Add no_assignment_operator to a few classes that must not be assigned, and fix a couple of warnings.

Eigen/src/Geometry/Homogeneous.h

@@ -59,7 +59,7 @@ template<typename MatrixType,typename Rhs> struct homogeneous_right_product_impl
 } // end namespace internal
 
 template<typename MatrixType,int _Direction> class Homogeneous
-  : public MatrixBase<Homogeneous<MatrixType,_Direction> >
+  : internal::no_assignment_operator, public MatrixBase<Homogeneous<MatrixType,_Direction> >
 {
   public:
 

Eigen/src/SparseLU/SparseLU.h

@@ -511,7 +511,7 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
   m_perm_r.resize(m); 
   m_perm_r.indices().setConstant(-1);
   marker.setConstant(-1);
-  m_detPermR = 1.0; // Record the determinant of the row permutation
+  m_detPermR = 1; // Record the determinant of the row permutation
   
   m_glu.supno(0) = emptyIdxLU; m_glu.xsup.setConstant(0);
   m_glu.xsup(0) = m_glu.xlsub(0) = m_glu.xusub(0) = m_glu.xlusup(0) = Index(0);
@@ -630,7 +630,7 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
 }
 
 template<typename MappedSupernodalType>
-struct SparseLUMatrixLReturnType
+struct SparseLUMatrixLReturnType : internal::no_assignment_operator
 {
   typedef typename MappedSupernodalType::Index Index;
   typedef typename MappedSupernodalType::Scalar Scalar;
@@ -647,7 +647,7 @@ struct SparseLUMatrixLReturnType
 };
 
 template<typename MatrixLType, typename MatrixUType>
-struct SparseLUMatrixUReturnType
+struct SparseLUMatrixUReturnType : internal::no_assignment_operator
 {
   typedef typename MatrixLType::Index Index;
   typedef typename MatrixLType::Scalar Scalar;
@@ -700,6 +700,7 @@ struct SparseLUMatrixUReturnType
   const MatrixLType& m_mapL;
   const MatrixUType& m_mapU;
 };
+
 namespace internal {
   
 template<typename _MatrixType, typename Derived, typename Rhs>

Eigen/src/SparseLU/SparseLU_Memory.h

@@ -70,7 +70,7 @@ Index  SparseLUImpl<Scalar,Index>::expand(VectorType& vec, Index& length, Index
   if(num_expansions == 0 || keep_prev) 
     new_len = length ; // First time allocate requested
   else 
-    new_len = alpha * length ;
+    new_len = Index(alpha * length);
   
   VectorType old_vec; // Temporary vector to hold the previous values   
   if (nbElts > 0 )
@@ -100,7 +100,7 @@ Index  SparseLUImpl<Scalar,Index>::expand(VectorType& vec, Index& length, Index
       do 
       {
         alpha = (alpha + 1)/2;
-        new_len = alpha * length ; 
+        new_len = Index(alpha * length);
         try
         {
           vec.resize(new_len); 
@@ -141,7 +141,7 @@ Index SparseLUImpl<Scalar,Index>::memInit(Index m, Index n, Index annz, Index lw
   Index& num_expansions = glu.num_expansions; //No memory expansions so far
   num_expansions = 0; 
   glu.nzumax = glu.nzlumax = (std::max)(fillratio * annz, m*n); // estimated number of nonzeros in U 
-  glu.nzlmax  = (std::max)(1., fillratio/4.) * annz; // estimated  nnz in L factor
+  glu.nzlmax = (std::max)(Index(4), fillratio) * annz / 4; // estimated  nnz in L factor
 
   // Return the estimated size to the user if necessary
   Index tempSpace;

Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h

@@ -216,13 +216,13 @@ class MappedSuperNodalMatrix<Scalar,Index>::InnerIterator
     
   protected:
     const MappedSuperNodalMatrix& m_matrix; // Supernodal lower triangular matrix 
-    const Index m_outer; // Current column 
+    const Index m_outer;                    // Current column 
-    const Index m_supno; // Current SuperNode number
+    const Index m_supno;                    // Current SuperNode number
-    Index m_idval; //Index to browse the values in the current column
+    Index m_idval;                          // Index to browse the values in the current column
-    const Index m_startidval; // Start of the column value
+    const Index m_startidval;               // Start of the column value
-    const Index m_endidval; // End of the column value
+    const Index m_endidval;                 // End of the column value
-    Index m_idrow;  //Index to browse the row indices 
+    Index m_idrow;                          // Index to browse the row indices 
-    Index m_endidrow; // End index of row indices of the current column
+    Index m_endidrow;                       // End index of row indices of the current column
 };
 
