ger: dimensions mismatch.");
+
+        for j in 0..dim1 {
+            let val = unsafe { conjugate(*y.vget_unchecked(j)) };
+            let subdim = Dynamic::new(dim1 - j);
+            // FIXME: avoid bound checks.
+            self.generic_slice_mut((j, j), (subdim, U1)).axpy(
+                alpha * val,
+                &x.rows_range(j..),
+                beta,
+            );
+        }
+    }
+
     /// Computes `self = alpha * x * y.transpose() + beta * self`, where `self` is a **symmetric**
     /// matrix.
     ///
@@ -1166,6 +1138,7 @@ where N: Scalar + Zero + ClosedAdd + ClosedMul
     /// assert_eq!(mat.lower_triangle(), expected.lower_triangle());
     /// assert_eq!(mat.m12, 99999.99999); // This was untouched.
     #[inline]
+    #[deprecated(note = "This is renamed `syger` to match the original BLAS terminology.")]
     pub fn ger_symm<D2: Dim, D3: Dim, SB, SC>(
         &mut self,
         alpha: N,
@@ -1178,26 +1151,77 @@ where N: Scalar + Zero + ClosedAdd + ClosedMul
         SC: Storage<N, D3>,
         ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,
     {
-        let dim1 = self.nrows();
-        let dim2 = x.nrows();
-        let dim3 = y.nrows();
+        self.syger(alpha, x, y, beta)
+    }
 
-        assert!(
-            self.is_square(),
-            "Symmetric ger: the input matrix must be square."
-        );
-        assert!(dim1 == dim2 && dim1 == dim3, "ger: dimensions mismatch.");
+    /// Computes `self = alpha * x * y.transpose() + beta * self`, where `self` is a **symmetric**
+    /// matrix.
+    ///
+    /// If `beta` is zero, `self` is never read. The result is symmetric. Only the lower-triangular
+    /// (including the diagonal) part of `self` is read/written.
+    ///
+    /// # Examples:
+    ///
+    /// ```
+    /// # use nalgebra::{Matrix2, Vector2};
+    /// let mut mat = Matrix2::identity();
+    /// let vec1 = Vector2::new(1.0, 2.0);
+    /// let vec2 = Vector2::new(0.1, 0.2);
+    /// let expected = vec1 * vec2.transpose() * 10.0 + mat * 5.0;
+    /// mat.m12 = 99999.99999; // This component is on the upper-triangular part and will not be read/written.
+    ///
+    /// mat.ger_symm(10.0, &vec1, &vec2, 5.0);
+    /// assert_eq!(mat.lower_triangle(), expected.lower_triangle());
+    /// assert_eq!(mat.m12, 99999.99999); // This was untouched.
+    #[inline]
+    pub fn syger<D2: Dim, D3: Dim, SB, SC>(
+        &mut self,
+        alpha: N,
+        x: &Vector<N, D2, SB>,
+        y: &Vector<N, D3, SC>,
+        beta: N,
+    ) where
+        N: One,
+        SB: Storage<N, D2>,
+        SC: Storage<N, D3>,
+        ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,
+    {
+        self.sygerx(alpha, x, y, beta, |e| e)
+    }
 
-        for j in 0..dim1 {
-            let val = unsafe { *y.vget_unchecked(j) };
-            let subdim = Dynamic::new(dim1 - j);
-            // FIXME: avoid bound checks.
-            self.generic_slice_mut((j, j), (subdim, U1)).axpy(
-                alpha * val,
-                &x.rows_range(j..),
-                beta,
-            );
-        }
+    /// Computes `self = alpha * x * y.transpose() + beta * self`, where `self` is a **symmetric**
+    /// matrix.
+    ///
+    /// If `beta` is zero, `self` is never read. The result is symmetric. Only the lower-triangular
+    /// (including the diagonal) part of `self` is read/written.
+    ///
+    /// # Examples:
+    ///
+    /// ```
+    /// # use nalgebra::{Matrix2, Vector2};
+    /// let mut mat = Matrix2::identity();
+    /// let vec1 = Vector2::new(1.0, 2.0);
+    /// let vec2 = Vector2::new(0.1, 0.2);
+    /// let expected = vec1 * vec2.transpose() * 10.0 + mat * 5.0;
+    /// mat.m12 = 99999.99999; // This component is on the upper-triangular part and will not be read/written.
+    ///
+    /// mat.ger_symm(10.0, &vec1, &vec2, 5.0);
+    /// assert_eq!(mat.lower_triangle(), expected.lower_triangle());
+    /// assert_eq!(mat.m12, 99999.99999); // This was untouched.
+    #[inline]
+    pub fn hegerc<D2: Dim, D3: Dim, SB, SC>(
+        &mut self,
+        alpha: N,
+        x: &Vector<N, D2, SB>,
+        y: &Vector<N, D3, SC>,
+        beta: N,
+    ) where
+        N: Complex,
+        SB: Storage<N, D2>,
+        SC: Storage<N, D3>,
+        ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,
+    {
+        self.sygerx(alpha, x, y, beta, Complex::conjugate)
     }
 }
 
diff --git a/src/base/matrix.rs b/src/base/matrix.rs
index 8f6c071a..6a89e945 100644
--- a/src/base/matrix.rs
+++ b/src/base/matrix.rs
@@ -914,9 +914,9 @@ impl<N: Scalar, D: Dim, S: StorageMut<N, D, D>> Matrix<N, D, D, S> {
 }
 
 impl<N: Complex, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
-    /// Takes the conjugate and transposes `self` and store the result into `out`.
+    /// Takes the adjoint (aka. conjugate-transpose) of `self` and store the result into `out`.
     #[inline]
-    pub fn conjugate_transpose_to<R2, C2, SB>(&self, out: &mut Matrix<N, R2, C2, SB>)
+    pub fn adjoint_to<R2, C2, SB>(&self, out: &mut Matrix<N, R2, C2, SB>)
     where
         R2: Dim,
         C2: Dim,
@@ -939,20 +939,41 @@ impl<N: Complex, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         }
     }
 
