diff --git a/internal/ceres/cxsparse.h b/internal/ceres/cxsparse.h
index 1fed82f..5868401 100644
--- a/internal/ceres/cxsparse.h
+++ b/internal/ceres/cxsparse.h
@@ -38,7 +38,6 @@
 
 #include <vector>
 #include "cs.h"
-#include "ceres/internal/port.h"
 
 namespace ceres {
 namespace internal {
@@ -130,9 +129,13 @@
 
 #else  // CERES_NO_CXSPARSE
 
-class CXSparse {};
 typedef void cs_dis;
 
+class CXSparse {
+ public:
+  void Free(void*) {};
+
+};
 #endif  // CERES_NO_CXSPARSE
 
 #endif  // CERES_INTERNAL_CXSPARSE_H_
diff --git a/internal/ceres/reorder_program.cc b/internal/ceres/reorder_program.cc
index fa9e1d8..162bfb8 100644
--- a/internal/ceres/reorder_program.cc
+++ b/internal/ceres/reorder_program.cc
@@ -114,22 +114,22 @@
   LOG(FATAL) << "Congratulations, you found a Ceres bug! "
              << "Please report this error to the developers.";
 #else  // CERES_NO_CXSPARSE
-    // CXSparse works with J'J instead of J'. So compute the block
-    // sparsity for J'J and compute an approximate minimum degree
-    // ordering.
-    CXSparse cxsparse;
-    cs_di* block_jacobian_transpose;
-    block_jacobian_transpose =
-        cxsparse.CreateSparseMatrix(
+  // CXSparse works with J'J instead of J'. So compute the block
+  // sparsity for J'J and compute an approximate minimum degree
+  // ordering.
+  CXSparse cxsparse;
+  cs_di* block_jacobian_transpose;
+  block_jacobian_transpose =
+      cxsparse.CreateSparseMatrix(
             const_cast<TripletSparseMatrix*>(&tsm_block_jacobian_transpose));
-    cs_di* block_jacobian = cxsparse.TransposeMatrix(block_jacobian_transpose);
-    cs_di* block_hessian =
-        cxsparse.MatrixMatrixMultiply(block_jacobian_transpose, block_jacobian);
-    cxsparse.Free(block_jacobian);
-    cxsparse.Free(block_jacobian_transpose);
+  cs_di* block_jacobian = cxsparse.TransposeMatrix(block_jacobian_transpose);
+  cs_di* block_hessian =
+      cxsparse.MatrixMatrixMultiply(block_jacobian_transpose, block_jacobian);
+  cxsparse.Free(block_jacobian);
+  cxsparse.Free(block_jacobian_transpose);
 
-    cxsparse.ApproximateMinimumDegreeOrdering(block_hessian, ordering);
-    cxsparse.Free(block_hessian);
+  cxsparse.ApproximateMinimumDegreeOrdering(block_hessian, ordering);
+  cxsparse.Free(block_hessian);
 #endif  // CERES_NO_CXSPARSE
 }
 
@@ -378,11 +378,26 @@
     string* error) {
 
   if (sparse_linear_algebra_library_type != SUITE_SPARSE &&
-      sparse_linear_algebra_library_type != CX_SPARSE) {
+      sparse_linear_algebra_library_type != CX_SPARSE &&
+      sparse_linear_algebra_library_type != EIGEN_SPARSE) {
     *error = "Unknown sparse linear algebra library.";
     return false;
   }
 
+  // For Eigen, there is nothing to do. This is because Eigen in its
+  // current stable version does not expose a method for doing
+  // symbolic analysis on pre-ordered matrices, so a block
+  // pre-ordering is a bit pointless.
+  //
+  // The dev version as recently as July 20, 2014 has support for
+  // pre-ordering. Once this becomes more widespread, or we add
+  // support for detecting Eigen versions, we can add support for this
+  // along the lines of CXSparse.
+  if (sparse_linear_algebra_library_type == EIGEN_SPARSE) {
+    program->SetParameterOffsetsAndIndex();
+    return true;
+  }
+
   // Set the offsets and index for CreateJacobianSparsityTranspose.
   program->SetParameterOffsetsAndIndex();
   // Compute a block sparse presentation of J'.
diff --git a/internal/ceres/schur_complement_solver.cc b/internal/ceres/schur_complement_solver.cc
index d3eef5d..f295d60 100644
--- a/internal/ceres/schur_complement_solver.cc
+++ b/internal/ceres/schur_complement_solver.cc
@@ -1,5 +1,5 @@
 // Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
+// Copyright 2014 Google Inc. All rights reserved.
 // http://code.google.com/p/ceres-solver/
 //
 // Redistribution and use in source and binary forms, with or without
@@ -28,12 +28,13 @@
 //
 // Author: sameeragarwal@google.com (Sameer Agarwal)
 
+#include "ceres/internal/port.h"
+
 #include <algorithm>
 #include <ctime>
 #include <set>
 #include <vector>
 