 /**
@@ -235,17 +235,17 @@ void MappedSuperNodalMatrix<Scalar,Index>::solveInPlace( MatrixBase<Dest>&X) con
 {
     Index n = X.rows(); 
     Index nrhs = X.cols(); 
-    const Scalar * Lval = valuePtr(); // Nonzero values 
+    const Scalar * Lval = valuePtr();                 // Nonzero values 
-    Matrix<Scalar,Dynamic,Dynamic> work(n, nrhs); // working vector
+    Matrix<Scalar,Dynamic,Dynamic> work(n, nrhs);     // working vector
     work.setZero();
     for (Index k = 0; k <= nsuper(); k ++)
     {
-      Index fsupc = supToCol()[k]; // First column of the current supernode 
+      Index fsupc = supToCol()[k];                    // First column of the current supernode 
-      Index istart = rowIndexPtr()[fsupc];  // Pointer index to the subscript of the current column
+      Index istart = rowIndexPtr()[fsupc];            // Pointer index to the subscript of the current column
       Index nsupr = rowIndexPtr()[fsupc+1] - istart;  // Number of rows in the current supernode
-      Index nsupc = supToCol()[k+1] - fsupc;  // Number of columns in the current supernode
+      Index nsupc = supToCol()[k+1] - fsupc;          // Number of columns in the current supernode
-      Index nrow = nsupr - nsupc; // Number of rows in the non-diagonal part of the supernode
+      Index nrow = nsupr - nsupc;                     // Number of rows in the non-diagonal part of the supernode
-      Index irow; //Current index row
+      Index irow;                                     //Current index row
       
       if (nsupc == 1 )
       {
@@ -294,4 +294,5 @@ void MappedSuperNodalMatrix<Scalar,Index>::solveInPlace( MatrixBase<Dest>&X) con
 } // end namespace internal
 
 } // end namespace Eigen
+
 #endif // EIGEN_SPARSELU_MATRIX_H

Eigen/src/SparseLU/SparseLU_column_dfs.h

@@ -36,7 +36,7 @@ namespace Eigen {
 namespace internal {
 
 template<typename IndexVector, typename ScalarVector>
-struct column_dfs_traits
+struct column_dfs_traits : no_assignment_operator
 {
   typedef typename ScalarVector::Scalar Scalar;
   typedef typename IndexVector::Scalar Index;

test/cholesky.cpp

@@ -328,4 +328,5 @@ void test_cholesky()
   CALL_SUBTEST_9( LDLT<MatrixXf>(10) );
   
   TEST_SET_BUT_UNUSED_VARIABLE(s)
+  TEST_SET_BUT_UNUSED_VARIABLE(nb_temporaries)
 }

test/geo_eulerangles.cpp

@@ -41,7 +41,7 @@ template<typename Scalar> void check_all_var(const Matrix<Scalar,3,1>& ea)
   VERIFY_EULER(2,1,2, Z,Y,Z);
 }
 
-template<typename Scalar> void eulerangles(void)
+template<typename Scalar> void eulerangles()
 {
   typedef Matrix<Scalar,3,3> Matrix3;
   typedef Matrix<Scalar,3,1> Vector3;
@@ -60,13 +60,13 @@ template<typename Scalar> void eulerangles(void)
   ea = m.eulerAngles(0,1,0);
   check_all_var(ea);
   
-  ea = (Array3::Random() + Array3(1,1,0))*M_PI*Array3(0.5,0.5,1);
+  ea = (Array3::Random() + Array3(1,1,0))*Scalar(M_PI)*Array3(0.5,0.5,1);
   check_all_var(ea);
   
