merge with default branch

test/CMakeLists.txt

@@ -82,7 +82,7 @@ endif()
 
 find_package(Pastix)
 find_package(Scotch)
-find_package(Metis)
+find_package(Metis 5.0 REQUIRED)
 if(PASTIX_FOUND)
   add_definitions("-DEIGEN_PASTIX_SUPPORT")
   include_directories(${PASTIX_INCLUDES})
@@ -304,6 +304,7 @@ ei_add_property(EIGEN_TESTING_SUMMARY "CXX_FLAGS:         ${CMAKE_CXX_FLAGS}\n")
 ei_add_property(EIGEN_TESTING_SUMMARY "Sparse lib flags:  ${SPARSE_LIBS}\n")
 
 option(EIGEN_TEST_EIGEN2 "Run whole Eigen2 test suite against EIGEN2_SUPPORT" OFF)
+mark_as_advanced(EIGEN_TEST_EIGEN2)
 if(EIGEN_TEST_EIGEN2)
   message(WARNING "The Eigen2 test suite has been removed")
 endif()

test/block.cpp

@@ -141,11 +141,11 @@ template<typename MatrixType> void block(const MatrixType& m)
   VERIFY_IS_EQUAL( (m1.transpose().block(c1,r1,c2-c1+1,r2-r1+1).col(0)) , (m1.row(r1).segment(c1,c2-c1+1)).transpose() );
 
   // expressions without direct access
-  VERIFY_IS_EQUAL( ((m1+m2).block(r1,c1,rows-r1,cols-c1).block(r2-r1,c2-c1,rows-r2,cols-c2)) , ((m1+m2).block(r2,c2,rows-r2,cols-c2)) );
-  VERIFY_IS_EQUAL( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).row(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)) );
-  VERIFY_IS_EQUAL( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).col(0)) , ((m1+m2).col(c1).segment(r1,r2-r1+1)) );
-  VERIFY_IS_EQUAL( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).transpose().col(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)).transpose() );
-  VERIFY_IS_EQUAL( ((m1+m2).transpose().block(c1,r1,c2-c1+1,r2-r1+1).col(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)).transpose() );
+  VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,rows-r1,cols-c1).block(r2-r1,c2-c1,rows-r2,cols-c2)) , ((m1+m2).block(r2,c2,rows-r2,cols-c2)) );
+  VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).row(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)) );
+  VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).col(0)) , ((m1+m2).col(c1).segment(r1,r2-r1+1)) );
+  VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).transpose().col(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)).transpose() );
+  VERIFY_IS_APPROX( ((m1+m2).transpose().block(c1,r1,c2-c1+1,r2-r1+1).col(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)).transpose() );
 
   // evaluation into plain matrices from expressions with direct access (stress MapBase)
   DynamicMatrixType dm;

test/cholesky.cpp

@@ -68,6 +68,7 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
   Index cols = m.cols();
 
   typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
   typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
   typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
 
@@ -180,7 +181,7 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
     if(rows>=3)
     {
       SquareMatrixType A = symm;
-      int c = internal::random<int>(0,rows-2);
+      Index c = internal::random<Index>(0,rows-2);
       A.bottomRightCorner(c,c).setZero();
       // Make sure a solution exists:
       vecX.setRandom();
@@ -195,7 +196,7 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
     // check non-full rank matrices
     if(rows>=3)
     {
-      int r = internal::random<int>(1,rows-1);
+      Index r = internal::random<Index>(1,rows-1);
       Matrix<Scalar,Dynamic,Dynamic> a = Matrix<Scalar,Dynamic,Dynamic>::Random(rows,r);
       SquareMatrixType A = a * a.adjoint();
       // Make sure a solution exists:
@@ -207,6 +208,25 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
       vecX = ldltlo.solve(vecB);
       VERIFY_IS_APPROX(A * vecX, vecB);
     }
+    
+    // check matrices with a wide spectrum
+    if(rows>=3)
+    {
+      RealScalar s = (std::min)(16,std::numeric_limits<RealScalar>::max_exponent10/8);
+      Matrix<Scalar,Dynamic,Dynamic> a = Matrix<Scalar,Dynamic,Dynamic>::Random(rows,rows);
+      Matrix<RealScalar,Dynamic,1> d =  Matrix<RealScalar,Dynamic,1>::Random(rows);
+      for(Index k=0; k<rows; ++k)
+        d(k) = d(k)*std::pow(RealScalar(10),internal::random<RealScalar>(-s,s));
+      SquareMatrixType A = a * d.asDiagonal() * a.adjoint();
+      // Make sure a solution exists:
+      vecX.setRandom();
+      vecB = A * vecX;
+      vecX.setZero();
+      ldltlo.compute(A);
+      VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
+      vecX = ldltlo.solve(vecB);
+      VERIFY_IS_APPROX(A * vecX, vecB);
+    }
   }
 
   // update/downdate

test/mapstaticmethods.cpp

@@ -69,7 +69,8 @@ struct mapstaticmethods_impl<PlainObjectType, true, false>
 {
   static void run(const PlainObjectType& m)
   {
-    int rows = m.rows(), cols = m.cols();
+    typedef typename PlainObjectType::Index Index;
+    Index rows = m.rows(), cols = m.cols();
 
     int i = internal::random<int>(2,5), j = internal::random<int>(2,5);
 