-    /// The conjugate transposition of `self`.
+    /// The adjoint (aka. conjugate-transpose) of `self`.
     #[inline]
-    pub fn conjugate_transpose(&self) -> MatrixMN<N, C, R>
+    pub fn adjoint(&self) -> MatrixMN<N, C, R>
     where DefaultAllocator: Allocator<N, C, R> {
         let (nrows, ncols) = self.data.shape();
 
         unsafe {
             let mut res: MatrixMN<_, C, R> = Matrix::new_uninitialized_generic(ncols, nrows);
-            self.conjugate_transpose_to(&mut res);
+            self.adjoint_to(&mut res);
 
             res
         }
     }
 
+    /// Takes the conjugate and transposes `self` and store the result into `out`.
+    #[deprecated(note = "Renamed `self.adjoint_to(out)`.")]
+    #[inline]
+    pub fn conjugate_transpose_to<R2, C2, SB>(&self, out: &mut Matrix<N, R2, C2, SB>)
+        where
+            R2: Dim,
+            C2: Dim,
+            SB: StorageMut<N, R2, C2>,
+            ShapeConstraint: SameNumberOfRows<R, C2> + SameNumberOfColumns<C, R2>,
+    {
+        self.adjoint_to(out)
+    }
+
+    /// The conjugate transposition of `self`.
+    #[deprecated(note = "Renamed `self.adjoint()`.")]
+    #[inline]
+    pub fn conjugate_transpose(&self) -> MatrixMN<N, C, R>
+        where DefaultAllocator: Allocator<N, C, R> {
+        self.adjoint()
+    }
+
     /// The conjugate of `self`.
     #[inline]
     pub fn conjugate(&self) -> MatrixMN<N, R, C>
@@ -1088,13 +1109,13 @@ impl<N: Complex, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
         tr
     }
 
-    /// The hermitian part of `self`, i.e., `0.5 * (self + self.conjugate_transpose())`.
+    /// The hermitian part of `self`, i.e., `0.5 * (self + self.adjoint())`.
     #[inline]
     pub fn hermitian_part(&self) -> MatrixMN<N, D, D>
         where DefaultAllocator: Allocator<N, D, D> {
         assert!(self.is_square(), "Cannot compute the hermitian part of a non-square matrix.");
 
-        let mut tr = self.conjugate_transpose();
+        let mut tr = self.adjoint();
         tr += self;
         tr *= ::convert::<_, N>(0.5);
         tr
@@ -1522,7 +1543,7 @@ impl<N: Complex, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         SB: Storage<N, R2, C2>,
         ShapeConstraint: DimEq<R, R2> + DimEq<C, C2>,
     {
-        let prod = self.cdot(other);
+        let prod = self.dotc(other);
         let n1 = self.norm();
         let n2 = other.norm();
 
@@ -1593,7 +1614,7 @@ impl<N: Complex, D: Dim, S: Storage<N, D>> Unit<Vector<N, D, S>> {
     where
         DefaultAllocator: Allocator<N, D>,
     {
-        let c_hang = self.cdot(rhs).real();
+        let c_hang = self.dotc(rhs).real();
 
         // self == other
         if c_hang.abs() >= N::Real::one() {
diff --git a/src/base/matrix_alga.rs b/src/base/matrix_alga.rs
index a2e1dd54..4d540f46 100644
--- a/src/base/matrix_alga.rs
+++ b/src/base/matrix_alga.rs
@@ -192,7 +192,7 @@ where DefaultAllocator: Allocator<N, R, C>
 
     #[inline]
     fn inner_product(&self, other: &Self) -> N {
-        self.cdot(other)
+        self.dotc(other)
     }
 }
 
diff --git a/src/base/norm.rs b/src/base/norm.rs
index b3056067..2a50f4ec 100644
--- a/src/base/norm.rs
+++ b/src/base/norm.rs
@@ -33,7 +33,7 @@ impl<N: Complex> Norm<N> for EuclideanNorm {
     #[inline]
     fn norm<R, C, S>(&self, m: &Matrix<N, R, C, S>) -> N::Real
         where R: Dim, C: Dim, S: Storage<N, R, C> {
-        m.cdot(m).real().sqrt()
+        m.dotc(m).real().sqrt()
     }
 
     #[inline]
@@ -101,7 +101,7 @@ impl<N: Complex, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
 
         for i in 0..self.ncols() {
             let col = self.column(i);
-            res += col.cdot(&col).real()
+            res += col.dotc(&col).real()
         }
 
         res
diff --git a/src/base/ops.rs b/src/base/ops.rs
index bee584b6..f2a683c1 100644
--- a/src/base/ops.rs
+++ b/src/base/ops.rs
@@ -13,7 +13,7 @@ use base::constraint::{
 };
 use base::dimension::{Dim, DimMul, DimName, DimProd};
 use base::storage::{ContiguousStorageMut, Storage, StorageMut};
-use base::{DefaultAllocator, Matrix, MatrixMN, MatrixN, MatrixSum, Scalar};
+use base::{DefaultAllocator, Matrix, MatrixMN, MatrixN, MatrixSum, Scalar, VectorSliceN};
 
