* suppressed some minor warnings

1 files changed, 12 insertions, 12 deletions

diff --git a/Eigen/src/SVD/SVD.h b/Eigen/src/SVD/SVD.h
index b8432c943..7041d16b5 100644
--- a/Eigen/src/SVD/SVD.h
+++ b/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);
         }