diff --git a/gtsam/slam/JacobianSchurFactor.h b/gtsam/slam/JacobianSchurFactor.h
deleted file mode 100644
index 6a218a9a7..000000000
--- a/gtsam/slam/JacobianSchurFactor.h
+++ /dev/null
@@ -1,103 +0,0 @@
-/* ----------------------------------------------------------------------------
-
- * GTSAM Copyright 2010, Georgia Tech Research Corporation,
- * Atlanta, Georgia 30332-0415
- * All Rights Reserved
- * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
-
- * See LICENSE for the license information
-
- * -------------------------------------------------------------------------- */
-
-/*
- * @file  JacobianSchurFactor.h
- * @brief Jacobianfactor that combines and eliminates points
- * @date  Oct 27, 2013
- * @uthor Frank Dellaert
- */
-
-#pragma once
-
-#include <gtsam/linear/JacobianFactor.h>
-#include <boost/foreach.hpp>
-
-namespace gtsam {
-/**
- * JacobianFactor for Schur complement
- * Is base class for JacobianQFactor, JacobianFactorQR, and JacobianFactorSVD
- * Provides raw memory access versions of linear operator.
- */
-template<size_t D>
-class JacobianSchurFactor: public JacobianFactor {
-
-public:
-
-  // Use eigen magic to access raw memory
-  typedef Eigen::Matrix<double, D, 1> DVector;
-  typedef Eigen::Map<DVector> DMap;
-  typedef Eigen::Map<const DVector> ConstDMap;
-
-  /**
-   * @brief double* Matrix-vector multiply, i.e. y = A*x
-   * RAW memory access! Assumes keys start at 0 and go to M-1, and x is laid out that way
-   */
-  Vector operator*(const double* x) const {
-    Vector Ax = zero(Ab_.rows());
-    if (empty())
-      return Ax;
-
-    // Just iterate over all A matrices and multiply in correct config part
-    for (size_t pos = 0; pos < size(); ++pos)
-      Ax += Ab_(pos) * ConstDMap(x + D * keys_[pos]);
-
-    return model_ ? model_->whiten(Ax) : Ax;
-  }
-
-  /**
-   * @brief double* Transpose Matrix-vector multiply, i.e. x += A'*e
-   * RAW memory access! Assumes keys start at 0 and go to M-1, and y is laid out that way
-   */
-  void transposeMultiplyAdd(double alpha, const Vector& e, double* x) const {
-    Vector E = alpha * (model_ ? model_->whiten(e) : e);
-    // Just iterate over all A matrices and insert Ai^e into y
-    for (size_t pos = 0; pos < size(); ++pos)
-      DMap(x + D * keys_[pos]) += Ab_(pos).transpose() * E;
-  }
-
-  /** y += alpha * A'*A*x */
-  void multiplyHessianAdd(double alpha, const VectorValues& x,
-      VectorValues& y) const {
-    JacobianFactor::multiplyHessianAdd(alpha, x, y);
-  }
-
-  /**
-   * @brief double* Hessian-vector multiply, i.e. y += A'*(A*x)
-   * RAW memory access! Assumes keys start at 0 and go to M-1, and x and and y are laid out that way
-   */
-  void multiplyHessianAdd(double alpha, const double* x, double* y) const {
-    if (empty())
-      return;
-    Vector Ax = zero(Ab_.rows());
-
-    // Just iterate over all A matrices and multiply in correct config part
-    for (size_t pos = 0; pos < size(); ++pos)
-      Ax += Ab_(pos) * ConstDMap(x + D * keys_[pos]);
-
-    // Deal with noise properly, need to Double* whiten as we are dividing by variance
-    if (model_) {
-      model_->whitenInPlace(Ax);
-      model_->whitenInPlace(Ax);
-    }
-
-    // multiply with alpha
-    Ax *= alpha;
-
-    // Again iterate over all A matrices and insert Ai^e into y
-    for (size_t pos = 0; pos < size(); ++pos)
-      DMap(y + D * keys_[pos]) += Ab_(pos).transpose() * Ax;
-  }
-
-};
-// end class JacobianSchurFactor
-
-}// end namespace gtsam