diff --git a/src/lapack_traits/solveh.rs b/src/lapack_traits/solveh.rs
index 39d2b6b1..e8b05294 100644
--- a/src/lapack_traits/solveh.rs
+++ b/src/lapack_traits/solveh.rs
@@ -39,7 +39,10 @@ impl Solveh_ for $scalar {
     unsafe fn solveh(l: MatrixLayout, uplo: UPLO, a: &[Self], ipiv: &Pivot, b: &mut [Self]) -> Result<()> {
         let (n, _) = l.size();
         let nrhs = 1;
-        let ldb = 1;
+        let ldb = match l {
+            MatrixLayout::C(_) => 1,
+            MatrixLayout::F(_) => n,
+        };
         let info = $trs(l.lapacke_layout(), uplo as u8, n, nrhs, a, l.lda(), ipiv, b, ldb);
         into_result(info, ())
     }
diff --git a/tests/det.rs b/tests/det.rs
new file mode 100644
index 00000000..dba4f598
--- /dev/null
+++ b/tests/det.rs
@@ -0,0 +1,141 @@
+extern crate ndarray;
+#[macro_use]
+extern crate ndarray_linalg;
+extern crate num_traits;
+
+use ndarray::*;
+use ndarray_linalg::*;
+use num_traits::{One, Zero};
+
+/// Returns the matrix with the specified `row` and `col` removed.
+fn matrix_minor<A, S>(a: ArrayBase<S, Ix2>, (row, col): (usize, usize)) -> Array2<A>
+where
+    A: Scalar,
+    S: Data<Elem = A>,
+{
+    let mut select_rows = (0..a.rows()).collect::<Vec<_>>();
+    select_rows.remove(row);
+    let mut select_cols = (0..a.cols()).collect::<Vec<_>>();
+    select_cols.remove(col);
+    a.select(Axis(0), &select_rows).select(
+        Axis(1),
+        &select_cols,
+    )
+}
+
+/// Computes the determinant of matrix `a`.
+///
+/// Note: This implementation is written to be clearly correct so that it's
+/// useful for verification, but it's very inefficient.
+fn det_naive<A, S>(a: ArrayBase<S, Ix2>) -> A
+where
+    A: Scalar,
+    S: Data<Elem = A>,
+{
+    assert_eq!(a.rows(), a.cols());
+    match a.cols() {
+        0 => A::one(),
+        1 => a[(0, 0)],
+        cols => {
+            (0..cols)
+                .map(|col| {
+                    let sign = if col % 2 == 0 { A::one() } else { -A::one() };
+                    sign * a[(0, col)] * det_naive(matrix_minor(a.view(), (0, col)))
+                })
+                .fold(A::zero(), |sum, subdet| sum + subdet)
+        }
+    }
+}
+
+#[test]
+fn det_empty() {
+    macro_rules! det_empty {
+        ($elem:ty) => {
+            let a: Array2<$elem> = Array2::zeros((0, 0));
+            assert_eq!(a.factorize().unwrap().det().unwrap(), One::one());
+            assert_eq!(a.factorize().unwrap().det_into().unwrap(), One::one());
+            assert_eq!(a.det().unwrap(), One::one());
+            assert_eq!(a.det_into().unwrap(), One::one());
+        }
+    }
+    det_empty!(f64);
+    det_empty!(f32);
+    det_empty!(c64);
+    det_empty!(c32);
+}
+
+#[test]
+fn det_zero() {
+    macro_rules! det_zero {
+        ($elem:ty) => {
+            let a: Array2<$elem> = Array2::zeros((1, 1));
+            assert_eq!(a.det().unwrap(), Zero::zero());
+            assert_eq!(a.det_into().unwrap(), Zero::zero());
+        }
+    }
+    det_zero!(f64);
+    det_zero!(f32);
+    det_zero!(c64);
+    det_zero!(c32);
+}
+
+#[test]
+fn det_zero_nonsquare() {
+    macro_rules! det_zero_nonsquare {
+        ($elem:ty, $shape:expr) => {
+            let a: Array2<$elem> = Array2::zeros($shape);
+            assert!(a.det().is_err());
+            assert!(a.det_into().is_err());
+        }
+    }
+    for &shape in &[(1, 2).into_shape(), (1, 2).f()] {
+        det_zero_nonsquare!(f64, shape);
+        det_zero_nonsquare!(f32, shape);
+        det_zero_nonsquare!(c64, shape);
+        det_zero_nonsquare!(c32, shape);
+    }
+}
+
+#[test]
+fn det() {
+    macro_rules! det {