-#include "Eigen/Dense"
 #include "ceres/block_random_access_dense_matrix.h"
 #include "ceres/block_random_access_matrix.h"
 #include "ceres/block_random_access_sparse_matrix.h"
@@ -42,7 +43,6 @@
 #include "ceres/cxsparse.h"
 #include "ceres/detect_structure.h"
 #include "ceres/internal/eigen.h"
-#include "ceres/internal/port.h"
 #include "ceres/internal/scoped_ptr.h"
 #include "ceres/lapack.h"
 #include "ceres/linear_solver.h"
@@ -51,6 +51,8 @@
 #include "ceres/triplet_sparse_matrix.h"
 #include "ceres/types.h"
 #include "ceres/wall_time.h"
+#include "Eigen/Dense"
+#include "Eigen/SparseCore"
 
 namespace ceres {
 namespace internal {
@@ -138,7 +140,8 @@
         .llt();
     if (llt.info() != Eigen::Success) {
       summary.termination_type = LINEAR_SOLVER_FAILURE;
-      summary.message = "Eigen LLT decomposition failed.";
+      summary.message =
+          "Eigen failure. Unable to perform dense Cholesky factorization.";
       return summary;
     }
 
@@ -155,8 +158,6 @@
   return summary;
 }
 
-#if !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARE)
-
 SparseSchurComplementSolver::SparseSchurComplementSolver(
     const LinearSolver::Options& options)
     : SchurComplementSolver(options),
@@ -165,19 +166,15 @@
 }
 
 SparseSchurComplementSolver::~SparseSchurComplementSolver() {
-#ifndef CERES_NO_SUITESPARSE
   if (factor_ != NULL) {
     ss_.Free(factor_);
     factor_ = NULL;
   }
-#endif  // CERES_NO_SUITESPARSE
 
-#ifndef CERES_NO_CXSPARSE
   if (cxsparse_factor_ != NULL) {
     cxsparse_.Free(cxsparse_factor_);
     cxsparse_factor_ = NULL;
   }
-#endif  // CERES_NO_CXSPARSE
 }
 
 // Determine the non-zero blocks in the Schur Complement matrix, and
@@ -258,6 +255,8 @@
       return SolveReducedLinearSystemUsingSuiteSparse(solution);
     case CX_SPARSE:
       return SolveReducedLinearSystemUsingCXSparse(solution);
+    case EIGEN_SPARSE:
+      return SolveReducedLinearSystemUsingEigen(solution);
     default:
       LOG(FATAL) << "Unknown sparse linear algebra library : "
                  << options().sparse_linear_algebra_library_type;
@@ -266,13 +265,23 @@
   return LinearSolver::Summary();
 }
 
-#ifndef CERES_NO_SUITESPARSE
 // Solve the system Sx = r, assuming that the matrix S is stored in a
 // BlockRandomAccessSparseMatrix.  The linear system is solved using
 // CHOLMOD's sparse cholesky factorization routines.
 LinearSolver::Summary
 SparseSchurComplementSolver::SolveReducedLinearSystemUsingSuiteSparse(
     double* solution) {
+#ifdef CERES_NO_SUITESPARSE
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 0;
+  summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+  summary.message = "Ceres was not built with SuiteSparse support."
+      "Therefore, SPARSE_SCHUR cannot be used with SUITE_SPARSE";
+  return summary;
+
+#else
+
   LinearSolver::Summary summary;
   summary.num_iterations = 0;
   summary.termination_type = LINEAR_SOLVER_SUCCESS;
@@ -326,6 +335,8 @@
   if (factor_ == NULL) {
     ss_.Free(cholmod_lhs);
     summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+    // No need to set message as it has already been set by the
+    // symbolic analysis routines above.
     return summary;
   }
 
@@ -335,6 +346,8 @@
   ss_.Free(cholmod_lhs);
 
   if (summary.termination_type != LINEAR_SOLVER_SUCCESS) {
+    // No need to set message as it has already been set by the
+    // numeric factorization routine above.
     return summary;
   }
 
@@ -346,6 +359,8 @@
   ss_.Free(cholmod_rhs);
 
   if (cholmod_solution == NULL) {
+    summary.message =
+        "SuiteSparse failure. Unable to perform triangular solve.";
     summary.termination_type = LINEAR_SOLVER_FAILURE;
     return summary;
   }
@@ -354,23 +369,26 @@
       = VectorRef(static_cast<double*>(cholmod_solution->x), num_rows);
   ss_.Free(cholmod_solution);
   return summary;
-}
-#else
-LinearSolver::Summary
-SparseSchurComplementSolver::SolveReducedLinearSystemUsingSuiteSparse(
-    double* solution) {
-  LOG(FATAL) << "No SuiteSparse support in Ceres.";
-  return LinearSolver::Summary();
-}
 #endif  // CERES_NO_SUITESPARSE
+}
 
-#ifndef CERES_NO_CXSPARSE
 // Solve the system Sx = r, assuming that the matrix S is stored in a
 // BlockRandomAccessSparseMatrix.  The linear system is solved using
 // CXSparse's sparse cholesky factorization routines.
 LinearSolver::Summary
 SparseSchurComplementSolver::SolveReducedLinearSystemUsingCXSparse(
     double* solution) {
+#ifdef CERES_NO_CXSPARSE
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 0;
+  summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+  summary.message = "Ceres was not built with CXSparse support."
+      "Therefore, SPARSE_SCHUR cannot be used with CX_SPARSE";
+  return summary;
+
+#else
+
   LinearSolver::Summary summary;
   summary.num_iterations = 0;
   summary.termination_type = LINEAR_SOLVER_SUCCESS;
@@ -407,16 +425,96 @@
 
   cxsparse_.Free(lhs);
   return summary;
-}
-#else
-LinearSolver::Summary
-SparseSchurComplementSolver::SolveReducedLinearSystemUsingCXSparse(
-    double* solution) {
-  LOG(FATAL) << "No CXSparse support in Ceres.";
-  return LinearSolver::Summary();
-}
 #endif  // CERES_NO_CXPARSE
+}
 
