diff --git a/src/sparse/cs_matrix_cholesky.rs b/src/sparse/cs_matrix_cholesky.rs
index 2826b555..3b4185a8 100644
--- a/src/sparse/cs_matrix_cholesky.rs
+++ b/src/sparse/cs_matrix_cholesky.rs
@@ -21,7 +21,6 @@ where
     // equal to `original_i.len()` at the end.
     original_p: Vec<usize>,
     original_i: Vec<usize>,
-    original_len: usize, // Number of elements on the numerical value vector of the original matrix.
     // Decomposition result.
     l: CsMatrix<N, D, D>,
     // Used only for the pattern.
@@ -63,7 +62,6 @@ where
         CsCholesky {
             original_p,
             original_i: m.data.i.clone(),
-            original_len: m.data.i.len(),
             l,
             u,
             ok: false,
@@ -91,7 +89,7 @@ where
     // Performs the numerical Cholesky decomposition given the set of numerical values.
     pub fn decompose(&mut self, values: &[N]) -> bool {
         assert!(
-            values.len() >= self.original_len,
+            values.len() >= self.original_i.len(),
             "The set of values is too small."
         );
 
@@ -100,51 +98,64 @@ where
 
         // Perform the decomposition.
         for k in 0..self.l.nrows() {
-            // Scatter the k-th column of the original matrix with the values provided.
-            let column_range = self.original_p[k]..self.original_p[k + 1];
+            unsafe {
+                // Scatter the k-th column of the original matrix with the values provided.
+                let column_range =
+                    *self.original_p.get_unchecked(k)..*self.original_p.get_unchecked(k + 1);
 
-            self.work_x[k] = N::zero();
-            for p in column_range.clone() {
-                let irow = self.original_i[p];
+                *self.work_x.vget_unchecked_mut(k) = N::zero();
+                for p in column_range.clone() {
+                    let irow = *self.original_i.get_unchecked(p);
 
-                if irow <= k {
-                    self.work_x[irow] = values[p];
-                }
-            }
-
-            let mut diag = self.work_x[k];
-            self.work_x[k] = N::zero();
-
-            // Triangular solve.
-            for irow in self.u.data.column_row_indices(k) {
-                if irow >= k {
-                    continue;
+                    if irow <= k {
+                        *self.work_x.vget_unchecked_mut(irow) = *values.get_unchecked(p);
+                    }
                 }
 
-                let lki = self.work_x[irow] / self.l.data.vals[self.l.data.p[irow]];
-                self.work_x[irow] = N::zero();
+                let mut diag = *self.work_x.vget_unchecked(k);
+                *self.work_x.vget_unchecked_mut(k) = N::zero();
 
-                for p in self.l.data.p[irow] + 1..self.work_c[irow] {
-                    self.work_x[self.l.data.i[p]] -= self.l.data.vals[p] * lki;
+                // Triangular solve.
+                for irow in self.u.data.column_row_indices(k) {
+                    if irow >= k {
+                        continue;
+                    }
+
+                    let lki = *self.work_x.vget_unchecked(irow)
+                        / *self
+                            .l
+                            .data
+                            .vals
+                            .get_unchecked(*self.l.data.p.vget_unchecked(irow));
+                    *self.work_x.vget_unchecked_mut(irow) = N::zero();
+
+                    for p in
+                        *self.l.data.p.vget_unchecked(irow) + 1..*self.work_c.vget_unchecked(irow)
+                    {
+                        *self
+                            .work_x
+                            .vget_unchecked_mut(*self.l.data.i.get_unchecked(p)) -=
+                            *self.l.data.vals.get_unchecked(p) * lki;
+                    }
+
+                    diag -= lki * lki;
+                    let p = *self.work_c.vget_unchecked(irow);
+                    *self.work_c.vget_unchecked_mut(irow) += 1;
+                    *self.l.data.i.get_unchecked_mut(p) = k;
+                    *self.l.data.vals.get_unchecked_mut(p) = lki;
                 }
 
-                diag -= lki * lki;
-                let p = self.work_c[irow];
-                self.work_c[irow] += 1;
-                self.l.data.i[p] = k;
-                self.l.data.vals[p] = lki;
-            }
+                if diag <= N::zero() {
+                    self.ok = false;
+                    return false;
+                }
 
-            if diag <= N::zero() {
-                self.ok = false;
-                return false;
+                // Deal with the diagonal element.
+                let p = *self.work_c.vget_unchecked(k);
+                *self.work_c.vget_unchecked_mut(k) += 1;
+                *self.l.data.i.get_unchecked_mut(p) = k;
+                *self.l.data.vals.get_unchecked_mut(p) = diag.sqrt();
             }
-
-            // Deal with the diagonal element.
-            let p = self.work_c[k];
-            self.work_c[k] += 1;
-            self.l.data.i[p] = k;
-            self.l.data.vals[p] = diag.sqrt();
         }
 
         self.ok = true;