diff --git a/Eigen/src/SparseCholesky/SimplicialCholesky.h b/Eigen/src/SparseCholesky/SimplicialCholesky.h
index f3ce975cf..3ccbb037e 100644
--- a/Eigen/src/SparseCholesky/SimplicialCholesky.h
+++ b/Eigen/src/SparseCholesky/SimplicialCholesky.h
@@ -134,7 +134,7 @@ class SimplicialCholeskyBase : public SparseSolverBase<Derived> {
       << "\n";
     s << "  tree:     " << ((total += m_parent.size() * sizeof(int)) >> 20) << "Mb"
       << "\n";
-    s << "  nonzeros: " << ((total += m_nonZerosPerCol.size() * sizeof(int)) >> 20) << "Mb"
+    s << "  nonzeros: " << ((total += m_workSpace.size() * sizeof(int)) >> 20) << "Mb"
       << "\n";
     s << "  perm:     " << ((total += m_P.size() * sizeof(int)) >> 20) << "Mb"
       << "\n";
@@ -240,7 +240,7 @@ class SimplicialCholeskyBase : public SparseSolverBase<Derived> {
   CholMatrixType m_matrix;
   VectorType m_diag;  // the diagonal coefficients (LDLT mode)
   VectorI m_parent;   // elimination tree
-  VectorI m_nonZerosPerCol;
+  VectorI m_workSpace;
   PermutationMatrix<Dynamic, Dynamic, StorageIndex> m_P;     // the permutation
   PermutationMatrix<Dynamic, Dynamic, StorageIndex> m_Pinv;  // the inverse permutation
 
@@ -830,7 +830,7 @@ void SimplicialCholeskyBase<Derived>::ordering(const MatrixType& a, ConstCholMat
   const Index size = a.rows();
   pmat = &ap;
   // Note that ordering methods compute the inverse permutation
-  if (!internal::is_same<OrderingType, NaturalOrdering<Index> >::value) {
+  if (!internal::is_same<OrderingType, NaturalOrdering<StorageIndex> >::value) {
     {
       CholMatrixType C;
       internal::permute_symm_to_fullsymm<UpLo, NonHermitian>(a, C, NULL);
diff --git a/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h b/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
index 0b13c56b5..0288bd91c 100644
--- a/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
+++ b/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
@@ -25,40 +25,265 @@ the Mozilla Public License v. 2.0, as stated at the top of this file.
 
 namespace Eigen {
 
-template <typename Derived>
-void SimplicialCholeskyBase<Derived>::analyzePattern_preordered(const CholMatrixType& ap, bool doLDLT) {
-  const StorageIndex size = StorageIndex(ap.rows());
-  m_matrix.resize(size, size);
-  m_parent.resize(size);
-  m_nonZerosPerCol.resize(size);
+namespace internal {
 
-  ei_declare_aligned_stack_constructed_variable(StorageIndex, tags, size, 0);
+template <typename Scalar, typename StorageIndex>
+struct simpl_chol_helper {
+  using CholMatrixType = SparseMatrix<Scalar, ColMajor, StorageIndex>;
+  using InnerIterator = typename CholMatrixType::InnerIterator;
+  using VectorI = Matrix<StorageIndex, Dynamic, 1>;
+  static constexpr StorageIndex kEmpty = -1;
 