-#endif  // !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARE)
+// Solve the system Sx = r, assuming that the matrix S is stored in a
+// BlockRandomAccessSparseMatrix.  The linear system is solved using
+// Eigen's sparse cholesky factorization routines.
+LinearSolver::Summary
+SparseSchurComplementSolver::SolveReducedLinearSystemUsingEigen(
+    double* solution) {
+#ifndef CERES_USE_EIGEN_SPARSE
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 0;
+  summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+  summary.message =
+      "SPARSE_SCHUR cannot be used with EIGEN_SPARSE."
+      "Ceres was not built with support for"
+      "Eigen's SimplicialLDLT decomposition."
+      "This requires enabling building with -DEIGENSPARSE=ON.";
+  return summary;
+
+#else
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 0;
+  summary.termination_type = LINEAR_SOLVER_SUCCESS;
+  summary.message = "Success.";
+
+  // Extract the TripletSparseMatrix that is used for actually storing S.
+  TripletSparseMatrix* tsm =
+      const_cast<TripletSparseMatrix*>(
+          down_cast<const BlockRandomAccessSparseMatrix*>(lhs())->matrix());
+  const int num_rows = tsm->num_rows();
+
+  // The case where there are no f blocks, and the system is block
+  // diagonal.
+  if (num_rows == 0) {
+    return summary;
+  }
+
+  CompressedRowSparseMatrix crsm(*tsm);
+
+  // The crsm above is a symmetric matrix in upper triangular form, in
+  // compressed row form. When we map it to a compressed column matrix
+  // for Eigen, that turns it into a lower triangular matrix.
+  //
+  // TODO(sameeragarwal): What is the best memory layout for sparse
+  // cholesky factorization?
+  Eigen::MappedSparseMatrix<double, Eigen::ColMajor> eigen_lhs(
+      crsm.num_rows(),
+      crsm.num_rows(),
+      crsm.num_nonzeros(),
+      crsm.mutable_rows(),
+      crsm.mutable_cols(),
+      crsm.mutable_values());
+
+  typedef Eigen::SimplicialLDLT<
+    Eigen::SparseMatrix<double, Eigen::ColMajor>,
+    Eigen::Lower> SimplicialLDLT;
+
+  // Compute symbolic factorization if one does not exist.
+  if (simplicial_ldlt_.get() == NULL) {
+    simplicial_ldlt_.reset(new SimplicialLDLT);
+    simplicial_ldlt_->analyzePattern(eigen_lhs.selfadjointView<Eigen::Lower>());
+    if (simplicial_ldlt_->info() != Eigen::Success) {
+      summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+      summary.message =
+          "Eigen failure. Unable to find symbolic factorization.";
+      return summary;
+    }
+  }
+
+  simplicial_ldlt_->factorize(eigen_lhs.selfadjointView<Eigen::Lower>());
+  if (simplicial_ldlt_->info() != Eigen::Success) {
+    summary.termination_type = LINEAR_SOLVER_FAILURE;
+    summary.message = "Eigen failure. Unable to find numeric factoriztion.";
+    return summary;
+  }
+
+  VectorRef(solution, num_rows) =
+      simplicial_ldlt_->solve(ConstVectorRef(rhs(), num_rows));
+
+  if (simplicial_ldlt_->info() != Eigen::Success) {
+    summary.termination_type = LINEAR_SOLVER_FAILURE;
+    summary.message = "Eigen failure. Unable to do triangular solve.";
+  }
+
+  return summary;
+#endif  // CERES_USE_EIGEN_SPARSE
+}
+
 }  // namespace internal
 }  // namespace ceres
diff --git a/internal/ceres/schur_complement_solver.h b/internal/ceres/schur_complement_solver.h
index a997851..e911bfb 100644
--- a/internal/ceres/schur_complement_solver.h
+++ b/internal/ceres/schur_complement_solver.h
@@ -35,6 +35,8 @@
 #include <utility>
 #include <vector>
 
+#include "ceres/internal/port.h"
+
 #include "ceres/block_random_access_matrix.h"
 #include "ceres/block_sparse_matrix.h"
 #include "ceres/block_structure.h"
@@ -45,6 +47,10 @@
 #include "ceres/internal/scoped_ptr.h"
 #include "ceres/types.h"
 
+#ifdef CERES_USE_EIGEN_SPARSE
+#include "Eigen/SparseCholesky"
+#endif
+
 namespace ceres {
 namespace internal {
 
@@ -153,7 +159,6 @@
   CERES_DISALLOW_COPY_AND_ASSIGN(DenseSchurComplementSolver);
 };
 