@@ -115,7 +116,8 @@ struct mapstaticmethods_impl<PlainObjectType, true, true>
 {
   static void run(const PlainObjectType& v)
   {
-    int size = v.size();
+    typedef typename PlainObjectType::Index Index;
+    Index size = v.size();
 
     int i = internal::random<int>(2,5);
 

test/packetmath.cpp

@@ -408,14 +408,17 @@ template<typename Scalar> void packetmath_scatter_gather() {
   for (int i=0; i<PacketSize; ++i) {
     data1[i] = internal::random<Scalar>()/RealScalar(PacketSize);
   }
-  EIGEN_ALIGN_DEFAULT Scalar buffer[PacketSize*11];
-  memset(buffer, 0, 11*sizeof(Packet));
+  
+  int stride = internal::random<int>(1,20);
+  
+  EIGEN_ALIGN_DEFAULT Scalar buffer[PacketSize*20];
+  memset(buffer, 0, 20*sizeof(Packet));
   Packet packet = internal::pload<Packet>(data1);
-  internal::pscatter<Scalar, Packet>(buffer, packet, 11);
+  internal::pscatter<Scalar, Packet>(buffer, packet, stride);
 
-  for (int i = 0; i < PacketSize*11; ++i) {
-    if ((i%11) == 0) {
-      VERIFY(isApproxAbs(buffer[i], data1[i/11], refvalue) && "pscatter");
+  for (int i = 0; i < PacketSize*20; ++i) {
+    if ((i%stride) == 0 && i<stride*PacketSize) {
+      VERIFY(isApproxAbs(buffer[i], data1[i/stride], refvalue) && "pscatter");
     } else {
       VERIFY(isApproxAbs(buffer[i], Scalar(0), refvalue) && "pscatter");
     }

test/product_notemporary.cpp

@@ -7,13 +7,12 @@
 // 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/.
 
-static int nb_temporaries;
+static long int nb_temporaries;
 
-inline void on_temporary_creation(int size) {
+inline void on_temporary_creation(long int size) {
   // here's a great place to set a breakpoint when debugging failures in this test!
   if(size!=0) nb_temporaries++;
 }
-  
 
 #define EIGEN_DENSE_STORAGE_CTOR_PLUGIN { on_temporary_creation(size); }
 

test/ref.cpp

@@ -12,13 +12,12 @@
 #undef EIGEN_DEFAULT_TO_ROW_MAJOR
 #endif
 
-static int nb_temporaries;
+static long int nb_temporaries;
 
-inline void on_temporary_creation(int) {
+inline void on_temporary_creation(long int) {
   // here's a great place to set a breakpoint when debugging failures in this test!
   nb_temporaries++;
 }
-  
 
 #define EIGEN_DENSE_STORAGE_CTOR_PLUGIN { on_temporary_creation(size); }
 

test/sparse.h

@@ -71,7 +71,7 @@ initSparse(double density,
     //sparseMat.startVec(j);
     for(Index i=0; i<sparseMat.innerSize(); i++)
     {
-      int ai(i), aj(j);
+      Index ai(i), aj(j);
       if(IsRowMajor)
         std::swap(ai,aj);
       Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
@@ -163,7 +163,7 @@ initSparse(double density,
 {
   sparseVec.reserve(int(refVec.size()*density));
   sparseVec.setZero();
-  for(Index i=0; i<refVec.size(); i++)
+  for(int i=0; i<refVec.size(); i++)
   {
     Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
     if (v!=Scalar(0))

test/sparse_basic.cpp

@@ -147,7 +147,7 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
       DenseMatrix m1(rows,cols);
       m1.setZero();
       SparseMatrixType m2(rows,cols);
-      VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? m2.innerSize() : std::max<int>(1,m2.innerSize()/8)));
+      VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? int(m2.innerSize()) : std::max<int>(1,int(m2.innerSize())/8)));
       m2.reserve(r);
       for (int k=0; k<rows*cols; ++k)
       {
@@ -181,7 +181,7 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
       VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
 
     SparseMatrixType m3(rows,rows);
-    m3.reserve(VectorXi::Constant(rows,rows/2));
+    m3.reserve(VectorXi::Constant(rows,int(rows/2)));
     for(Index j=0; j<rows; ++j)
       for(Index k=0; k<j; ++k)
         m3.insertByOuterInner(j,k) = k+1;
@@ -384,11 +384,11 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
   {
     typedef Triplet<Scalar,Index> TripletType;
     std::vector<TripletType> triplets;
-    int ntriplets = rows*cols;
+    Index ntriplets = rows*cols;
     triplets.reserve(ntriplets);
     DenseMatrix refMat(rows,cols);
     refMat.setZero();
-    for(int i=0;i<ntriplets;++i)
+    for(Index i=0;i<ntriplets;++i)
     {
       Index r = internal::random<Index>(0,rows-1);
       Index c = internal::random<Index>(0,cols-1);

test/triangular.cpp

@@ -113,6 +113,13 @@ template<typename MatrixType> void triangular_square(const MatrixType& m)
   m3.setZero();
   m3.template triangularView<Upper>().setOnes();
   VERIFY_IS_APPROX(m2,m3);
+  
+  m1.setRandom();
+  m3 = m1.template triangularView<Upper>();
+  Matrix<Scalar, MatrixType::ColsAtCompileTime, Dynamic> m5(cols, internal::random<int>(1,20));  m5.setRandom();
+  Matrix<Scalar, Dynamic, MatrixType::RowsAtCompileTime> m6(internal::random<int>(1,20), rows);  m6.setRandom();
+  VERIFY_IS_APPROX(m1.template triangularView<Upper>() * m5, m3*m5);
+  VERIFY_IS_APPROX(m6*m1.template triangularView<Upper>(), m6*m3);
 
 }