-  for (StorageIndex k = 0; k < size; ++k) {
-    /* L(k,:) pattern: all nodes reachable in etree from nz in A(0:k-1,k) */
-    m_parent[k] = -1;        /* parent of k is not yet known */
-    tags[k] = k;             /* mark node k as visited */
-    m_nonZerosPerCol[k] = 0; /* count of nonzeros in column k of L */
-    for (typename CholMatrixType::InnerIterator it(ap, k); it; ++it) {
-      StorageIndex i = it.index();
-      if (i < k) {
-        /* follow path from i to root of etree, stop at flagged node */
-        for (; tags[i] != k; i = m_parent[i]) {
-          /* find parent of i if not yet determined */
-          if (m_parent[i] == -1) m_parent[i] = k;
-          m_nonZerosPerCol[i]++; /* L (k,i) is nonzero */
-          tags[i] = k;           /* mark i as visited */
+  // Implementation of a stack or last-in first-out structure with some debugging machinery.
+  struct Stack {
+    StorageIndex* m_data;
+    Index m_size;
+#ifndef EIGEN_NO_DEBUG
+    const Index m_maxSize;
+    Stack(StorageIndex* data, StorageIndex size, StorageIndex maxSize)
+        : m_data(data), m_size(size), m_maxSize(maxSize) {
+      eigen_assert(size >= 0);
+      eigen_assert(maxSize >= size);
+    }
+#else
+    Stack(StorageIndex* data, StorageIndex size, StorageIndex /*maxSize*/) : m_data(data), m_size(size) {}
+#endif
+    bool empty() const { return m_size == 0; }
+    Index size() const { return m_size; }
+    StorageIndex back() const {
+      eigen_assert(m_size > 0);
+      return m_data[m_size - 1];
+    }
+    void push(const StorageIndex& value) {
+#ifndef EIGEN_NO_DEBUG
+      eigen_assert(m_size < m_maxSize);
+#endif
+      m_data[m_size] = value;
+      m_size++;
+    }
+    void pop() {
+      eigen_assert(m_size > 0);
+      m_size--;
+    }
+  };
+
+  // Implementation of a disjoint-set or union-find structure with path compression.
+  struct DisjointSet {
+    StorageIndex* m_set;
+    DisjointSet(StorageIndex* set, StorageIndex size) : m_set(set) { std::iota(set, set + size, 0); }
+    // Find the set representative or root of `u`.
+    StorageIndex find(StorageIndex u) const {
+      eigen_assert(u != kEmpty);
+      while (m_set[u] != u) {
+        // manually unroll the loop by a factor of 2 to improve performance
+        u = m_set[m_set[u]];
+      }
+      return u;
+    }
+    // Perform full path compression such that each node from `u` to `v` points to `v`.
+    void compress(StorageIndex u, StorageIndex v) {
+      eigen_assert(u != kEmpty);
+      eigen_assert(v != kEmpty);
+      while (m_set[u] != v) {
+        StorageIndex next = m_set[u];
+        m_set[u] = v;
+        u = next;
+      }
+    };
+  };
+
+  // Computes the higher adjacency pattern by transposing the input lower adjacency matrix.
+  // Only the index arrays are calculated, as the values are not needed for the symbolic factorization.
+  // The outer index array provides the size requirements of the inner index array.
+
+  // Computes the outer index array of the higher adjacency matrix.
+  static void calc_hadj_outer(const StorageIndex size, const CholMatrixType& ap, StorageIndex* outerIndex) {
+    for (StorageIndex j = 1; j < size; j++) {
+      for (InnerIterator it(ap, j); it; ++it) {
+        StorageIndex i = it.index();
+        if (i < j) outerIndex[i + 1]++;
+      }
+    }
+    std::partial_sum(outerIndex, outerIndex + size + 1, outerIndex);
+  }
+
+  // inner index array
+  static void calc_hadj_inner(const StorageIndex size, const CholMatrixType& ap, const StorageIndex* outerIndex,
+                              StorageIndex* innerIndex, StorageIndex* tmp) {
+    std::fill_n(tmp, size, 0);
+
+    for (StorageIndex j = 1; j < size; j++) {
+      for (InnerIterator it(ap, j); it; ++it) {
+        StorageIndex i = it.index();
+        if (i < j) {
+          StorageIndex b = outerIndex[i] + tmp[i];
+          innerIndex[b] = j;
+          tmp[i]++;
         }
       }
     }
   }
 