 /*
  *
@@ -629,13 +629,28 @@ where
         res
     }
 
-    /// Equivalent to `self.transpose() * rhs` but stores the result into `out` to avoid
-    /// allocations.
+    /// Equivalent to `self.adjoint() * rhs`.
     #[inline]
-    pub fn tr_mul_to<R2: Dim, C2: Dim, SB, R3: Dim, C3: Dim, SC>(
+    pub fn ad_mul<R2: Dim, C2: Dim, SB>(&self, rhs: &Matrix<N, R2, C2, SB>) -> MatrixMN<N, C1, C2>
+        where
+            N: Complex,
+            SB: Storage<N, R2, C2>,
+            DefaultAllocator: Allocator<N, C1, C2>,
+            ShapeConstraint: SameNumberOfRows<R1, R2>,
+    {
+        let mut res =
+            unsafe { Matrix::new_uninitialized_generic(self.data.shape().1, rhs.data.shape().1) };
+
+        self.ad_mul_to(rhs, &mut res);
+        res
+    }
+
+    #[inline(always)]
+    fn xx_mul_to<R2: Dim, C2: Dim, SB, R3: Dim, C3: Dim, SC>(
         &self,
         rhs: &Matrix<N, R2, C2, SB>,
         out: &mut Matrix<N, R3, C3, SC>,
+        dot: impl Fn(&VectorSliceN<N, R1, SA::RStride, SA::CStride>, &VectorSliceN<N, R2, SB::RStride, SB::CStride>) -> N,
     ) where
         SB: Storage<N, R2, C2>,
         SC: StorageMut<N, R3, C3>,
@@ -656,12 +671,43 @@ where
 
         for i in 0..ncols1 {
             for j in 0..ncols2 {
-                let dot = self.column(i).dot(&rhs.column(j));
+                let dot = dot(&self.column(i), &rhs.column(j));
                 unsafe { *out.get_unchecked_mut((i, j)) = dot };
             }
         }
     }
 
+    /// Equivalent to `self.transpose() * rhs` but stores the result into `out` to avoid
+    /// allocations.
+    #[inline]
+    pub fn tr_mul_to<R2: Dim, C2: Dim, SB, R3: Dim, C3: Dim, SC>(
+        &self,
+        rhs: &Matrix<N, R2, C2, SB>,
+        out: &mut Matrix<N, R3, C3, SC>,
+    ) where
+        SB: Storage<N, R2, C2>,
+        SC: StorageMut<N, R3, C3>,
+        ShapeConstraint: SameNumberOfRows<R1, R2> + DimEq<C1, R3> + DimEq<C2, C3>,
+    {
+        self.xx_mul_to(rhs, out, |a, b| a.dot(b))
+    }
+
+    /// Equivalent to `self.adjoint() * rhs` but stores the result into `out` to avoid
+    /// allocations.
+    #[inline]
+    pub fn ad_mul_to<R2: Dim, C2: Dim, SB, R3: Dim, C3: Dim, SC>(
+        &self,
+        rhs: &Matrix<N, R2, C2, SB>,
+        out: &mut Matrix<N, R3, C3, SC>,
+    ) where
+        N: Complex,
+        SB: Storage<N, R2, C2>,
+        SC: StorageMut<N, R3, C3>,
+        ShapeConstraint: SameNumberOfRows<R1, R2> + DimEq<C1, R3> + DimEq<C2, C3>,
+    {
+        self.xx_mul_to(rhs, out, |a, b| a.dotc(b))
+    }
+
     /// Equivalent to `self * rhs` but stores the result into `out` to avoid allocations.
     #[inline]
     pub fn mul_to<R2: Dim, C2: Dim, SB, R3: Dim, C3: Dim, SC>(
@@ -760,35 +806,16 @@ where
     }
 }
 
-// XXX: avoid code duplication.
-impl<N: Complex, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
-    /// Returns the absolute value of the component with the largest absolute value.
-    #[inline]
-    pub fn camax(&self) -> N::Real {
-        let mut max = N::Real::zero();
+impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
+    #[inline(always)]
+    fn xcmp<N2>(&self, abs: impl Fn(N) -> N2, cmp: impl Fn(N2, N2) -> bool) -> N2
+        where N2: Scalar + PartialOrd + Zero {
+        let mut max = N2::zero();
 
         for e in self.iter() {
-            let ae = e.asum();
+            let ae = abs(*e);
 
-            if ae > max {
-                max = ae;
-            }
-        }
-
-        max
-    }
-}
-
-impl<N: Scalar + PartialOrd + Signed, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
-    /// Returns the absolute value of the component with the largest absolute value.
-    #[inline]
-    pub fn amax(&self) -> N {
-        let mut max = N::zero();
-
-        for e in self.iter() {
-            let ae = e.abs();
-
-            if ae > max {
+            if cmp(ae, max) {
                 max = ae;
             }
         }
@@ -796,61 +823,45 @@ impl<N: Scalar + PartialOrd + Signed, R: Dim, C: Dim, S: Storage<N, R, C>> Matri
         max
     }
 
-    /// Returns the absolute value of the component with the smallest absolute value.
+    /// Returns the absolute value of the component with the largest absolute value.
     #[inline]
-    pub fn amin(&self) -> N {
-        let mut it = self.iter();
-        let mut min = it
-            .next()
-            .expect("amin: empty matrices not supported.")
-            .abs();
+    pub fn amax(&self) -> N
+        where N: PartialOrd + Signed {
+        self.xcmp(|e| e.abs(), |a, b| a > b)
+    }
 
-        for e in it {
-            let ae = e.abs();
-
-            if ae < min {
-                min = ae;
-            }
-        }
-
-        min
+    /// Returns the the 1-norm of the complex component with the largest 1-norm.
+    #[inline]
+    pub fn camax(&self) -> N::Real
+        where N: Complex {
+        self.xcmp(|e| e.norm1(), |a, b| a > b)
     }
 