-#if !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARE)
 // Sparse Cholesky factorization based solver.
 class SparseSchurComplementSolver : public SchurComplementSolver {
  public:
@@ -168,6 +173,8 @@
       double* solution);
   LinearSolver::Summary SolveReducedLinearSystemUsingCXSparse(
       double* solution);
+  LinearSolver::Summary SolveReducedLinearSystemUsingEigen(
+      double* solution);
 
   // Size of the blocks in the Schur complement.
   vector<int> blocks_;
@@ -180,10 +187,16 @@
   CXSparse cxsparse_;
   // Cached factorization
   cs_dis* cxsparse_factor_;
+
+#ifdef CERES_USE_EIGEN_SPARSE
+  scoped_ptr<Eigen::SimplicialLDLT<
+               Eigen::SparseMatrix<double, Eigen::ColMajor>,
+               Eigen::Lower> > simplicial_ldlt_;
+#endif
+
   CERES_DISALLOW_COPY_AND_ASSIGN(SparseSchurComplementSolver);
 };
 
-#endif  // !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARE)
 }  // namespace internal
 }  // namespace ceres
 
diff --git a/internal/ceres/schur_complement_solver_test.cc b/internal/ceres/schur_complement_solver_test.cc
index d91c162..8e71b2e 100644
--- a/internal/ceres/schur_complement_solver_test.cc
+++ b/internal/ceres/schur_complement_solver_test.cc
@@ -187,17 +187,31 @@
 
 #ifndef CERES_NO_CXSPARSE
 TEST_F(SchurComplementSolverTest,
-       SparseSchurWithSuiteSparseSmallProblem) {
+       SparseSchurWithCXSparseSmallProblem) {
   ComputeAndCompareSolutions(2, false, SPARSE_SCHUR, EIGEN, CX_SPARSE, true);
   ComputeAndCompareSolutions(2, true, SPARSE_SCHUR, EIGEN, CX_SPARSE, true);
 }
 
 TEST_F(SchurComplementSolverTest,
-       SparseSchurWithSuiteSparseLargeProblem) {
+       SparseSchurWithCXSparseLargeProblem) {
   ComputeAndCompareSolutions(3, false, SPARSE_SCHUR, EIGEN, CX_SPARSE, true);
   ComputeAndCompareSolutions(3, true, SPARSE_SCHUR, EIGEN, CX_SPARSE, true);
 }
 #endif  // CERES_NO_CXSPARSE
 
+#ifdef CERES_USE_EIGEN_SPARSE
+TEST_F(SchurComplementSolverTest,
+       SparseSchurWithEigenSparseSmallProblem) {
+  ComputeAndCompareSolutions(2, false, SPARSE_SCHUR, EIGEN, EIGEN_SPARSE, true);
+  ComputeAndCompareSolutions(2, true, SPARSE_SCHUR, EIGEN, EIGEN_SPARSE, true);
+}
+
+TEST_F(SchurComplementSolverTest,
+       SparseSchurWithEigenSparseLargeProblem) {
+  ComputeAndCompareSolutions(3, false, SPARSE_SCHUR, EIGEN, EIGEN_SPARSE, true);
+  ComputeAndCompareSolutions(3, true, SPARSE_SCHUR, EIGEN, EIGEN_SPARSE, true);
+}
+#endif  // CERES_USE_EIGEN_SPARSE
+
 }  // namespace internal
 }  // namespace ceres
diff --git a/internal/ceres/solver_impl.cc b/internal/ceres/solver_impl.cc
index a4edf08..a1cf4ca 100644
--- a/internal/ceres/solver_impl.cc
+++ b/internal/ceres/solver_impl.cc
@@ -826,14 +826,6 @@
   }
 #endif
 
-#if defined(CERES_NO_SUITESPARSE) && defined(CERES_NO_CXSPARSE)
-  if (options->linear_solver_type == SPARSE_SCHUR) {
-    *error = "Can't use SPARSE_SCHUR because neither SuiteSparse nor"
-        "CXSparse was enabled when Ceres was compiled.";
-    return NULL;
-  }
-#endif
-
   if (options->max_linear_solver_iterations <= 0) {
     *error = "Solver::Options::max_linear_solver_iterations is not positive.";
     return NULL;
diff --git a/internal/ceres/sparse_normal_cholesky_solver.cc b/internal/ceres/sparse_normal_cholesky_solver.cc
index cf5bb23..4277480 100644
--- a/internal/ceres/sparse_normal_cholesky_solver.cc
+++ b/internal/ceres/sparse_normal_cholesky_solver.cc
@@ -31,8 +31,6 @@
 // This include must come before any #ifndef check on Ceres compile options.
 #include "ceres/internal/port.h"
 
-#if !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARSE)
-
 #include "ceres/sparse_normal_cholesky_solver.h"
 
 #include <algorithm>
@@ -48,6 +46,8 @@
 #include "ceres/triplet_sparse_matrix.h"
 #include "ceres/types.h"
 #include "ceres/wall_time.h"
+#include "Eigen/SparseCore"
+
 
 namespace ceres {
 namespace internal {
@@ -56,23 +56,19 @@
     const LinearSolver::Options& options)
     : factor_(NULL),
       cxsparse_factor_(NULL),
-      options_(options) {
+      options_(options){
 }
 
 void SparseNormalCholeskySolver::FreeFactorization() {
-#ifndef CERES_NO_SUITESPARSE
   if (factor_ != NULL) {
     ss_.Free(factor_);
     factor_ = NULL;
   }
-#endif  // CERES_NO_SUITESPARSE
 