-  /* construct Lp index array from m_nonZerosPerCol column counts */
-  StorageIndex* Lp = m_matrix.outerIndexPtr();
-  Lp[0] = 0;
-  for (StorageIndex k = 0; k < size; ++k) Lp[k + 1] = Lp[k] + m_nonZerosPerCol[k] + (doLDLT ? 0 : 1);
+  // Adapted from:
+  // Joseph W. Liu. (1986).
+  // A compact row storage scheme for Cholesky factors using elimination trees.
+  // ACM Trans. Math. Softw. 12, 2 (June 1986), 127–148. https://doi.org/10.1145/6497.6499
 
-  m_matrix.resizeNonZeros(Lp[size]);
+  // Computes the elimination forest of the lower adjacency matrix, a compact representation of the sparse L factor.
+  // The L factor may contain multiple elimination trees if a column contains only its diagonal element.
+  // Each elimination tree is an n-ary tree in which each node points to its parent.
+  static void calc_etree(const StorageIndex size, const CholMatrixType& ap, StorageIndex* parent, StorageIndex* tmp) {
+    std::fill_n(parent, size, kEmpty);
+
+    DisjointSet ancestor(tmp, size);
+
+    for (StorageIndex j = 1; j < size; j++) {
+      for (InnerIterator it(ap, j); it; ++it) {
+        StorageIndex i = it.index();
+        if (i < j) {
+          StorageIndex r = ancestor.find(i);
+          if (r != j) parent[r] = j;
+          ancestor.compress(i, j);
+        }
+      }
+    }
+  }
+
+  // Computes the child pointers of the parent tree to facilitate a depth-first search traversal.
+  static void calc_lineage(const StorageIndex size, const StorageIndex* parent, StorageIndex* firstChild,
+                           StorageIndex* firstSibling) {
+    std::fill_n(firstChild, size, kEmpty);
+    std::fill_n(firstSibling, size, kEmpty);
+
+    for (StorageIndex j = 0; j < size; j++) {
+      StorageIndex p = parent[j];
+      if (p == kEmpty) continue;
+      StorageIndex c = firstChild[p];
+      if (c == kEmpty)
+        firstChild[p] = j;
+      else {
+        while (firstSibling[c] != kEmpty) c = firstSibling[c];
+        firstSibling[c] = j;
+      }
+    }
+  }
+
+  // Computes a post-ordered traversal of the elimination tree.
+  static void calc_post(const StorageIndex size, const StorageIndex* parent, StorageIndex* firstChild,
+                        const StorageIndex* firstSibling, StorageIndex* post, StorageIndex* dfs) {
+    Stack post_stack(post, 0, size);
+    for (StorageIndex j = 0; j < size; j++) {
+      if (parent[j] != kEmpty) continue;
+      // Begin at a root
+      Stack dfs_stack(dfs, 0, size);
+      dfs_stack.push(j);
+      while (!dfs_stack.empty()) {
+        StorageIndex i = dfs_stack.back();
+        StorageIndex c = firstChild[i];
+        if (c == kEmpty) {
+          post_stack.push(i);
+          dfs_stack.pop();
+        } else {
+          dfs_stack.push(c);
+          // Remove the path from `i` to `c` for future traversals.
+          firstChild[i] = firstSibling[c];
+        }
+      }
+    }
+    eigen_assert(post_stack.size() == size);
+    eigen_assert(std::all_of(firstChild, firstChild + size, [](StorageIndex a) { return a == kEmpty; }));
+  }
+
+  // Adapted from:
+  // Gilbert, J. R., Ng, E., & Peyton, B. W. (1994).
+  // An efficient algorithm to compute row and column counts for sparse Cholesky factorization.
+  // SIAM Journal on Matrix Analysis and Applications, 15(4), 1075–1091.
+
+  // Computes the non-zero pattern of the L factor.
+  static void calc_colcount(const StorageIndex size, const StorageIndex* hadjOuter, const StorageIndex* hadjInner,
+                            const StorageIndex* parent, StorageIndex* prevLeaf, StorageIndex* tmp,