     /// Returns the component with the largest value.
     #[inline]
-    pub fn max(&self) -> N {
-        let mut it = self.iter();
-        let mut max = it
-            .next()
-            .expect("max: empty matrices not supported.");
+    pub fn max(&self) -> N
+        where N: PartialOrd + Signed {
+        self.xcmp(|e| e, |a, b| a > b)
+    }
 
-        for e in it {
-            let ae = e;
+    /// Returns the absolute value of the component with the smallest absolute value.
+    #[inline]
+    pub fn amin(&self) -> N
+        where N: PartialOrd + Signed {
+        self.xcmp(|e| e.abs(), |a, b| a < b)
+    }
 
-            if ae > max {
-                max = ae;
-            }
-        }
-
-        *max
+    /// Returns the the 1-norm of the complex component with the smallest 1-norm.
+    #[inline]
+    pub fn camin(&self) -> N::Real
+        where N: Complex {
+        self.xcmp(|e| e.norm1(), |a, b| a < b)
     }
 
     /// Returns the component with the smallest value.
     #[inline]
-    pub fn min(&self) -> N {
-        let mut it = self.iter();
-        let mut min = it
-            .next()
-            .expect("min: empty matrices not supported.");
-
-        for e in it {
-            let ae = e;
-
-            if ae < min {
-                min = ae;
-            }
-        }
-
-        *min
+    pub fn min(&self) -> N
+        where N: PartialOrd + Signed {
+        self.xcmp(|e| e, |a, b| a < b)
     }
-}
+}
\ No newline at end of file
diff --git a/src/base/properties.rs b/src/base/properties.rs
index a511c0f8..de6be72a 100644
--- a/src/base/properties.rs
+++ b/src/base/properties.rs
@@ -97,8 +97,7 @@ impl<N: Complex, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
             N::Epsilon: Copy,
             DefaultAllocator: Allocator<N, R, C> + Allocator<N, C, C>,
     {
-        // FIXME: add a conjugate-transpose-mul
-        (self.conjugate().tr_mul(self)).is_identity(eps)
+        (self.ad_mul(self)).is_identity(eps)
     }
 }
 
diff --git a/src/debug/random_sdp.rs b/src/debug/random_sdp.rs
index 772eccc9..f8ee6d30 100644
--- a/src/debug/random_sdp.rs
+++ b/src/debug/random_sdp.rs
@@ -31,7 +31,7 @@ where DefaultAllocator: Allocator<N, D, D>
     /// random reals generators.
     pub fn new<Rand: FnMut() -> N>(dim: D, mut rand: Rand) -> Self {
         let mut m = RandomOrthogonal::new(dim, || rand()).unwrap();
-        let mt = m.conjugate_transpose();
+        let mt = m.adjoint();
 
         for i in 0..dim.value() {
             let mut col = m.column_mut(i);
diff --git a/src/geometry/reflection.rs b/src/geometry/reflection.rs
index b3c77bd9..653a9bdc 100644
--- a/src/geometry/reflection.rs
+++ b/src/geometry/reflection.rs
@@ -35,7 +35,7 @@ impl<N: Complex, D: Dim, S: Storage<N, D>> Reflection<N, D, S> {
         D: DimName,
         DefaultAllocator: Allocator<N, D>,
     {
-        let bias = axis.cdot(&pt.coords);
+        let bias = axis.dotc(&pt.coords);
         Self::new(axis, bias)
     }
 
@@ -56,7 +56,7 @@ impl<N: Complex, D: Dim, S: Storage<N, D>> Reflection<N, D, S> {
             // dot product, and then mutably. Somehow, this allows significantly
             // better optimizations of the dot product from the compiler.
             let m_two: N = ::convert(-2.0f64);
-            let factor = (self.axis.cdot(&rhs.column(i)) - self.bias) * m_two;
+            let factor = (self.axis.dotc(&rhs.column(i)) - self.bias) * m_two;
             rhs.column_mut(i).axpy(factor, &self.axis, N::one());
         }
     }
@@ -73,7 +73,7 @@ impl<N: Complex, D: Dim, S: Storage<N, D>> Reflection<N, D, S> {
             // dot product, and then mutably. Somehow, this allows significantly
             // better optimizations of the dot product from the compiler.
             let m_two = sign.scale(::convert(-2.0f64));
-            let factor = (self.axis.cdot(&rhs.column(i)) - self.bias) * m_two;
+            let factor = (self.axis.dotc(&rhs.column(i)) - self.bias) * m_two;
             rhs.column_mut(i).axpy(factor, &self.axis, sign);
         }
     }
diff --git a/src/lib.rs b/src/lib.rs
index a718cf91..c2bb04f2 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -170,6 +170,7 @@ pub use alga::general::{Id, Real, Complex};
 /// Gets the ubiquitous multiplicative identity element.
 ///
 /// Same as `Id::new()`.
+#[deprecated(note = "use `Id::new()` instead.")]
 #[inline]
 pub fn id() -> Id {
     Id::new()
@@ -416,6 +417,7 @@ pub fn partial_sort2<'a, T: PartialOrd>(a: &'a T, b: &'a T) -> Option<(&'a T, &'
 /// # See also:
 ///
 /// * [`inverse`](fn.inverse.html)
+#[deprecated(note = "use the `.try_inverse()` method instead")]
 #[inline]
 pub fn try_inverse<M: AlgaSquareMatrix>(m: &M) -> Option<M> {
     m.try_inverse()
@@ -426,6 +428,7 @@ pub fn try_inverse<M: AlgaSquareMatrix>(m: &M) -> Option<M> {
 /// # See also:
 ///
 /// * [`try_inverse`](fn.try_inverse.html)
+#[deprecated(note = "use the `.inverse()` method instead")]
 #[inline]
 pub fn inverse<M: TwoSidedInverse<Multiplicative>>(m: &M) -> M {
     m.two_sided_inverse()
diff --git a/src/linalg/cholesky.rs b/src/linalg/cholesky.rs
index 682bd990..d4973df1 100644
--- a/src/linalg/cholesky.rs
+++ b/src/linalg/cholesky.rs
@@ -121,7 +121,7 @@ where DefaultAllocator: Allocator<N, D, D>
         ShapeConstraint: SameNumberOfRows<R2, D>,
     {
         let _ = self.chol.solve_lower_triangular_mut(b);
-        let _ = self.chol.conjugate().tr_solve_lower_triangular_mut(b);
+        let _ = self.chol.ad_solve_lower_triangular_mut(b);
     }
 