-  ea[2] = ea[0] = internal::random<Scalar>(0,M_PI);
+  ea[2] = ea[0] = internal::random<Scalar>(0,Scalar(M_PI));
   check_all_var(ea);
   
-  ea[0] = ea[1] = internal::random<Scalar>(0,M_PI);
+  ea[0] = ea[1] = internal::random<Scalar>(0,Scalar(M_PI));
   check_all_var(ea);
   
   ea[1] = 0;

test/mapstride.cpp

@@ -116,7 +116,7 @@ template<int Alignment,typename MatrixType> void map_class_matrix(const MatrixTy
 void test_mapstride()
 {
   for(int i = 0; i < g_repeat; i++) {
-    EIGEN_UNUSED int maxn = 30;
+    int maxn = 30;
     CALL_SUBTEST_1( map_class_vector<Aligned>(Matrix<float, 1, 1>()) );
     CALL_SUBTEST_1( map_class_vector<Unaligned>(Matrix<float, 1, 1>()) );
     CALL_SUBTEST_2( map_class_vector<Aligned>(Vector4d()) );
@@ -142,5 +142,7 @@ void test_mapstride()
     CALL_SUBTEST_5( map_class_matrix<Unaligned>(MatrixXi(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
     CALL_SUBTEST_6( map_class_matrix<Aligned>(MatrixXcd(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
     CALL_SUBTEST_6( map_class_matrix<Unaligned>(MatrixXcd(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
+    
+    TEST_SET_BUT_UNUSED_VARIABLE(maxn);
   }
 }

test/redux.cpp

@@ -22,7 +22,7 @@ template<typename MatrixType> void matrixRedux(const MatrixType& m)
 
   // The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
   // failures if we underflow into denormals. Thus, we scale so that entires are close to 1.
-  MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + Scalar(0.2) * m1;
+  MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1;
 
   VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
   VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy

test/sizeof.cpp

@@ -13,7 +13,7 @@ template<typename MatrixType> void verifySizeOf(const MatrixType&)
 {
   typedef typename MatrixType::Scalar Scalar;
   if (MatrixType::RowsAtCompileTime!=Dynamic && MatrixType::ColsAtCompileTime!=Dynamic)
-    VERIFY(sizeof(MatrixType)==sizeof(Scalar)*size_t(MatrixType::SizeAtCompileTime));
+    VERIFY(sizeof(MatrixType)==sizeof(Scalar)*std::ptrdiff_t(MatrixType::SizeAtCompileTime));
   else
     VERIFY(sizeof(MatrixType)==sizeof(Scalar*) + 2 * sizeof(typename MatrixType::Index));
 }

test/sparse.h

@@ -58,8 +58,8 @@ initSparse(double density,
            Matrix<Scalar,Dynamic,Dynamic,Opt1>& refMat,
            SparseMatrix<Scalar,Opt2,Index>& sparseMat,
            int flags = 0,
-           std::vector<Vector2i>* zeroCoords = 0,
+           std::vector<Matrix<Index,2,1> >* zeroCoords = 0,
-           std::vector<Vector2i>* nonzeroCoords = 0)
+           std::vector<Matrix<Index,2,1> >* nonzeroCoords = 0)
 {
   enum { IsRowMajor = SparseMatrix<Scalar,Opt2,Index>::IsRowMajor };
   sparseMat.setZero();
@@ -93,11 +93,11 @@ initSparse(double density,
         //sparseMat.insertBackByOuterInner(j,i) = v;
         sparseMat.insertByOuterInner(j,i) = v;
         if (nonzeroCoords)
-          nonzeroCoords->push_back(Vector2i(ai,aj));
+          nonzeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
       }
       else if (zeroCoords)
       {
-        zeroCoords->push_back(Vector2i(ai,aj));
+        zeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
       }
       refMat(ai,aj) = v;
     }
@@ -110,8 +110,8 @@ initSparse(double density,
            Matrix<Scalar,Dynamic,Dynamic, Opt1>& refMat,
            DynamicSparseMatrix<Scalar, Opt2, Index>& sparseMat,
            int flags = 0,
-           std::vector<Vector2i>* zeroCoords = 0,
+           std::vector<Matrix<Index,2,1> >* zeroCoords = 0,
-           std::vector<Vector2i>* nonzeroCoords = 0)
+           std::vector<Matrix<Index,2,1> >* nonzeroCoords = 0)
 {
   enum { IsRowMajor = DynamicSparseMatrix<Scalar,Opt2,Index>::IsRowMajor };
   sparseMat.setZero();
@@ -142,11 +142,11 @@ initSparse(double density,
       {
         sparseMat.insertBackByOuterInner(j,i) = v;
         if (nonzeroCoords)
-          nonzeroCoords->push_back(Vector2i(ai,aj));
+          nonzeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
       }
       else if (zeroCoords)
       {
-        zeroCoords->push_back(Vector2i(ai,aj));
+        zeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
       }
       refMat(ai,aj) = v;
     }