+                            const StorageIndex* post, StorageIndex* nonZerosPerCol, bool doLDLT) {
+    // initialize nonZerosPerCol with 1 for leaves, 0 for non-leaves
+    std::fill_n(nonZerosPerCol, size, 1);
+    for (StorageIndex j = 0; j < size; j++) {
+      StorageIndex p = parent[j];
+      // p is not a leaf
+      if (p != kEmpty) nonZerosPerCol[p] = 0;
+    }
+
+    DisjointSet parentSet(tmp, size);
+    // prevLeaf is already initialized
+    eigen_assert(std::all_of(prevLeaf, prevLeaf + size, [](StorageIndex a) { return a == kEmpty; }));
+
+    for (StorageIndex j_ = 0; j_ < size; j_++) {
+      StorageIndex j = post[j_];
+      nonZerosPerCol[j] += hadjOuter[j + 1] - hadjOuter[j];
+      for (StorageIndex k = hadjOuter[j]; k < hadjOuter[j + 1]; k++) {
+        StorageIndex i = hadjInner[k];
+        eigen_assert(i > j);
+        StorageIndex prev = prevLeaf[i];
+        if (prev != kEmpty) {
+          StorageIndex q = parentSet.find(prev);
+          parentSet.compress(prev, q);
+          nonZerosPerCol[q]--;
+        }
+        prevLeaf[i] = j;
+      }
+      StorageIndex p = parent[j];
+      if (p != kEmpty) parentSet.compress(j, p);
+    }
+
+    for (StorageIndex j = 0; j < size; j++) {
+      StorageIndex p = parent[j];
+      if (p != kEmpty) nonZerosPerCol[p] += nonZerosPerCol[j] - 1;
+      if (doLDLT) nonZerosPerCol[j]--;
+    }
+  }
+
+  // Finalizes the non zero pattern of the L factor and allocates the memory for the factorization.
+  static void init_matrix(const StorageIndex size, const StorageIndex* nonZerosPerCol, CholMatrixType& L) {
+    eigen_assert(L.outerIndexPtr()[0] == 0);
+    std::partial_sum(nonZerosPerCol, nonZerosPerCol + size, L.outerIndexPtr() + 1);
+    L.resizeNonZeros(L.outerIndexPtr()[size]);
+  }
+
+  // Driver routine for the symbolic sparse Cholesky factorization.
+  static void run(const StorageIndex size, const CholMatrixType& ap, CholMatrixType& L, VectorI& parent,
+                  VectorI& workSpace, bool doLDLT) {
+    parent.resize(size);
+    workSpace.resize(4 * size);
+    L.resize(size, size);
+
+    StorageIndex* tmp1 = workSpace.data();
+    StorageIndex* tmp2 = workSpace.data() + size;
+    StorageIndex* tmp3 = workSpace.data() + 2 * size;
+    StorageIndex* tmp4 = workSpace.data() + 3 * size;
+
+    // Borrow L's outer index array for the higher adjacency pattern.
+    StorageIndex* hadj_outer = L.outerIndexPtr();
+    calc_hadj_outer(size, ap, hadj_outer);
+    // Request additional temporary storage for the inner indices of the higher adjacency pattern.
+    ei_declare_aligned_stack_constructed_variable(StorageIndex, hadj_inner, hadj_outer[size], nullptr);
+    calc_hadj_inner(size, ap, hadj_outer, hadj_inner, tmp1);
+
+    calc_etree(size, ap, parent.data(), tmp1);
+    calc_lineage(size, parent.data(), tmp1, tmp2);
+    calc_post(size, parent.data(), tmp1, tmp2, tmp3, tmp4);
+    calc_colcount(size, hadj_outer, hadj_inner, parent.data(), tmp1, tmp2, tmp3, tmp4, doLDLT);
+    init_matrix(size, tmp4, L);
+  }
+};
+
+}  // namespace internal
+
+template <typename Derived>
+void SimplicialCholeskyBase<Derived>::analyzePattern_preordered(const CholMatrixType& ap, bool doLDLT) {
+  using Helper = internal::simpl_chol_helper<Scalar, StorageIndex>;
+
+  eigen_assert(ap.innerSize() == ap.outerSize());
+  const StorageIndex size = internal::convert_index<StorageIndex>(ap.outerSize());
+
+  Helper::run(size, ap, m_matrix, m_parent, m_workSpace, doLDLT);
 