-#ifndef CERES_NO_CXSPARSE
   if (cxsparse_factor_ != NULL) {
     cxsparse_.Free(cxsparse_factor_);
     cxsparse_factor_ = NULL;
   }
-#endif  // CERES_NO_CXSPARSE
 }
 
 SparseNormalCholeskySolver::~SparseNormalCholeskySolver() {
@@ -111,6 +107,9 @@
     case CX_SPARSE:
       summary = SolveImplUsingCXSparse(A, per_solve_options, x);
       break;
+    case EIGEN_SPARSE:
+      summary = SolveImplUsingEigen(A, per_solve_options, x);
+      break;
     default:
       LOG(FATAL) << "Unknown sparse linear algebra library : "
                  << options_.sparse_linear_algebra_library_type;
@@ -123,11 +122,129 @@
   return summary;
 }
 
-#ifndef CERES_NO_CXSPARSE
+LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingEigen(
+    CompressedRowSparseMatrix* A,
+    const LinearSolver::PerSolveOptions& per_solve_options,
+    double * rhs_and_solution) {
+#ifndef CERES_USE_EIGEN_SPARSE
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 0;
+  summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+   summary.message =
+      "SPARSE_NORMAL_CHOLESKY cannot be used with EIGEN_SPARSE"
+      "because Ceres was not built with support for"
+      "Eigen's SimplicialLDLT decomposition."
+      "This requires enabling building with -DEIGENSPARSE=ON.";
+  return summary;
+
+#else
+
+  EventLogger event_logger("SparseNormalCholeskySolver::Eigen::Solve");
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 1;
+  summary.termination_type = LINEAR_SOLVER_SUCCESS;
+  summary.message = "Success.";
+
+  // Compute the normal equations. J'J delta = J'f and solve them
+  // using a sparse Cholesky factorization. Notice that when compared
+  // to SuiteSparse we have to explicitly compute the normal equations
+  // before they can be factorized. CHOLMOD/SuiteSparse on the other
+  // hand can just work off of Jt to compute the Cholesky
+  // factorization of the normal equations.
+  //
+  // TODO(sameeragarwal): See note about how this maybe a bad idea for
+  // dynamic sparsity.
+  if (outer_product_.get() == NULL) {
+    outer_product_.reset(
+        CompressedRowSparseMatrix::CreateOuterProductMatrixAndProgram(
+            *A, &pattern_));
+  }
+
+  CompressedRowSparseMatrix::ComputeOuterProduct(
+      *A, pattern_, outer_product_.get());
+
+  // Map to an upper triangular column major matrix.
+  //
+  // outer_product_ is a compressed row sparse matrix and in lower
+  // triangular form, when mapped to a compressed column sparse
+  // matrix, it becomes an upper triangular matrix.
+  //
+  // TODO(sameeragarwal): It is not clear to me if an upper triangular
+  // column major matrix is the way to go here, or if a lower
+  // triangular matrix is better. This will require some testing. If
+  // it turns out that the lower triangular is better, then the logic
+  // used to compute the outer product needs to be updated.
+  Eigen::MappedSparseMatrix<double, Eigen::ColMajor> AtA(
+      outer_product_->num_rows(),
+      outer_product_->num_rows(),
+      outer_product_->num_nonzeros(),
+      outer_product_->mutable_rows(),
+      outer_product_->mutable_cols(),
+      outer_product_->mutable_values());
+
+  const Vector b = VectorRef(rhs_and_solution, outer_product_->num_rows());
+  if (simplicial_ldlt_.get() == NULL || options_.dynamic_sparsity) {
+    typedef Eigen::SimplicialLDLT<Eigen::SparseMatrix<double, Eigen::ColMajor>,
+                                  Eigen::Upper> SimplicialLDLT;
+    simplicial_ldlt_.reset(new SimplicialLDLT);
+    // This is a crappy way to be doing this. But right now Eigen does
+    // not expose a way to do symbolic analysis with a given
+    // permutation pattern, so we cannot use a block analysis of the
+    // Jacobian.
+    simplicial_ldlt_->analyzePattern(AtA.selfadjointView<Eigen::Upper>());
+    if (simplicial_ldlt_->info() != Eigen::Success) {
+      summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+      summary.message =
+          "Eigen failure. Unable to find symbolic factorization.";
+      return summary;
+    }
+  }
+  event_logger.AddEvent("Analysis");
+
+  simplicial_ldlt_->factorize(AtA.selfadjointView<Eigen::Upper>());
+  if(simplicial_ldlt_->info() != Eigen::Success) {
+    summary.termination_type = LINEAR_SOLVER_FAILURE;
+    summary.message =
+        "Eigen failure. Unable to find numeric factorization.";
+    return summary;
+  }
+
+  VectorRef(rhs_and_solution, outer_product_->num_rows()) =
+      simplicial_ldlt_->solve(b);
+  if(simplicial_ldlt_->info() != Eigen::Success) {
+    summary.termination_type = LINEAR_SOLVER_FAILURE;
+    summary.message =
+        "Eigen failure. Unable to do triangular solve.";
+    return summary;
+  }
+
+  event_logger.AddEvent("Solve");
+  return summary;
+#endif  // EIGEN_USE_EIGEN_SPARSE
+}
+
+
+
 LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingCXSparse(
     CompressedRowSparseMatrix* A,
     const LinearSolver::PerSolveOptions& per_solve_options,
     double * rhs_and_solution) {
+#ifdef CERES_NO_CXSPARSE
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 0;
+  summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+  summary.message =
+      "SPARSE_NORMAL_CHOLESKY cannot be used with CX_SPARSE"
+      "because Ceres was not built with support for CXSparse."
+      "This requires enabling building with -DCXSPARSE=ON.";
+
+  return summary;
+
+#else
+
   EventLogger event_logger("SparseNormalCholeskySolver::CXSparse::Solve");
 