     /// Returns the solution of the system `self * x = b` where `self` is the decomposed matrix and
diff --git a/src/linalg/mod.rs b/src/linalg/mod.rs
index 6c7f7194..4418b283 100644
--- a/src/linalg/mod.rs
+++ b/src/linalg/mod.rs
@@ -30,6 +30,6 @@ pub use self::lu::*;
 pub use self::permutation_sequence::*;
 pub use self::qr::*;
 pub use self::schur::*;
-//pub use self::svd::*;
+pub use self::svd::*;
 pub use self::symmetric_eigen::*;
 pub use self::symmetric_tridiagonal::*;
diff --git a/src/linalg/solve.rs b/src/linalg/solve.rs
index b43b44df..fad85a81 100644
--- a/src/linalg/solve.rs
+++ b/src/linalg/solve.rs
@@ -4,7 +4,7 @@ use base::allocator::Allocator;
 use base::constraint::{SameNumberOfRows, ShapeConstraint};
 use base::dimension::{Dim, U1};
 use base::storage::{Storage, StorageMut};
-use base::{DefaultAllocator, Matrix, MatrixMN, SquareMatrix, Vector};
+use base::{DefaultAllocator, Matrix, MatrixMN, SquareMatrix, Vector, DVectorSlice};
 
 impl<N: Complex, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
     /// Computes the solution of the linear system `self . x = b` where `x` is the unknown and only
@@ -180,10 +180,9 @@ impl<N: Complex, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
 
     /*
      *
-     * Transpose versions
+     * Transpose and adjoint versions
      *
      */
-
     /// Computes the solution of the linear system `self.transpose() . x = b` where `x` is the unknown and only
     /// the lower-triangular part of `self` (including the diagonal) is considered not-zero.
     #[inline]
@@ -191,10 +190,10 @@ impl<N: Complex, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
         &self,
         b: &Matrix<N, R2, C2, S2>,
     ) -> Option<MatrixMN<N, R2, C2>>
-    where
-        S2: StorageMut<N, R2, C2>,
-        DefaultAllocator: Allocator<N, R2, C2>,
-        ShapeConstraint: SameNumberOfRows<R2, D>,
+        where
+            S2: StorageMut<N, R2, C2>,
+            DefaultAllocator: Allocator<N, R2, C2>,
+            ShapeConstraint: SameNumberOfRows<R2, D>,
     {
         let mut res = b.clone_owned();
         if self.tr_solve_lower_triangular_mut(&mut res) {
@@ -211,10 +210,10 @@ impl<N: Complex, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
         &self,
         b: &Matrix<N, R2, C2, S2>,
     ) -> Option<MatrixMN<N, R2, C2>>
-    where
-        S2: StorageMut<N, R2, C2>,
-        DefaultAllocator: Allocator<N, R2, C2>,
-        ShapeConstraint: SameNumberOfRows<R2, D>,
+        where
+            S2: StorageMut<N, R2, C2>,
+            DefaultAllocator: Allocator<N, R2, C2>,
+            ShapeConstraint: SameNumberOfRows<R2, D>,
     {
         let mut res = b.clone_owned();
         if self.tr_solve_upper_triangular_mut(&mut res) {
@@ -230,14 +229,14 @@ impl<N: Complex, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
         &self,
         b: &mut Matrix<N, R2, C2, S2>,
     ) -> bool
-    where
-        S2: StorageMut<N, R2, C2>,
-        ShapeConstraint: SameNumberOfRows<R2, D>,
+        where
+            S2: StorageMut<N, R2, C2>,
+            ShapeConstraint: SameNumberOfRows<R2, D>,
     {
         let cols = b.ncols();
 
         for i in 0..cols {
-            if !self.tr_solve_lower_triangular_vector_mut(&mut b.column_mut(i)) {
+            if !self.xx_solve_lower_triangular_vector_mut(&mut b.column_mut(i), |e| e, |a, b| a.dot(b)) {
                 return false;
             }
         }
@@ -245,46 +244,20 @@ impl<N: Complex, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
         true
     }
 
