diff --git a/unsupported/Eigen/src/KLUSupport/KLUSupport.h b/unsupported/Eigen/src/KLUSupport/KLUSupport.h
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+++ b/unsupported/Eigen/src/KLUSupport/KLUSupport.h
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Kyle Macfarlan <kyle.macfarlan@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_KLUSUPPORT_H
+#define EIGEN_KLUSUPPORT_H
+
+namespace Eigen {
+
+/* TODO extract L, extract U, compute det, etc... */
+
+/** \ingroup KLUSupport_Module
+  * \brief A sparse LU factorization and solver based on KLU
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a LU factorization
+  * using the KLU library. The sparse matrix A must be squared and full rank.
+  * The vectors or matrices X and B can be either dense or sparse.
+  *
+  * \warning The input matrix A should be in a \b compressed and \b column-major form.
+  * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa \ref TutorialSparseSolverConcept, class SparseLU
+  */
+
+
+inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, int ldim, int nrhs, double B [ ], klu_common *Common, double) {
+   return klu_solve(Symbolic, Numeric, ldim, nrhs, B, Common);
+}
+
+inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, int ldim, int nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) {
+   return klu_z_solve(Symbolic, Numeric, ldim, nrhs, &numext::real_ref(B[0]), Common);
+}
+
+inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, int ldim, int nrhs, double B[], klu_common *Common, double) {
+   return klu_tsolve(Symbolic, Numeric, ldim, nrhs, B, Common);
+}
+
+inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, int ldim, int nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) {
+   return klu_z_tsolve(Symbolic, Numeric, ldim, nrhs, &numext::real_ref(B[0]), 0, Common);
+}
+
+inline klu_numeric* klu_factor(int Ap [ ], int Ai [ ], double Ax [ ], klu_symbolic *Symbolic, klu_common *Common, double) {
+   return klu_factor(Ap, Ai, Ax, Symbolic, Common);
+}
+
+inline klu_numeric* klu_factor(int Ap[], int Ai[], std::complex<double> Ax[], klu_symbolic *Symbolic, klu_common *Common, std::complex<double>) {
+   return klu_z_factor(Ap, Ai, &numext::real_ref(Ax[0]), Symbolic, Common);
+}
+
+
+template<typename _MatrixType>
+class KLU : public SparseSolverBase<KLU<_MatrixType> >
+{
+  protected:
+    typedef SparseSolverBase<KLU<_MatrixType> > Base;
+    using Base::m_isInitialized;
+  public:
+    using Base::_solve_impl;
+    typedef _MatrixType MatrixType;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
+    typedef SparseMatrix<Scalar> LUMatrixType;
+    typedef SparseMatrix<Scalar,ColMajor,int> KLUMatrixType;
+    typedef Ref<const KLUMatrixType, StandardCompressedFormat> KLUMatrixRef;
+    enum {
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+
+  public:
+
+    KLU()
+      : m_dummy(0,0), mp_matrix(m_dummy)
+    {
+      init();
+    }
+
+    template<typename InputMatrixType>
+    explicit KLU(const InputMatrixType& matrix)
+      : mp_matrix(matrix)
+    {
+      init();
+      compute(matrix);
+    }
+
+    ~KLU()
+    {
+      if(m_symbolic) klu_free_symbolic(&m_symbolic,&m_common);
+      if(m_numeric)  klu_free_numeric(&m_numeric,&m_common);
+    }
+
+    inline Index rows() const { return mp_matrix.rows(); }
+    inline Index cols() const { return mp_matrix.cols(); }
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was succesful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+
+    inline const LUMatrixType& matrixL() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_l;
+    }
+
+    inline const LUMatrixType& matrixU() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_u;
+    }
+
+    inline const IntColVectorType& permutationP() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_p;
+    }
+
+    inline const IntRowVectorType& permutationQ() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_q;
+    }
+
+    /** Computes the sparse Cholesky decomposition of \a matrix
+     *  Note that the matrix should be column-major, and in compressed format for best performance.
+     *  \sa SparseMatrix::makeCompressed().
+     */
+    template<typename InputMatrixType>
+    void compute(const InputMatrixType& matrix)
+    {
+      if(m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
+      if(m_numeric)  klu_free_numeric(&m_numeric, &m_common);
+      grab(matrix.derived());
+      analyzePattern_impl();
+      factorize_impl();
+    }
+
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      *
+      * \sa factorize(), compute()
+      */
+    template<typename InputMatrixType>
+    void analyzePattern(const InputMatrixType& matrix)
+    {
+      if(m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
+      if(m_numeric)  klu_free_numeric(&m_numeric, &m_common);
+
+      grab(matrix.derived());
+
+      analyzePattern_impl();
+    }
+
+
+    /** Provides access to the control settings array used by KLU.
+      *
+      * See KLU documentation for details.
+      */
+    inline const klu_common& kluCommon() const
+    {
+      return m_common;
+    }
+
+    /** Provides access to the control settings array used by UmfPack.
+      *
+      * If this array contains NaN's, the default values are used.
+      *
+      * See KLU documentation for details.
+      */
+    inline klu_common& kluCommon()
+    {
+      return m_common;
+    }
+
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.