test/sparse_basic.cpp

@@ -14,7 +14,8 @@
 template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
 {
   typedef typename SparseMatrixType::Index Index;
-
+  typedef Matrix<Index,2,1> Vector2;
+  
   const Index rows = ref.rows();
   const Index cols = ref.cols();
   typedef typename SparseMatrixType::Scalar Scalar;
@@ -31,8 +32,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
     DenseVector vec1 = DenseVector::Random(rows);
 
-    std::vector<Vector2i> zeroCoords;
+    std::vector<Vector2> zeroCoords;
-    std::vector<Vector2i> nonzeroCoords;
+    std::vector<Vector2> nonzeroCoords;
     initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
 
     if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
@@ -104,11 +105,11 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
       SparseMatrixType m2(rows,cols);
       if(internal::random<int>()%2)
         m2.reserve(VectorXi::Constant(m2.outerSize(), 2));
-      for (int j=0; j<cols; ++j)
+      for (Index j=0; j<cols; ++j)
       {
-        for (int k=0; k<rows/2; ++k)
+        for (Index k=0; k<rows/2; ++k)
         {
-          int i = internal::random<int>(0,rows-1);
+          Index i = internal::random<Index>(0,rows-1);
           if (m1.coeff(i,j)==Scalar(0))
             m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
         }
@@ -126,8 +127,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
         m2.reserve(VectorXi::Constant(m2.outerSize(), 2));
       for (int k=0; k<rows*cols; ++k)
       {
-        int i = internal::random<int>(0,rows-1);
+        Index i = internal::random<Index>(0,rows-1);
-        int j = internal::random<int>(0,cols-1);
+        Index j = internal::random<Index>(0,cols-1);
         if ((m1.coeff(i,j)==Scalar(0)) && (internal::random<int>()%2))
           m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
         else
@@ -150,8 +151,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
       m2.reserve(r);
       for (int k=0; k<rows*cols; ++k)
       {
-        int i = internal::random<int>(0,rows-1);
+        Index i = internal::random<Index>(0,rows-1);
-        int j = internal::random<int>(0,cols-1);
+        Index j = internal::random<Index>(0,cols-1);
         if (m1.coeff(i,j)==Scalar(0))
           m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
         if(mode==3)
@@ -167,8 +168,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
     SparseMatrixType m2(rows, rows);
     initSparse<Scalar>(density, refMat2, m2);
-    int j0 = internal::random<int>(0,rows-1);
+    Index j0 = internal::random<Index>(0,rows-1);
-    int j1 = internal::random<int>(0,rows-1);
+    Index j1 = internal::random<Index>(0,rows-1);
     if(SparseMatrixType::IsRowMajor)
       VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.row(j0));
     else
@@ -181,17 +182,17 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
 
     SparseMatrixType m3(rows,rows);
     m3.reserve(VectorXi::Constant(rows,rows/2));
-    for(int j=0; j<rows; ++j)
+    for(Index j=0; j<rows; ++j)
-      for(int k=0; k<j; ++k)
+      for(Index k=0; k<j; ++k)
         m3.insertByOuterInner(j,k) = k+1;
-    for(int j=0; j<rows; ++j)
+    for(Index j=0; j<rows; ++j)
     {
       VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
       if(j>0)
         VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
     }
     m3.makeCompressed();
-    for(int j=0; j<rows; ++j)
+    for(Index j=0; j<rows; ++j)
     {
       VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
       if(j>0)
@@ -210,9 +211,9 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     initSparse<Scalar>(density, refMat2, m2);
     if(internal::random<float>(0,1)>0.5) m2.makeCompressed();
     