   LinearSolver::Summary summary;
@@ -137,11 +254,14 @@
 
   // Compute the normal equations. J'J delta = J'f and solve them
   // using a sparse Cholesky factorization. Notice that when compared
-  // to SuiteSparse we have to explicitly compute the transpose of Jt,
-  // and then the normal equations before they can be
-  // factorized. CHOLMOD/SuiteSparse on the other hand can just work
-  // off of Jt to compute the Cholesky factorization of the normal
-  // equations.
+  // to SuiteSparse we have to explicitly compute the normal equations
+  // before they can be factorized. CHOLMOD/SuiteSparse on the other
+  // hand can just work off of Jt to compute the Cholesky
+  // factorization of the normal equations.
+  //
+  // TODO(sameeragarwal): If dynamic sparsity is enabled, then this is
+  // not a good idea performance wise, since the jacobian has far too
+  // many entries and the program will go crazy with memory.
   if (outer_product_.get() == NULL) {
     outer_product_.reset(
         CompressedRowSparseMatrix::CreateOuterProductMatrixAndProgram(
@@ -181,28 +301,31 @@
         "CXSparse failure. Unable to find symbolic factorization.";
   } else if (!cxsparse_.SolveCholesky(AtA, cxsparse_factor_, rhs_and_solution)) {
     summary.termination_type = LINEAR_SOLVER_FAILURE;
+    summary.message = "CXSparse::SolveCholesky failed.";
   }
   event_logger.AddEvent("Solve");
 
   return summary;
-}
-#else
-LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingCXSparse(
-    CompressedRowSparseMatrix* A,
-    const LinearSolver::PerSolveOptions& per_solve_options,
-    double * rhs_and_solution) {
-  LOG(FATAL) << "No CXSparse support in Ceres.";
-
-  // Unreachable but MSVC does not know this.
-  return LinearSolver::Summary();
-}
 #endif
+}
 
-#ifndef CERES_NO_SUITESPARSE
 LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
     CompressedRowSparseMatrix* A,
     const LinearSolver::PerSolveOptions& per_solve_options,
     double * rhs_and_solution) {
+#ifdef CERES_NO_SUITESPARSE
+
+  LinearSolver::Summary summary;
+  summary.num_iterations = 0;
+  summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+  summary.message =
+      "SPARSE_NORMAL_CHOLESKY cannot be used with SUITE_SPARSE"
+      "because Ceres was not built with support for SuiteSparse."
+      "This requires enabling building with -DSUITESPARSE=ON.";
+  return summary;
+
+#else
+
   EventLogger event_logger("SparseNormalCholeskySolver::SuiteSparse::Solve");
   LinearSolver::Summary summary;
   summary.termination_type = LINEAR_SOLVER_SUCCESS;
@@ -234,6 +357,8 @@
 
   if (factor_ == NULL) {
     summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
+    // No need to set message as it has already been set by the
+    // symbolic analysis routines above.
     return summary;
   }
 
@@ -251,25 +376,15 @@
     memcpy(rhs_and_solution, solution->x, num_cols * sizeof(*rhs_and_solution));
     ss_.Free(solution);
   } else {
+    // No need to set message as it has already been set by the
+    // numeric factorization routine above.
     summary.termination_type = LINEAR_SOLVER_FAILURE;
   }
 
   event_logger.AddEvent("Teardown");
   return summary;
-}
-#else
-LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
-    CompressedRowSparseMatrix* A,
-    const LinearSolver::PerSolveOptions& per_solve_options,
-    double * rhs_and_solution) {
-  LOG(FATAL) << "No SuiteSparse support in Ceres.";
-
-  // Unreachable but MSVC does not know this.
-  return LinearSolver::Summary();
-}
 #endif
+}
 
 }   // namespace internal
 }   // namespace ceres
-
-#endif  // !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARSE)
diff --git a/internal/ceres/sparse_normal_cholesky_solver.h b/internal/ceres/sparse_normal_cholesky_solver.h
index ed777a1..0e6a988 100644
--- a/internal/ceres/sparse_normal_cholesky_solver.h
+++ b/internal/ceres/sparse_normal_cholesky_solver.h
@@ -37,12 +37,14 @@
 // This include must come before any #ifndef check on Ceres compile options.
 #include "ceres/internal/port.h"
 