+      *
+      * \sa analyzePattern(), compute()
+      */
+    template<typename InputMatrixType>
+    void factorize(const InputMatrixType& matrix)
+    {
+      eigen_assert(m_analysisIsOk && "KLU: you must first call analyzePattern()");
+      if(m_numeric)
+        klu_free_numeric(&m_numeric,&m_common);
+
+      grab(matrix.derived());
+
+      factorize_impl();
+    }
+
+    /** \internal */
+    template<typename BDerived,typename XDerived>
+    bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
+
+    Scalar determinant() const;
+
+    void extractData() const;
+
+  protected:
+
+    void init()
+    {
+      m_info                  = InvalidInput;
+      m_isInitialized         = false;
+      m_numeric               = 0;
+      m_symbolic              = 0;
+      m_extractedDataAreDirty = true;
+
+      klu_defaults(&m_common);
+    }
+
+    void analyzePattern_impl()
+    {
+      m_info = InvalidInput;
+      m_analysisIsOk = false;
+      m_factorizationIsOk = false;
+      m_symbolic = klu_analyze(internal::convert_index<int>(mp_matrix.rows()),
+                                     const_cast<StorageIndex*>(mp_matrix.outerIndexPtr()), const_cast<StorageIndex*>(mp_matrix.innerIndexPtr()),
+                                     &m_common);
+      if (m_symbolic) {
+         m_isInitialized = true;
+         m_info = Success;
+         m_analysisIsOk = true;
+         m_extractedDataAreDirty = true;
+      }
+    }
+
+    void factorize_impl()
+    {
+
+      m_numeric = klu_factor(const_cast<StorageIndex*>(mp_matrix.outerIndexPtr()), const_cast<StorageIndex*>(mp_matrix.innerIndexPtr()), const_cast<Scalar*>(mp_matrix.valuePtr()),
+                                    m_symbolic, &m_common, Scalar());
+                                         
+
+      m_info = m_numeric ? Success : NumericalIssue;
+      m_factorizationIsOk = m_numeric ? 1 : 0;
+      m_extractedDataAreDirty = true;
+    }
+
+    template<typename MatrixDerived>
+    void grab(const EigenBase<MatrixDerived> &A)
+    {
+      mp_matrix.~KLUMatrixRef();
+      ::new (&mp_matrix) KLUMatrixRef(A.derived());
+    }
+
+    void grab(const KLUMatrixRef &A)
+    {
+      if(&(A.derived()) != &mp_matrix)
+      {
+        mp_matrix.~KLUMatrixRef();
+        ::new (&mp_matrix) KLUMatrixRef(A);
+      }
+    }
+
+    // cached data to reduce reallocation, etc.
+    mutable LUMatrixType m_l;
+
+    mutable LUMatrixType m_u;
+    mutable IntColVectorType m_p;
+    mutable IntRowVectorType m_q;
+
+    KLUMatrixType m_dummy;
+    KLUMatrixRef mp_matrix;
+
+    klu_numeric* m_numeric;
+    klu_symbolic* m_symbolic;
+    klu_common m_common;
+    mutable ComputationInfo m_info;
+    int m_factorizationIsOk;
+    int m_analysisIsOk;
+    mutable bool m_extractedDataAreDirty;
+
+  private:
+    KLU(const KLU& ) { }
+};
+
+
+template<typename MatrixType>
+void KLU<MatrixType>::extractData() const
+{
+  if (m_extractedDataAreDirty)
+  {
+     eigen_assert(false && "KLU: extractData Not Yet Implemented");
+
+//     // get size of the data
+//     int lnz, unz, rows, cols, nz_udiag;
+//     umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
+// 
+//     // allocate data
+//     m_l.resize(rows,(std::min)(rows,cols));
+//     m_l.resizeNonZeros(lnz);
+// 
+//     m_u.resize((std::min)(rows,cols),cols);
+//     m_u.resizeNonZeros(unz);
+// 
+//     m_p.resize(rows);
+//     m_q.resize(cols);
+// 
+//     // extract
+//     umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(),
+//                         m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(),
+//                         m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
+// 
+//     m_extractedDataAreDirty = false;
+  }
+}
+
+template<typename MatrixType>
+typename KLU<MatrixType>::Scalar KLU<MatrixType>::determinant() const
+{
+  eigen_assert(false && "KLU: extractData Not Yet Implemented");
+  return Scalar();
+}
+
+template<typename MatrixType>
+template<typename BDerived,typename XDerived>
+bool KLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const
+{
+  Index rhsCols = b.cols();
+  eigen_assert((BDerived::Flags&RowMajorBit)==0 && "KLU backend does not support non col-major rhs yet");
+  eigen_assert((XDerived::Flags&RowMajorBit)==0 && "KLU backend does not support non col-major result yet");
+  eigen_assert(b.derived().data() != x.derived().data() && " KLU does not support inplace solve");
+  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
+
+  x = b;
+  int info = 0;
+  if (true/*(MatrixType::Flags&RowMajorBit) == 0*/)
+  {
+      info = klu_solve(m_symbolic, m_numeric, b.rows(), rhsCols, x.const_cast_derived().data(), const_cast<klu_common*>(&m_common), Scalar());
+  }
+  else
+  {
+     info = klu_tsolve(m_symbolic, m_numeric, b.rows(), rhsCols, x.const_cast_derived().data(), const_cast<klu_common*>(&m_common), Scalar());
+  }
+
+  m_info = info!=0 ? Success : NumericalIssue;
+  return true;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_KLUSUPPORT_H