-    fn tr_solve_lower_triangular_vector_mut<R2: Dim, S2>(&self, b: &mut Vector<N, R2, S2>) -> bool
-    where
-        S2: StorageMut<N, R2, U1>,
-        ShapeConstraint: SameNumberOfRows<R2, D>,
-    {
-        let dim = self.nrows();
-
-        for i in (0..dim).rev() {
-            let dot = self.slice_range(i + 1.., i).dot(&b.slice_range(i + 1.., 0));
-
-            unsafe {
-                let b_i = b.vget_unchecked_mut(i);
-
-                let diag = *self.get_unchecked((i, i));
-
-                if diag.is_zero() {
-                    return false;
-                }
-
-                *b_i = (*b_i - dot) / diag;
-            }
-        }
-
-        true
-    }
-
     /// Solves the linear system `self.transpose() . x = b` where `x` is the unknown and only the
     /// upper-triangular part of `self` (including the diagonal) is considered not-zero.
     pub fn tr_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>,
+        where
+            S2: StorageMut<N, R2, C2>,
+            ShapeConstraint: SameNumberOfRows<R2, D>,
     {
         let cols = b.ncols();
 
         for i in 0..cols {
-            if !self.tr_solve_upper_triangular_vector_mut(&mut b.column_mut(i)) {
+            if !self.xx_solve_upper_triangular_vector_mut(&mut b.column_mut(i), |e| e, |a, b| a.dot(b)) {
                 return false;
             }
         }
@@ -292,19 +265,140 @@ impl<N: Complex, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
         true
     }
 
-    fn tr_solve_upper_triangular_vector_mut<R2: Dim, S2>(&self, b: &mut Vector<N, R2, S2>) -> bool
-    where
-        S2: StorageMut<N, R2, U1>,
-        ShapeConstraint: SameNumberOfRows<R2, D>,
+    /// Computes the solution of the linear system `self.adjoint() . x = b` where `x` is the unknown and only
+    /// the lower-triangular part of `self` (including the diagonal) is considered not-zero.
+    #[inline]
+    pub fn ad_solve_lower_triangular<R2: Dim, C2: Dim, S2>(
+        &self,
+        b: &Matrix<N, R2, C2, S2>,
+    ) -> Option<MatrixMN<N, R2, C2>>
+        where
+            S2: StorageMut<N, R2, C2>,
+            DefaultAllocator: Allocator<N, R2, C2>,
+            ShapeConstraint: SameNumberOfRows<R2, D>,
+    {
+        let mut res = b.clone_owned();
+        if self.ad_solve_lower_triangular_mut(&mut res) {
+            Some(res)
+        } else {
+            None
+        }
+    }
+
+    /// Computes the solution of the linear system `self.adjoint() . x = b` where `x` is the unknown and only
+    /// the upper-triangular part of `self` (including the diagonal) is considered not-zero.
+    #[inline]
+    pub fn ad_solve_upper_triangular<R2: Dim, C2: Dim, S2>(
+        &self,
+        b: &Matrix<N, R2, C2, S2>,
+    ) -> Option<MatrixMN<N, R2, C2>>
+        where
+            S2: StorageMut<N, R2, C2>,
+            DefaultAllocator: Allocator<N, R2, C2>,
+            ShapeConstraint: SameNumberOfRows<R2, D>,
+    {
+        let mut res = b.clone_owned();
+        if self.ad_solve_upper_triangular_mut(&mut res) {
+            Some(res)
+        } else {
+            None
+        }
+    }
+
+    /// Solves the linear system `self.adjoint() . x = b` where `x` is the unknown and only the
+    /// lower-triangular part of `self` (including the diagonal) is considered not-zero.
+    pub fn ad_solve_lower_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 cols = b.ncols();
+
+        for i in 0..cols {
+            if !self.xx_solve_lower_triangular_vector_mut(&mut b.column_mut(i), |e| e.conjugate(), |a, b| a.dotc(b)) {
+                return false;
+            }
+        }
+
+        true
+    }
+
+    /// Solves the linear system `self.adjoint() . x = b` where `x` is the unknown and only the
+    /// upper-triangular part of `self` (including the diagonal) is considered not-zero.
+    pub fn ad_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 cols = b.ncols();
+
+        for i in 0..cols {
+            if !self.xx_solve_upper_triangular_vector_mut(&mut b.column_mut(i), |e| e.conjugate(), |a, b| a.dotc(b)) {
+                return false;
+            }
+        }
+
+        true
+    }
+
+
+    #[inline(always)]
+    fn xx_solve_lower_triangular_vector_mut<R2: Dim, S2>(
+        &self,
+        b: &mut Vector<N, R2, S2>,
+        conjugate: impl Fn(N) -> N,
+        dot: impl Fn(&DVectorSlice<N, S::RStride, S::CStride>, &DVectorSlice<N, S2::RStride, S2::CStride>) -> N,
+    ) -> bool
+        where
+            S2: StorageMut<N, R2, U1>,
+            ShapeConstraint: SameNumberOfRows<R2, D>,
+    {
+        let dim = self.nrows();
+
+        for i in (0..dim).rev() {
+            let dot = dot(&self.slice_range(i + 1.., i), &b.slice_range(i + 1.., 0));
+
+            unsafe {
+                let b_i = b.vget_unchecked_mut(i);
+
+                let diag = conjugate(*self.get_unchecked((i, i)));
+
+                if diag.is_zero() {
+                    return false;
+                }
+
+                *b_i = (*b_i - dot) / diag;
+            }
+        }
+
+        true
+    }
+
+    #[inline(always)]
+    fn xx_solve_upper_triangular_vector_mut<R2: Dim, S2>(
+        &self,
+        b: &mut Vector<N, R2, S2>,
+        conjugate: impl Fn(N) -> N,
+        dot: impl Fn(&DVectorSlice<N, S::RStride, S::CStride>, &DVectorSlice<N, S2::RStride, S2::CStride>) -> N,
+    ) -> bool
+        where
+            S2: StorageMut<N, R2, U1>,
+            ShapeConstraint: SameNumberOfRows<R2, D>,
     {
         let dim = self.nrows();
 
         for i in 0..dim {
-            let dot = self.slice_range(..i, i).dot(&b.slice_range(..i, 0));
+            let dot = dot(&self.slice_range(..i, i), &b.slice_range(..i, 0));
 
             unsafe {
                 let b_i = b.vget_unchecked_mut(i);
-                let diag = *self.get_unchecked((i, i));
+                let diag = conjugate(*self.get_unchecked((i, i)));
 
                 if diag.is_zero() {
                     return false;
diff --git a/src/linalg/svd.rs b/src/linalg/svd.rs
index a455badd..12ee0469 100644
--- a/src/linalg/svd.rs
+++ b/src/linalg/svd.rs
@@ -496,7 +496,7 @@ where
                 }
             }
 