-#if !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARSE)
-
-#include "ceres/cxsparse.h"
 #include "ceres/internal/macros.h"
 #include "ceres/linear_solver.h"
 #include "ceres/suitesparse.h"
+#include "ceres/cxsparse.h"
+
+#ifdef CERES_USE_EIGEN_SPARSE
+#include "Eigen/SparseCholesky"
+#endif
 
 namespace ceres {
 namespace internal {
@@ -74,6 +76,12 @@
       const LinearSolver::PerSolveOptions& options,
       double* rhs_and_solution);
 
+  // Crashes if CERES_USE_LGPGL_CODE is not defined.
+  LinearSolver::Summary SolveImplUsingEigen(
+      CompressedRowSparseMatrix* A,
+      const LinearSolver::PerSolveOptions& options,
+      double* rhs_and_solution);
+
   void FreeFactorization();
 
   SuiteSparse ss_;
@@ -83,6 +91,13 @@
   CXSparse cxsparse_;
   // Cached factorization
   cs_dis* cxsparse_factor_;
+
+#ifdef CERES_USE_EIGEN_SPARSE
+  scoped_ptr<Eigen::SimplicialLDLT<
+               Eigen::SparseMatrix<double, Eigen::ColMajor>,
+               Eigen::Upper> > simplicial_ldlt_;
+#endif
+
   scoped_ptr<CompressedRowSparseMatrix> outer_product_;
   vector<int> pattern_;
   const LinearSolver::Options options_;
@@ -92,5 +107,4 @@
 }  // namespace internal
 }  // namespace ceres
 
-#endif  // !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARSE)
 #endif  // CERES_INTERNAL_SPARSE_NORMAL_CHOLESKY_SOLVER_H_
diff --git a/internal/ceres/suitesparse.h b/internal/ceres/suitesparse.h
index f27e338..baab899 100644
--- a/internal/ceres/suitesparse.h
+++ b/internal/ceres/suitesparse.h
@@ -286,6 +286,7 @@
 typedef void cholmod_factor;
 
 class SuiteSparse {
+ public:
   // Defining this static function even when SuiteSparse is not
   // available, allows client code to check for the presence of CAMD
   // without checking for the absence of the CERES_NO_CAMD symbol.
@@ -296,6 +297,8 @@
   static bool IsConstrainedApproximateMinimumDegreeOrderingAvailable() {
     return false;
   }
+
+  void Free(void*) {};
 };
 
 #endif  // CERES_NO_SUITESPARSE
diff --git a/internal/ceres/system_test.cc b/internal/ceres/system_test.cc
index f4b9ff3..f70b79a 100644
--- a/internal/ceres/system_test.cc
+++ b/internal/ceres/system_test.cc
@@ -43,6 +43,8 @@
 #include <cstdlib>
 #include <string>
 
+#include "ceres/internal/port.h"
+
 #include "ceres/autodiff_cost_function.h"
 #include "ceres/ordered_groups.h"
 #include "ceres/problem.h"
@@ -493,40 +495,45 @@
                                  ordering,                              \
                                  preconditioner))
 
+  CONFIGURE(DENSE_SCHUR,            SUITE_SPARSE, kAutomaticOrdering, IDENTITY);
+  CONFIGURE(DENSE_SCHUR,            SUITE_SPARSE, kUserOrdering,      IDENTITY);
+
+  CONFIGURE(CGNR,                   SUITE_SPARSE, kAutomaticOrdering, JACOBI);
+
+  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kUserOrdering,      JACOBI);
+  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kAutomaticOrdering, JACOBI);
+
+  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kUserOrdering,      SCHUR_JACOBI);
+  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kAutomaticOrdering, SCHUR_JACOBI);
+
 #ifndef CERES_NO_SUITESPARSE
   CONFIGURE(SPARSE_NORMAL_CHOLESKY, SUITE_SPARSE, kAutomaticOrdering, IDENTITY);
   CONFIGURE(SPARSE_NORMAL_CHOLESKY, SUITE_SPARSE, kUserOrdering,      IDENTITY);
 
   CONFIGURE(SPARSE_SCHUR,           SUITE_SPARSE, kAutomaticOrdering, IDENTITY);
   CONFIGURE(SPARSE_SCHUR,           SUITE_SPARSE, kUserOrdering,      IDENTITY);
+
+  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kAutomaticOrdering, CLUSTER_JACOBI);
+  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kUserOrdering,      CLUSTER_JACOBI);
+
+  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kAutomaticOrdering, CLUSTER_TRIDIAGONAL);
+  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kUserOrdering,      CLUSTER_TRIDIAGONAL);
 #endif  // CERES_NO_SUITESPARSE
 
 #ifndef CERES_NO_CXSPARSE
+  CONFIGURE(SPARSE_NORMAL_CHOLESKY, CX_SPARSE, kAutomaticOrdering, IDENTITY);
+  CONFIGURE(SPARSE_NORMAL_CHOLESKY, CX_SPARSE, kUserOrdering,      IDENTITY);
+
   CONFIGURE(SPARSE_SCHUR,           CX_SPARSE,    kAutomaticOrdering, IDENTITY);
   CONFIGURE(SPARSE_SCHUR,           CX_SPARSE,    kUserOrdering,      IDENTITY);
 #endif  // CERES_NO_CXSPARSE
 
