* suppressed some minor warnings

Eigen/src/Geometry/AngleAxis.h

@@ -147,7 +147,7 @@ public:
   inline explicit AngleAxis(const AngleAxis<OtherScalarType>& other)
   {
     m_axis = other.axis().template cast<Scalar>();
-    m_angle = other.angle();
+    m_angle = Scalar(other.angle());
   }
 
   /** \returns \c true if \c *this is approximately equal to \a other, within the precision

Eigen/src/Geometry/Quaternion.h

@@ -459,7 +459,7 @@ struct ei_quaternion_assign_impl<Other,3,3>
       int j = (i+1)%3;
       int k = (j+1)%3;
 
-      t = ei_sqrt(mat.coeff(i,i)-mat.coeff(j,j)-mat.coeff(k,k) + 1.0);
+      t = Scalar(ei_sqrt(mat.coeff(i,i)-mat.coeff(j,j)-mat.coeff(k,k) + 1.0));
       q.coeffs().coeffRef(i) = Scalar(0.5) * t;
       t = Scalar(0.5)/t;
       q.w() = (mat.coeff(k,j)-mat.coeff(j,k))*t;

Eigen/src/Geometry/Rotation2D.h

@@ -114,7 +114,7 @@ public:
   template<typename OtherScalarType>
   inline explicit Rotation2D(const Rotation2D<OtherScalarType>& other)
   {
-    m_angle = other.angle();
+    m_angle = Scalar(other.angle());
   }
 
   /** \returns \c true if \c *this is approximately equal to \a other, within the precision

Eigen/src/QR/EigenSolver.h

@@ -282,7 +282,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
   int n = nn-1;
   int low = 0;
   int high = nn-1;
-  Scalar eps = pow(2.0,-52.0);
+  Scalar eps = Scalar(pow(2.0,-52.0));
   Scalar exshift = 0.0;
   Scalar p=0,q=0,r=0,s=0,z=0,t,w,x,y;
 
@@ -328,7 +328,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
     else if (l == n-1) // Two roots found
     {
       w = matH.coeff(n,n-1) * matH.coeff(n-1,n);
-      p = (matH.coeff(n-1,n-1) - matH.coeff(n,n)) / 2.0;
+      p = Scalar((matH.coeff(n-1,n-1) - matH.coeff(n,n)) / 2.0);
       q = p * p + w;
       z = ei_sqrt(ei_abs(q));
       matH.coeffRef(n,n) = matH.coeff(n,n) + exshift;
@@ -405,25 +405,25 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
         for (int i = low; i <= n; ++i)
           matH.coeffRef(i,i) -= x;
         s = ei_abs(matH.coeff(n,n-1)) + ei_abs(matH.coeff(n-1,n-2));
-        x = y = 0.75 * s;
+        x = y = Scalar(0.75 * s);
-        w = -0.4375 * s * s;
+        w = Scalar(-0.4375 * s * s);
       }
 
       // MATLAB's new ad hoc shift
       if (iter == 30)
       {
-        s = (y - x) / 2.0;
+        s = Scalar((y - x) / 2.0);
         s = s * s + w;
         if (s > 0)
         {
           s = ei_sqrt(s);
           if (y < x)
             s = -s;
-          s = x - w / ((y - x) / 2.0 + s);
+          s = Scalar(x - w / ((y - x) / 2.0 + s));
           for (int i = low; i <= n; ++i)
             matH.coeffRef(i,i) -= s;
           exshift += s;
-          x = y = w = 0.964;
+          x = y = w = Scalar(0.964);
         }
       }
 
@@ -469,7 +469,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
         if (k != m) {
           p = matH.coeff(k,k-1);
           q = matH.coeff(k+1,k-1);
-          r = (notlast ? matH.coeff(k+2,k-1) : 0.0);
+          r = Scalar(notlast ? matH.coeff(k+2,k-1) : 0.0);
           x = ei_abs(p) + ei_abs(q) + ei_abs(r);
           if (x != 0.0)
           {
@@ -647,7 +647,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
             x = matH.coeff(i,i+1);
             y = matH.coeff(i+1,i);
             vr = (m_eivalues.coeff(i).real() - p) * (m_eivalues.coeff(i).real() - p) + m_eivalues.coeff(i).imag() * m_eivalues.coeff(i).imag() - q * q;
-            vi = (m_eivalues.coeff(i).real() - p) * 2.0 * q;
+            vi = Scalar((m_eivalues.coeff(i).real() - p) * 2.0 * q);
             if ((vr == 0.0) && (vi == 0.0))
               vr = eps * norm * (ei_abs(w) + ei_abs(q) + ei_abs(x) + ei_abs(y) + ei_abs(z));
 

Eigen/src/QR/SelfAdjointEigenSolver.h

@@ -334,7 +334,7 @@ MatrixBase<Derived>::operatorNorm() const
 template<typename RealScalar, typename Scalar>
 static void ei_tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, int start, int end, Scalar* matrixQ, int n)
 {
-  RealScalar td = (diag[end-1] - diag[end])*0.5;
+  RealScalar td = (diag[end-1] - diag[end])*RealScalar(0.5);
   RealScalar e2 = ei_abs2(subdiag[end-1]);
   RealScalar mu = diag[end] - e2 / (td + (td>0 ? 1 : -1) * ei_sqrt(td*td + e2));
   RealScalar x = diag[start] - mu;