   m_isInitialized = true;
   m_info = Success;
@@ -70,20 +295,21 @@ template <typename Derived>
 template <bool DoLDLT, bool NonHermitian>
 void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType& ap) {
   using std::sqrt;
+  const StorageIndex size = StorageIndex(ap.rows());
 
   eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
   eigen_assert(ap.rows() == ap.cols());
-  eigen_assert(m_parent.size() == ap.rows());
-  eigen_assert(m_nonZerosPerCol.size() == ap.rows());
+  eigen_assert(m_parent.size() == size);
+  eigen_assert(m_workSpace.size() >= 3 * size);
 
-  const StorageIndex size = StorageIndex(ap.rows());
   const StorageIndex* Lp = m_matrix.outerIndexPtr();
   StorageIndex* Li = m_matrix.innerIndexPtr();
   Scalar* Lx = m_matrix.valuePtr();
 
   ei_declare_aligned_stack_constructed_variable(Scalar, y, size, 0);
-  ei_declare_aligned_stack_constructed_variable(StorageIndex, pattern, size, 0);
-  ei_declare_aligned_stack_constructed_variable(StorageIndex, tags, size, 0);
+  StorageIndex* nonZerosPerCol = m_workSpace.data();
+  StorageIndex* pattern = m_workSpace.data() + size;
+  StorageIndex* tags = m_workSpace.data() + 2 * size;
 
   bool ok = true;
   m_diag.resize(DoLDLT ? size : 0);
@@ -93,7 +319,7 @@ void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType&
     y[k] = Scalar(0);         // Y(0:k) is now all zero
     StorageIndex top = size;  // stack for pattern is empty
     tags[k] = k;              // mark node k as visited
-    m_nonZerosPerCol[k] = 0;  // count of nonzeros in column k of L
+    nonZerosPerCol[k] = 0;    // count of nonzeros in column k of L
     for (typename CholMatrixType::InnerIterator it(ap, k); it; ++it) {
       StorageIndex i = it.index();
       if (i <= k) {
@@ -124,13 +350,13 @@ void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType&
       else
         yi = l_ki = yi / Lx[Lp[i]];
 
-      Index p2 = Lp[i] + m_nonZerosPerCol[i];
+      Index p2 = Lp[i] + nonZerosPerCol[i];
       Index p;
       for (p = Lp[i] + (DoLDLT ? 0 : 1); p < p2; ++p) y[Li[p]] -= getSymm(Lx[p]) * yi;
       d -= getDiag(l_ki * getSymm(yi));
       Li[p] = k; /* store L(k,i) in column form of L */
       Lx[p] = l_ki;
-      ++m_nonZerosPerCol[i]; /* increment count of nonzeros in col i */
+      ++nonZerosPerCol[i]; /* increment count of nonzeros in col i */
     }
     if (DoLDLT) {
       m_diag[k] = d;
@@ -139,7 +365,7 @@ void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType&
         break;
       }
     } else {
-      Index p = Lp[k] + m_nonZerosPerCol[k]++;
+      Index p = Lp[k] + nonZerosPerCol[k]++;
       Li[p] = k; /* store L(k,k) = sqrt (d) in column k */
       if (NonHermitian ? d == RealScalar(0) : numext::real(d) <= RealScalar(0)) {
         ok = false; /* failure, matrix is not positive definite */