-  CONFIGURE(DENSE_SCHUR,            SUITE_SPARSE, kAutomaticOrdering, IDENTITY);
-  CONFIGURE(DENSE_SCHUR,            SUITE_SPARSE, kUserOrdering,      IDENTITY);
-
-  CONFIGURE(CGNR,                   SUITE_SPARSE, kAutomaticOrdering, JACOBI);
-  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kUserOrdering,      JACOBI);
-  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kUserOrdering,      SCHUR_JACOBI);
-
-#ifndef CERES_NO_SUITESPARSE
-
-  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kUserOrdering,      CLUSTER_JACOBI);
-  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kUserOrdering,      CLUSTER_TRIDIAGONAL);
-#endif  // CERES_NO_SUITESPARSE
-
-  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kAutomaticOrdering, JACOBI);
-  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kAutomaticOrdering, SCHUR_JACOBI);
-
-#ifndef CERES_NO_SUITESPARSE
-
-  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kAutomaticOrdering, CLUSTER_JACOBI);
-  CONFIGURE(ITERATIVE_SCHUR,        SUITE_SPARSE, kAutomaticOrdering, CLUSTER_TRIDIAGONAL);
-#endif  // CERES_NO_SUITESPARSE
+#ifdef CERES_USE_EIGEN_SPARSE
+  CONFIGURE(SPARSE_SCHUR,           EIGEN_SPARSE,    kAutomaticOrdering, IDENTITY);
+  CONFIGURE(SPARSE_SCHUR,           EIGEN_SPARSE,    kUserOrdering,      IDENTITY);
+  CONFIGURE(SPARSE_NORMAL_CHOLESKY, EIGEN_SPARSE,    kAutomaticOrdering, IDENTITY);
+  CONFIGURE(SPARSE_NORMAL_CHOLESKY, EIGEN_SPARSE,    kUserOrdering,      IDENTITY);
+#endif  // CERES_USE_EIGEN_SPARSE
 
 #undef CONFIGURE
 
diff --git a/internal/ceres/types.cc b/internal/ceres/types.cc
index adf1090..4710261 100644
--- a/internal/ceres/types.cc
+++ b/internal/ceres/types.cc
@@ -96,6 +96,7 @@
   switch (type) {
     CASESTR(SUITE_SPARSE);
     CASESTR(CX_SPARSE);
+    CASESTR(EIGEN_SPARSE);
     default:
       return "UNKNOWN";
   }
@@ -107,6 +108,7 @@
   UpperCase(&value);
   STRENUM(SUITE_SPARSE);
   STRENUM(CX_SPARSE);
+  STRENUM(EIGEN_SPARSE);
   return false;
 }
 
diff --git a/internal/ceres/unsymmetric_linear_solver_test.cc b/internal/ceres/unsymmetric_linear_solver_test.cc
index 412e611..0b82e6a 100644
--- a/internal/ceres/unsymmetric_linear_solver_test.cc
+++ b/internal/ceres/unsymmetric_linear_solver_test.cc
@@ -107,13 +107,19 @@
               LINEAR_SOLVER_SUCCESS);
 
     for (int i = 0; i < A_->num_cols(); ++i) {
-      EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8);
+      EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8)
+          << "\nExpected: "
+          << ConstVectorRef(sol_unregularized_.get(), A_->num_cols()).transpose()
+          << "\nActual: " << x_unregularized.transpose();
     }
 
     EXPECT_EQ(regularized_solve_summary.termination_type,
               LINEAR_SOLVER_SUCCESS);
     for (int i = 0; i < A_->num_cols(); ++i) {
-      EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8);
+      EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8)
+          << "\nExpected: "
+          << ConstVectorRef(sol_regularized_.get(), A_->num_cols()).transpose()
+          << "\nActual: " << x_regularized.transpose();
     }
   }
 
@@ -212,5 +218,35 @@
 }
 #endif
 
+#ifdef CERES_USE_EIGEN_SPARSE
+TEST_F(UnsymmetricLinearSolverTest,
+       SparseNormalCholeskyUsingEigenPreOrdering) {
+  LinearSolver::Options options;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  options.type = SPARSE_NORMAL_CHOLESKY;
+  options.use_postordering = false;
+  TestSolver(options);
+}
+
+TEST_F(UnsymmetricLinearSolverTest,
+       SparseNormalCholeskyUsingEigenPostOrdering) {
+  LinearSolver::Options options;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  options.type = SPARSE_NORMAL_CHOLESKY;
+  options.use_postordering = true;
+  TestSolver(options);
+}
+
+TEST_F(UnsymmetricLinearSolverTest,
+       SparseNormalCholeskyUsingEigenDynamicSparsity) {
+  LinearSolver::Options options;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  options.type = SPARSE_NORMAL_CHOLESKY;
+  options.dynamic_sparsity = true;
+  TestSolver(options);
+}
+
+#endif  // CERES_USE_EIGEN_SPARSE
+
 }  // namespace internal
 }  // namespace ceres