+        ($elem:ty, $shape:expr, $rtol:expr) => {
+            let a: Array2<$elem> = random($shape);
+            println!("a = \n{:?}", a);
+            let det = det_naive(a.view());
+            assert_rclose!(a.factorize().unwrap().det().unwrap(), det, $rtol);
+            assert_rclose!(a.factorize().unwrap().det_into().unwrap(), det, $rtol);
+            assert_rclose!(a.det().unwrap(), det, $rtol);
+            assert_rclose!(a.det_into().unwrap(), det, $rtol);
+        }
+    }
+    for rows in 1..5 {
+        for &shape in &[(rows, rows).into_shape(), (rows, rows).f()] {
+            det!(f64, shape, 1e-9);
+            det!(f32, shape, 1e-4);
+            det!(c64, shape, 1e-9);
+            det!(c32, shape, 1e-4);
+        }
+    }
+}
+
+#[test]
+fn det_nonsquare() {
+    macro_rules! det_nonsquare {
+        ($elem:ty, $shape:expr) => {
+            let a: Array2<$elem> = random($shape);
+            assert!(a.factorize().unwrap().det().is_err());
+            assert!(a.factorize().unwrap().det_into().is_err());
+            assert!(a.det().is_err());
+            assert!(a.det_into().is_err());
+        }
+    }
+    for &dims in &[(1, 0), (1, 2), (2, 1), (2, 3)] {
+        for &shape in &[dims.clone().into_shape(), dims.clone().f()] {
+            det_nonsquare!(f64, shape);
+            det_nonsquare!(f32, shape);
+            det_nonsquare!(c64, shape);
+            det_nonsquare!(c32, shape);
+        }
+    }
+}
diff --git a/tests/solve.rs b/tests/solve.rs
index dba4f598..48b66782 100644
--- a/tests/solve.rs
+++ b/tests/solve.rs
@@ -5,137 +5,21 @@ extern crate num_traits;
 
 use ndarray::*;
 use ndarray_linalg::*;
-use num_traits::{One, Zero};
-
-/// Returns the matrix with the specified `row` and `col` removed.
-fn matrix_minor<A, S>(a: ArrayBase<S, Ix2>, (row, col): (usize, usize)) -> Array2<A>
-where
-    A: Scalar,
-    S: Data<Elem = A>,
-{
-    let mut select_rows = (0..a.rows()).collect::<Vec<_>>();
-    select_rows.remove(row);
-    let mut select_cols = (0..a.cols()).collect::<Vec<_>>();
-    select_cols.remove(col);
-    a.select(Axis(0), &select_rows).select(
-        Axis(1),
-        &select_cols,
-    )
-}
-
-/// Computes the determinant of matrix `a`.
-///
-/// Note: This implementation is written to be clearly correct so that it's
-/// useful for verification, but it's very inefficient.
-fn det_naive<A, S>(a: ArrayBase<S, Ix2>) -> A
-where
-    A: Scalar,
-    S: Data<Elem = A>,
-{
-    assert_eq!(a.rows(), a.cols());
-    match a.cols() {
-        0 => A::one(),
-        1 => a[(0, 0)],
-        cols => {
-            (0..cols)
-                .map(|col| {
-                    let sign = if col % 2 == 0 { A::one() } else { -A::one() };
-                    sign * a[(0, col)] * det_naive(matrix_minor(a.view(), (0, col)))
-                })
-                .fold(A::zero(), |sum, subdet| sum + subdet)
-        }
-    }
-}
-
-#[test]
-fn det_empty() {
-    macro_rules! det_empty {
-        ($elem:ty) => {
-            let a: Array2<$elem> = Array2::zeros((0, 0));
-            assert_eq!(a.factorize().unwrap().det().unwrap(), One::one());
-            assert_eq!(a.factorize().unwrap().det_into().unwrap(), One::one());
-            assert_eq!(a.det().unwrap(), One::one());
-            assert_eq!(a.det_into().unwrap(), One::one());
-        }
-    }
-    det_empty!(f64);
-    det_empty!(f32);
-    det_empty!(c64);
-    det_empty!(c32);
-}
-
-#[test]
-fn det_zero() {
-    macro_rules! det_zero {
-        ($elem:ty) => {
-            let a: Array2<$elem> = Array2::zeros((1, 1));
-            assert_eq!(a.det().unwrap(), Zero::zero());