-            self.recompose().map(|m| m.conjugate_transpose())
+            self.recompose().map(|m| m.adjoint())
         }
     }
 
@@ -521,7 +521,7 @@ where
         else {
             match (&self.u, &self.v_t) {
                 (Some(u), Some(v_t)) => {
-                    let mut ut_b = u.conjugate().tr_mul(b);
+                    let mut ut_b = u.ad_mul(b);
 
                     for j in 0..ut_b.ncols() {
                         let mut col = ut_b.column_mut(j);
@@ -536,7 +536,7 @@ where
                         }
                     }
 
-                    Ok(v_t.conjugate().tr_mul(&ut_b))
+                    Ok(v_t.ad_mul(&ut_b))
                 }
                 (None, None) => Err("SVD solve: U and V^t have not been computed."),
                 (None, _) => Err("SVD solve: U has not been computed."),
diff --git a/src/linalg/symmetric_tridiagonal.rs b/src/linalg/symmetric_tridiagonal.rs
index 29451d31..d652ce4c 100644
--- a/src/linalg/symmetric_tridiagonal.rs
+++ b/src/linalg/symmetric_tridiagonal.rs
@@ -75,13 +75,13 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, DimDiff<D, U1>>
             if not_zero {
                 let mut p = p.rows_range_mut(i..);
 
-                p.cgemv_symm(::convert(2.0), &m, &axis, N::zero());
-                let dot = axis.cdot(&p);
+                p.hegemv(::convert(2.0), &m, &axis, N::zero());
+                let dot = axis.dotc(&p);
 
 //                p.axpy(-dot, &axis.conjugate(), N::one());
-                m.ger_symm(-N::one(), &p, &axis.conjugate(), N::one());
-                m.ger_symm(-N::one(), &axis, &p.conjugate(), N::one());
-                m.ger_symm(dot * ::convert(2.0), &axis, &axis.conjugate(), N::one());
+                m.hegerc(-N::one(), &p, &axis, N::one());
+                m.hegerc(-N::one(), &axis, &p, N::one());
+                m.hegerc(dot * ::convert(2.0), &axis, &axis, N::one());
             }
         }
 
@@ -142,7 +142,7 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, DimDiff<D, U1>>
             self.tri[(i, i + 1)] = val;
         }
 
-        &q * self.tri * q.conjugate_transpose()
+        &q * self.tri * q.adjoint()
     }
 }
 
diff --git a/tests/core/blas.rs b/tests/core/blas.rs
index 00eac3a3..38113c17 100644
--- a/tests/core/blas.rs
+++ b/tests/core/blas.rs
@@ -19,14 +19,14 @@ quickcheck! {
         let mut y2 = y1.clone();
 
         y1.gemv(alpha, &a, &x, beta);
-        y2.gemv_symm(alpha, &a.lower_triangle(), &x, beta);
+        y2.sygemv(alpha, &a.lower_triangle(), &x, beta);
 
         if !relative_eq!(y1, y2, epsilon = 1.0e-10) {
             return false;
         }
 
         y1.gemv(alpha, &a, &x, 0.0);
-        y2.gemv_symm(alpha, &a.lower_triangle(), &x, 0.0);
+        y2.sygemv(alpha, &a.lower_triangle(), &x, 0.0);
 
         relative_eq!(y1, y2, epsilon = 1.0e-10)
     }
@@ -61,14 +61,14 @@ quickcheck! {
         let y = DVector::new_random(n);
 
         a1.ger(alpha, &x, &y, beta);
-        a2.ger_symm(alpha, &x, &y, beta);
+        a2.syger(alpha, &x, &y, beta);
 
         if !relative_eq!(a1.lower_triangle(), a2) {
             return false;
         }
 
         a1.ger(alpha, &x, &y, 0.0);
-        a2.ger_symm(alpha, &x, &y, 0.0);
+        a2.syger(alpha, &x, &y, 0.0);
 
         relative_eq!(a1.lower_triangle(), a2)
     }
diff --git a/tests/linalg/cholesky.rs b/tests/linalg/cholesky.rs
index 5bc91dc2..774b76b0 100644
--- a/tests/linalg/cholesky.rs
+++ b/tests/linalg/cholesky.rs
@@ -16,7 +16,7 @@ macro_rules! gen_tests(
                 fn cholesky(n: usize) -> bool {
                     let m = RandomSDP::new(Dynamic::new(n.max(1).min(50)), || random::<$scalar>().0).unwrap();
                     let l = m.clone().cholesky().unwrap().unpack();
-                    relative_eq!(m, &l * l.conjugate_transpose(), epsilon = 1.0e-7)
+                    relative_eq!(m, &l * l.adjoint(), epsilon = 1.0e-7)
                 }
 
                 fn cholesky_static(_m: RandomSDP<f64, U4>) -> bool {
@@ -24,7 +24,7 @@ macro_rules! gen_tests(
                     let chol = m.cholesky().unwrap();
                     let l    = chol.unpack();
 
