merge Sparse LU branch

3 files changed, 229 insertions, 0 deletions

diff --git a/bench/spbench/CMakeLists.txt b/bench/spbench/CMakeLists.txt
index 079912266..5451843b9 100644
--- a/bench/spbench/CMakeLists.txt
+++ b/bench/spbench/CMakeLists.txt
@@ -63,3 +63,15 @@ endif(RT_LIBRARY)
 add_executable(spbenchsolver spbenchsolver.cpp)
 target_link_libraries (spbenchsolver ${SPARSE_LIBS})
 
+add_executable(spsolver sp_solver.cpp)
+target_link_libraries (spsolver ${SPARSE_LIBS})
+
+if(METIS_FOUND)
+  include_directories(${METIS_INCLUDES})
+  set (SPARSE_LIBS ${SPARSE_LIBS} ${METIS_LIBRARIES})
+  add_definitions("-DEIGEN_METIS_SUPPORT")
+endif(METIS_FOUND)
+
+add_executable(test_sparseLU test_sparseLU.cpp)
+target_link_libraries (test_sparseLU ${SPARSE_LIBS})
+
diff --git a/bench/spbench/sp_solver.cpp b/bench/spbench/sp_solver.cpp
new file mode 100644
index 000000000..e18f2d1c3
--- /dev/null
+++ b/bench/spbench/sp_solver.cpp
@@ -0,0 +1,124 @@
+// Small bench routine for Eigen available in Eigen
+// (C) Desire NUENTSA WAKAM, INRIA
+
+#include <iostream>
+#include <fstream>
+#include <iomanip>
+#include <Eigen/Jacobi>
+#include <Eigen/Householder>
+#include <Eigen/IterativeLinearSolvers>
+#include <Eigen/LU>
+#include <unsupported/Eigen/SparseExtra>
+//#include <Eigen/SparseLU>
+#include <Eigen/SuperLUSupport>
+// #include <unsupported/Eigen/src/IterativeSolvers/Scaling.h>
+#include <bench/BenchTimer.h>
+
+using namespace std;
+using namespace Eigen;
+
+int main(int argc, char **args)
+{
+  SparseMatrix<double, ColMajor> A; 
+  typedef SparseMatrix<double, ColMajor>::Index Index;
+  typedef Matrix<double, Dynamic, Dynamic> DenseMatrix;
+  typedef Matrix<double, Dynamic, 1> DenseRhs;
+  VectorXd b, x, tmp;
+  BenchTimer timer,totaltime; 
+  //SparseLU<SparseMatrix<double, ColMajor> >   solver;
+  SuperLU<SparseMatrix<double, ColMajor> >   solver;
+  ifstream matrix_file; 
+  string line;
+  int  n;
+  // Set parameters
+//   solver.iparm(IPARM_THREAD_NBR) = 4;
+  /* 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 ");
+  
+  timer.start();
+  totaltime.start();
+  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<double, ColMajor> temp; 
+    temp = A;
+    A = temp.selfadjointView<Lower>();
+  }
+  timer.stop();
+  
+  n = A.cols();
+  // ====== TESTS FOR SPARSE TUTORIAL ======
+//   cout<< "OuterSize " << A.outerSize() << " inner " << A.innerSize() << endl; 
+//   SparseMatrix<double, RowMajor> mat1(A); 
+//   SparseMatrix<double, RowMajor> mat2;
+//   cout << " norm of A " << mat1.norm() << endl; ;
+//   PermutationMatrix<Dynamic, Dynamic, int> perm(n);
+//   perm.resize(n,1);
+//   perm.indices().setLinSpaced(n, 0, n-1);
+//   mat2 = perm * mat1;
+//   mat.subrows();
+//   mat2.resize(n,n); 
+//   mat2.reserve(10);
+//   mat2.setConstant();
+//   std::cout<< "NORM " << mat1.squaredNorm()<< endl;  
+
+  cout<< "Time to load the matrix " << timer.value() <<endl;
+  /* Fill the right hand side */
+
+//   solver.set_restart(374);
+  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 ;
+  }
+//   Scaling<SparseMatrix<double> > scal; 
+//   scal.computeRef(A);
+//   b = scal.LeftScaling().cwiseProduct(b);
+
+  /* Compute the factorization */
+  cout<< "Starting the factorization "<< endl; 
+  timer.reset();
+  timer.start(); 
+  cout<< "Size of Input Matrix "<< b.size()<<"\n\n";
+  cout<< "Rows and columns "<< A.rows() <<" " <<A.cols() <<"\n";
+  solver.compute(A);
+//   solver.analyzePattern(A);
+//   solver.factorize(A);
+  if (solver.info() != Success) {
+    std::cout<< "The solver failed \n";
+    return -1; 
+  }
+  timer.stop(); 
+  float time_comp = timer.value(); 
+  cout <<" Compute Time " << time_comp<< endl; 
+  
+  timer.reset();
+  timer.start();
+  x = solver.solve(b);
+//   x = scal.RightScaling().cwiseProduct(x);
+  timer.stop();
+  float time_solve = timer.value(); 
+  cout<< " Time to solve " << time_solve << endl; 
+ 
+  /* Check the accuracy */
+  VectorXd tmp2 = b - A*x;
+  double tempNorm = tmp2.norm()/b.norm();
+  cout << "Relative norm of the computed solution : " << tempNorm <<"\n";
+//   cout << "Iterations : " << solver.iterations() << "\n"; 
+  
+  totaltime.stop();
+  cout << "Total time " << totaltime.value() << "\n";
+//  std::cout<<x.transpose()<<"\n";
+  
+  return 0;
+}
\ No newline at end of file
diff --git a/bench/spbench/test_sparseLU.cpp b/bench/spbench/test_sparseLU.cpp
new file mode 100644
index 000000000..c6511a9bc
--- /dev/null
+++ b/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;
+}
\ No newline at end of file