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diff --git a/eigen/bench/spbench/test_sparseLU.cpp b/eigen/bench/spbench/test_sparseLU.cpp
new file mode 100644
index 0000000..f8ecbe6
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+++ b/eigen/bench/spbench/test_sparseLU.cpp
@@ -0,0 +1,93 @@
+// Small bench routine for Eigen available in Eigen
+// (C) Desire NUENTSA WAKAM, INRIA
+
+#include <iostream>
+#include <fstream>
+#include <iomanip>
+#include <unsupported/Eigen/SparseExtra>
+#include <Eigen/SparseLU>
+#include <bench/BenchTimer.h>
+#ifdef EIGEN_METIS_SUPPORT
+#include <Eigen/MetisSupport>
+#endif
+
+using namespace std;
+using namespace Eigen;
+
+int main(int argc, char **args)
+{
+//   typedef complex<double> scalar; 
+  typedef double scalar; 
+  SparseMatrix<scalar, ColMajor> A; 
+  typedef SparseMatrix<scalar, ColMajor>::Index Index;
+  typedef Matrix<scalar, Dynamic, Dynamic> DenseMatrix;
+  typedef Matrix<scalar, Dynamic, 1> DenseRhs;
+  Matrix<scalar, Dynamic, 1> b, x, tmp;
+//   SparseLU<SparseMatrix<scalar, ColMajor>, AMDOrdering<int> >   solver;
+// #ifdef EIGEN_METIS_SUPPORT
+//   SparseLU<SparseMatrix<scalar, ColMajor>, MetisOrdering<int> > solver; 
+//   std::cout<< "ORDERING : METIS\n"; 
+// #else
+  SparseLU<SparseMatrix<scalar, ColMajor>, COLAMDOrdering<int> >  solver;
+  std::cout<< "ORDERING : COLAMD\n"; 
+// #endif
+  
+  ifstream matrix_file; 
+  string line;
+  int  n;
+  BenchTimer timer; 
+  
+  // Set parameters
+  /* Fill the matrix with sparse matrix stored in Matrix-Market coordinate column-oriented format */
+  if (argc < 2) assert(false && "please, give the matrix market file ");
+  loadMarket(A, args[1]);
+  cout << "End charging matrix " << endl;
+  bool iscomplex=false, isvector=false;
+  int sym;
+  getMarketHeader(args[1], sym, iscomplex, isvector);
+//   if (iscomplex) { cout<< " Not for complex matrices \n"; return -1; }
+  if (isvector) { cout << "The provided file is not a matrix file\n"; return -1;}
+  if (sym != 0) { // symmetric matrices, only the lower part is stored
+    SparseMatrix<scalar, ColMajor> temp; 
+    temp = A;
+    A = temp.selfadjointView<Lower>();
+  }
+  n = A.cols();
+  /* Fill the right hand side */
+
+  if (argc > 2)
+    loadMarketVector(b, args[2]);
+  else 
+  {
+    b.resize(n);
+    tmp.resize(n);
+//       tmp.setRandom();
+    for (int i = 0; i < n; i++) tmp(i) = i; 
+    b = A * tmp ;
+  }
+
+  /* Compute the factorization */
+//   solver.isSymmetric(true);
+  timer.start(); 
+//   solver.compute(A);
+  solver.analyzePattern(A); 
+  timer.stop(); 
+  cout << "Time to analyze " << timer.value() << std::endl;
+  timer.reset(); 
+  timer.start(); 
+  solver.factorize(A); 
+  timer.stop(); 
+  cout << "Factorize Time " << timer.value() << std::endl;
+  timer.reset(); 
+  timer.start(); 
+  x = solver.solve(b);
+  timer.stop();
+  cout << "solve time " << timer.value() << std::endl; 
+  /* Check the accuracy */
+  Matrix<scalar, Dynamic, 1> tmp2 = b - A*x;
+  scalar tempNorm = tmp2.norm()/b.norm();
+  cout << "Relative norm of the computed solution : " << tempNorm <<"\n";
+  cout << "Number of nonzeros in the factor : " << solver.nnzL() + solver.nnzU() << std::endl; 
+  
+  return 0;
+}
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