-                    if !relative_eq!(m, &l * l.conjugate_transpose(), epsilon = 1.0e-7) {
+                    if !relative_eq!(m, &l * l.adjoint(), epsilon = 1.0e-7) {
                         false
                     }
                     else {
diff --git a/tests/linalg/hessenberg.rs b/tests/linalg/hessenberg.rs
index dfabd29d..03cba7d1 100644
--- a/tests/linalg/hessenberg.rs
+++ b/tests/linalg/hessenberg.rs
@@ -27,21 +27,21 @@ macro_rules! gen_tests(
 
                     let hess = m.clone().hessenberg();
                     let (p, h) = hess.unpack();
-                    relative_eq!(m, &p * h * p.conjugate_transpose(), epsilon = 1.0e-7)
+                    relative_eq!(m, &p * h * p.adjoint(), epsilon = 1.0e-7)
                 }
 
                 fn hessenberg_static_mat2(m: Matrix2<$scalar>) -> bool {
                     let m = m.map(|e| e.0);
                     let hess = m.hessenberg();
                     let (p, h) = hess.unpack();
-                    relative_eq!(m, p * h * p.conjugate_transpose(), epsilon = 1.0e-7)
+                    relative_eq!(m, p * h * p.adjoint(), epsilon = 1.0e-7)
                 }
 
                 fn hessenberg_static(m: Matrix4<$scalar>) -> bool {
                     let m = m.map(|e| e.0);
                     let hess = m.hessenberg();
                     let (p, h) = hess.unpack();
-                    relative_eq!(m, p * h * p.conjugate_transpose(), epsilon = 1.0e-7)
+                    relative_eq!(m, p * h * p.adjoint(), epsilon = 1.0e-7)
                 }
             }
          }
diff --git a/tests/linalg/schur.rs b/tests/linalg/schur.rs
index 16767c94..f607ce06 100644
--- a/tests/linalg/schur.rs
+++ b/tests/linalg/schur.rs
@@ -31,21 +31,21 @@ mod quickcheck_tests {
 
                         let (vecs, vals) = m.clone().schur().unpack();
 
-                        if !relative_eq!(&vecs * &vals * vecs.conjugate_transpose(), m, epsilon = 1.0e-7) {
-                            println!("{:.5}{:.5}", m, &vecs * &vals * vecs.conjugate_transpose());
+                        if !relative_eq!(&vecs * &vals * vecs.adjoint(), m, epsilon = 1.0e-7) {
+                            println!("{:.5}{:.5}", m, &vecs * &vals * vecs.adjoint());
                         }
 
-                        relative_eq!(&vecs * vals * vecs.conjugate_transpose(), m, epsilon = 1.0e-7)
+                        relative_eq!(&vecs * vals * vecs.adjoint(), m, epsilon = 1.0e-7)
                     }
 
                     fn schur_static_mat2(m: Matrix2<$scalar>) -> bool {
                         let m = m.map(|e| e.0);
                         let (vecs, vals) = m.clone().schur().unpack();
 
-                        let ok = relative_eq!(vecs * vals * vecs.conjugate_transpose(), m, epsilon = 1.0e-7);
+                        let ok = relative_eq!(vecs * vals * vecs.adjoint(), m, epsilon = 1.0e-7);
                         if !ok {
                             println!("Vecs: {:.5} Vals: {:.5}", vecs, vals);
-                            println!("Reconstruction:{}{}", m, &vecs * &vals * vecs.conjugate_transpose());
+                            println!("Reconstruction:{}{}", m, &vecs * &vals * vecs.adjoint());
                         }
                         ok
                     }
@@ -54,10 +54,10 @@ mod quickcheck_tests {
                         let m = m.map(|e| e.0);
                         let (vecs, vals) = m.clone().schur().unpack();
 
-                        let ok = relative_eq!(vecs * vals * vecs.conjugate_transpose(), m, epsilon = 1.0e-7);
+                        let ok = relative_eq!(vecs * vals * vecs.adjoint(), m, epsilon = 1.0e-7);
                         if !ok {
                             println!("Vecs: {:.5} Vals: {:.5}", vecs, vals);
-                            println!("{:.5}{:.5}", m, &vecs * &vals * vecs.conjugate_transpose());
+                            println!("{:.5}{:.5}", m, &vecs * &vals * vecs.adjoint());
                         }
                         ok
                     }
@@ -66,9 +66,9 @@ mod quickcheck_tests {
                         let m = m.map(|e| e.0);
                         let (vecs, vals) = m.clone().schur().unpack();
 
-                        let ok = relative_eq!(vecs * vals * vecs.conjugate_transpose(), m, epsilon = 1.0e-7);
+                        let ok = relative_eq!(vecs * vals * vecs.adjoint(), m, epsilon = 1.0e-7);
                         if !ok {
-                            println!("{:.5}{:.5}", m, &vecs * &vals * vecs.conjugate_transpose());
+                            println!("{:.5}{:.5}", m, &vecs * &vals * vecs.adjoint());
                         }
 
                         ok
diff --git a/tests/linalg/solve.rs b/tests/linalg/solve.rs
index e2f8a3f8..3a9ff143 100644
--- a/tests/linalg/solve.rs
+++ b/tests/linalg/solve.rs
@@ -10,7 +10,7 @@ macro_rules! gen_tests(
 
             fn unzero_diagonal<N: Complex>(a: &mut Matrix4<N>) {
                 for i in 0..4 {
-                    if a[(i, i)].asum() < na::convert(1.0e-7) {
+                    if a[(i, i)].norm1() < na::convert(1.0e-7) {
                         a[(i, i)] = N::one();
                     }
                 }