-    int j0 = internal::random<int>(0,rows-2);
+    Index j0 = internal::random<Index>(0,rows-2);
-    int j1 = internal::random<int>(0,rows-2);
+    Index j1 = internal::random<Index>(0,rows-2);
-    int n0 = internal::random<int>(1,rows-(std::max)(j0,j1));
+    Index n0 = internal::random<Index>(1,rows-(std::max)(j0,j1));
     if(SparseMatrixType::IsRowMajor)
       VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(j0,0,n0,cols));
     else
@@ -300,9 +301,9 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
     SparseMatrixType m2(rows, rows);
     initSparse<Scalar>(density, refMat2, m2);
-    int j0 = internal::random<int>(0,rows-2);
+    Index j0 = internal::random<Index>(0,rows-2);
-    int j1 = internal::random<int>(0,rows-2);
+    Index j1 = internal::random<Index>(0,rows-2);
-    int n0 = internal::random<int>(1,rows-(std::max)(j0,j1));
+    Index n0 = internal::random<Index>(1,rows-(std::max)(j0,j1));
     if(SparseMatrixType::IsRowMajor)
       VERIFY_IS_APPROX(m2.block(j0,0,n0,cols), refMat2.block(j0,0,n0,cols));
     else
@@ -315,7 +316,7 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
       VERIFY_IS_APPROX(m2.block(0,j0,rows,n0)+m2.block(0,j1,rows,n0),
                       refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
       
-    int i = internal::random<int>(0,m2.outerSize()-1);
+    Index i = internal::random<Index>(0,m2.outerSize()-1);
     if(SparseMatrixType::IsRowMajor) {
       m2.innerVector(i) = m2.innerVector(i) * s1;
       refMat2.row(i) = refMat2.row(i) * s1;
@@ -334,10 +335,10 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     refM2.setZero();
     int countFalseNonZero = 0;
     int countTrueNonZero = 0;
-    for (int j=0; j<m2.outerSize(); ++j)
+    for (Index j=0; j<m2.outerSize(); ++j)
     {
       m2.startVec(j);
-      for (int i=0; i<m2.innerSize(); ++i)
+      for (Index i=0; i<m2.innerSize(); ++i)
       {
         float x = internal::random<float>(0,1);
         if (x<0.1)
@@ -378,8 +379,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     refMat.setZero();
     for(int i=0;i<ntriplets;++i)
     {
-      int r = internal::random<int>(0,rows-1);
+      Index r = internal::random<Index>(0,rows-1);
-      int c = internal::random<int>(0,cols-1);
+      Index c = internal::random<Index>(0,cols-1);
       Scalar v = internal::random<Scalar>();
       triplets.push_back(TripletType(r,c,v));
       refMat(r,c) += v;
@@ -456,8 +457,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
       inc.push_back(std::pair<int,int>(0,3));
       
       for(size_t i = 0; i< inc.size(); i++) {
-        int incRows = inc[i].first;
+        Index incRows = inc[i].first;
-        int incCols = inc[i].second;
+        Index incCols = inc[i].second;
         SparseMatrixType m1(rows, cols);
         DenseMatrix refMat1 = DenseMatrix::Zero(rows, cols);
         initSparse<Scalar>(density, refMat1, m1);
@@ -502,7 +503,7 @@ void test_sparse_basic()
     CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,long int>(s, s)) ));
     CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,long int>(s, s)) ));
     
-    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(s, s)) ));
+    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(s), short(s))) ));
-    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(s, s)) ));
+    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(s), short(s))) ));
   }
 }

unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h

@@ -107,7 +107,7 @@ void lmpar2(const QRSolver &qr, const VectorType  &diag, const VectorType  &qtb,
   * http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
   */
 template<typename _FunctorType>
-class LevenbergMarquardt
+class LevenbergMarquardt : internal::no_assignment_operator
 {
   public:
     typedef _FunctorType FunctorType;