-            assert_eq!(a.det_into().unwrap(), Zero::zero());
-        }
-    }
-    det_zero!(f64);
-    det_zero!(f32);
-    det_zero!(c64);
-    det_zero!(c32);
-}
-
-#[test]
-fn det_zero_nonsquare() {
-    macro_rules! det_zero_nonsquare {
-        ($elem:ty, $shape:expr) => {
-            let a: Array2<$elem> = Array2::zeros($shape);
-            assert!(a.det().is_err());
-            assert!(a.det_into().is_err());
-        }
-    }
-    for &shape in &[(1, 2).into_shape(), (1, 2).f()] {
-        det_zero_nonsquare!(f64, shape);
-        det_zero_nonsquare!(f32, shape);
-        det_zero_nonsquare!(c64, shape);
-        det_zero_nonsquare!(c32, shape);
-    }
-}
 
 #[test]
-fn det() {
-    macro_rules! det {
-        ($elem:ty, $shape:expr, $rtol:expr) => {
-            let a: Array2<$elem> = random($shape);
-            println!("a = \n{:?}", a);
-            let det = det_naive(a.view());
-            assert_rclose!(a.factorize().unwrap().det().unwrap(), det, $rtol);
-            assert_rclose!(a.factorize().unwrap().det_into().unwrap(), det, $rtol);
-            assert_rclose!(a.det().unwrap(), det, $rtol);
-            assert_rclose!(a.det_into().unwrap(), det, $rtol);
-        }
-    }
-    for rows in 1..5 {
-        for &shape in &[(rows, rows).into_shape(), (rows, rows).f()] {
-            det!(f64, shape, 1e-9);
-            det!(f32, shape, 1e-4);
-            det!(c64, shape, 1e-9);
-            det!(c32, shape, 1e-4);
-        }
-    }
+fn solve_random() {
+    let a: Array2<f64> = random((3, 3));
+    let x: Array1<f64> = random(3);
+    let b = a.dot(&x);
+    let y = a.solve_into(b).unwrap();
+    assert_close_l2!(&x, &y, 1e-7);
 }
 
 #[test]
-fn det_nonsquare() {
-    macro_rules! det_nonsquare {
-        ($elem:ty, $shape:expr) => {
-            let a: Array2<$elem> = random($shape);
-            assert!(a.factorize().unwrap().det().is_err());
-            assert!(a.factorize().unwrap().det_into().is_err());
-            assert!(a.det().is_err());
-            assert!(a.det_into().is_err());
-        }
-    }
-    for &dims in &[(1, 0), (1, 2), (2, 1), (2, 3)] {
-        for &shape in &[dims.clone().into_shape(), dims.clone().f()] {
-            det_nonsquare!(f64, shape);
-            det_nonsquare!(f32, shape);
-            det_nonsquare!(c64, shape);
-            det_nonsquare!(c32, shape);
-        }
-    }
+fn solve_random_t() {
+    let a: Array2<f64> = random((3, 3).f());
+    let x: Array1<f64> = random(3);
+    let b = a.dot(&x);
+    let y = a.solve_into(b).unwrap();
+    assert_close_l2!(&x, &y, 1e-7);
 }
diff --git a/tests/solveh.rs b/tests/solveh.rs
new file mode 100644
index 00000000..309ac32b
--- /dev/null
+++ b/tests/solveh.rs
@@ -0,0 +1,36 @@
+
+extern crate ndarray;
+#[macro_use]
+extern crate ndarray_linalg;
+extern crate num_traits;
+
+use ndarray::*;
+use ndarray_linalg::*;
+
+#[test]
+fn solveh_random() {
+    let a: Array2<f64> = random_hpd(3);
+    let x: Array1<f64> = random(3);
+    let b = a.dot(&x);
+    let y = a.solveh_into(b).unwrap();
+    assert_close_l2!(&x, &y, 1e-7);
+
+    let b = a.dot(&x);
+    let f = a.factorizeh_into().unwrap();
+    let y = f.solveh_into(b).unwrap();
+    assert_close_l2!(&x, &y, 1e-7);
+}
+
+#[test]
+fn solveh_random_t() {
+    let a: Array2<f64> = random_hpd(3).reversed_axes();
+    let x: Array1<f64> = random(3);
+    let b = a.dot(&x);
+    let y = a.solveh_into(b).unwrap();
+    assert_close_l2!(&x, &y, 1e-7);
+
+    let b = a.dot(&x);
+    let f = a.factorizeh_into().unwrap();
+    let y = f.solveh_into(b).unwrap();
+    assert_close_l2!(&x, &y, 1e-7);
+}