Eigen/src/SVD/SVD.h

@@ -208,7 +208,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
           m_matU.col(j).end(m-k) += t * m_matU.col(k).end(m-k);
         }
         m_matU.col(k).end(m-k) = - m_matU.col(k).end(m-k);
-        m_matU(k,k) = 1.0 + m_matU(k,k);
+        m_matU(k,k) = Scalar(1) + m_matU(k,k);
         if (k-1>0)
           m_matU.col(k).start(k-1).setZero();
       }
@@ -242,7 +242,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
   // Main iteration loop for the singular values.
   int pp = p-1;
   int iter = 0;
-  Scalar eps(pow(2.0,-52.0));
+  Scalar eps(Scalar(pow(2.0,-52.0)));
   while (p > 0)
   {
     int k=0;
@@ -260,7 +260,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
     //              s(k), ..., s(p) are not negligible (qr step).
     // kase = 4     if e(p-1) is negligible (convergence).
 
-    for (k = p-2; k >= -1; k--)
+    for (k = p-2; k >= -1; --k)
     {
       if (k == -1)
           break;
@@ -277,11 +277,11 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
     else
     {
       int ks;
-      for (ks = p-1; ks >= k; ks--)
+      for (ks = p-1; ks >= k; --ks)
       {
         if (ks == k)
           break;
-        Scalar t( (ks != p ? ei_abs(e[ks]) : 0.) + (ks != k+1 ? ei_abs(e[ks-1]) : 0.));
+        Scalar t( Scalar((ks != p ? ei_abs(e[ks]) : 0.) + (ks != k+1 ? ei_abs(e[ks-1]) : 0.)) );
         if (ei_abs(m_sigma[ks]) <= eps*t)
         {
           m_sigma[ks] = 0.0;
@@ -313,9 +313,9 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
       {
         Scalar f(e[p-2]);
         e[p-2] = 0.0;
-        for (j = p-2; j >= k; j--)
+        for (j = p-2; j >= k; --j)
         {
-          Scalar t(hypot(m_sigma[j],f));
+          Scalar t(Scalar(hypot(m_sigma[j],f)));
           Scalar cs(m_sigma[j]/t);
           Scalar sn(f/t);
           m_sigma[j] = t;
@@ -344,7 +344,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
         e[k-1] = 0.0;
         for (j = k; j < p; ++j)
         {
-          Scalar t(hypot(m_sigma[j],f));
+          Scalar t(Scalar(hypot(m_sigma[j],f)));
           Scalar cs( m_sigma[j]/t);
           Scalar sn(f/t);
           m_sigma[j] = t;
@@ -375,7 +375,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
         Scalar epm1 = e[p-2]/scale;
         Scalar sk = m_sigma[k]/scale;
         Scalar ek = e[k]/scale;
-        Scalar b = ((spm1 + sp)*(spm1 - sp) + epm1*epm1)/2.0;
+        Scalar b = Scalar(((spm1 + sp)*(spm1 - sp) + epm1*epm1)/2.0);
         Scalar c = (sp*epm1)*(sp*epm1);
         Scalar shift = 0.0;
         if ((b != 0.0) || (c != 0.0))
@@ -392,7 +392,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
 
         for (j = k; j < p-1; ++j)
         {
-          Scalar t = hypot(f,g);
+          Scalar t = Scalar(hypot(f,g));
           Scalar cs = f/t;
           Scalar sn = g/t;
           if (j != k)
@@ -410,7 +410,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
               m_matV(i,j) = t;
             }
           }
-          t = hypot(f,g);
+          t = Scalar(hypot(f,g));
           cs = f/t;
           sn = g/t;
           m_sigma[j] = t;
@@ -439,7 +439,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
         // Make the singular values positive.
         if (m_sigma[k] <= 0.0)
         {
-          m_sigma[k] = (m_sigma[k] < 0.0 ? -m_sigma[k] : 0.0);
+          m_sigma[k] = Scalar((m_sigma[k] < 0.0 ? -m_sigma[k] : 0.0));
           if (wantv)
             m_matV.col(k).start(pp+1) = -m_matV.col(k).start(pp+1);
         }

Eigen/src/Sparse/SparseMatrix.h

@@ -361,14 +361,14 @@ class SparseMatrix
     {
       EIGEN_DBG_SPARSE(
         s << "Nonzero entries:\n";
-        for (unsigned int i=0; i<m.nonZeros(); ++i)
+        for (int i=0; i<m.nonZeros(); ++i)
         {
           s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
         }
         s << std::endl;
         s << std::endl;
         s << "Column pointers:\n";
-        for (unsigned int i=0; i<m.cols(); ++i)
+        for (int i=0; i<m.cols(); ++i)
         {
           s << m.m_outerIndex[i] << " ";
         }

Eigen/src/Sparse/SparseProduct.h

@@ -150,7 +150,7 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
     float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
 
     res.resize(rows, cols);
-    res.startFill(ratioRes*rows*cols);
+    res.startFill(int(ratioRes*rows*cols));
     for (int j=0; j<cols; ++j)
     {
       // let's do a more accurate determination of the nnz ratio for the current column j of res