fix some compilation errors with ICC and -strict-ansi

5 files changed, 13 insertions, 13 deletions

diff --git a/Eigen/src/Core/DenseCoeffsBase.h b/Eigen/src/Core/DenseCoeffsBase.h
index e45238fb5..772c9e8bf 100644
--- a/Eigen/src/Core/DenseCoeffsBase.h
+++ b/Eigen/src/Core/DenseCoeffsBase.h
@@ -719,7 +719,7 @@ struct first_aligned_impl<Derived, false>
 {
   inline static typename Derived::Index run(const Derived& m)
   {
-    return first_aligned(&m.const_cast_derived().coeffRef(0,0), m.size());
+    return internal::first_aligned(&m.const_cast_derived().coeffRef(0,0), m.size());
   }
 };
 
diff --git a/Eigen/src/Core/products/SelfadjointMatrixVector.h b/Eigen/src/Core/products/SelfadjointMatrixVector.h
index 80ecd20f9..8cceb566f 100644
--- a/Eigen/src/Core/products/SelfadjointMatrixVector.h
+++ b/Eigen/src/Core/products/SelfadjointMatrixVector.h
@@ -99,7 +99,7 @@ static EIGEN_DONT_INLINE void run(
 
     size_t starti = FirstTriangular ? 0 : j+2;
     size_t endi   = FirstTriangular ? j : size;
-    size_t alignedStart = (starti) + first_aligned(&res[starti], endi-starti);
+    size_t alignedStart = (starti) + internal::first_aligned(&res[starti], endi-starti);
     size_t alignedEnd = alignedStart + ((endi-alignedStart)/(PacketSize))*(PacketSize);
 
     // TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed
diff --git a/Eigen/src/Eigen2Support/LU.h b/Eigen/src/Eigen2Support/LU.h
index c23c11baa..7cf92e67a 100644
--- a/Eigen/src/Eigen2Support/LU.h
+++ b/Eigen/src/Eigen2Support/LU.h
@@ -57,7 +57,7 @@ class LU : public FullPivLU<MatrixType>
     > ImageResultType;
 
     typedef FullPivLU<MatrixType> Base;
-    LU() : Base() {}
+    LU() : Base(), m_originalMatrix() {}
 
     template<typename T>
     explicit LU(const T& t) : Base(t), m_originalMatrix(t) {}
diff --git a/test/geo_transformations.cpp b/test/geo_transformations.cpp
index b606de2fb..3aa70a3af 100644
--- a/test/geo_transformations.cpp
+++ b/test/geo_transformations.cpp
@@ -284,9 +284,9 @@ template<typename Scalar, int Mode, int Options> void transformations()
   // mat * aligned scaling and mat * translation
   t1 = (Matrix3(q1) * AlignedScaling3(v0)) * Translation3(v0);
   VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
-  t1 = (Matrix3(q1) * Scaling(v0)) * Translation3(v0);
+  t1 = (Matrix3(q1) * Eigen::Scaling(v0)) * Translation3(v0);
   VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
-  t1 = (q1 * Scaling(v0)) * Translation3(v0);
+  t1 = (q1 * Eigen::Scaling(v0)) * Translation3(v0);
   VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
   // mat * transformation and aligned scaling * translation
   t1 = Matrix3(q1) * (AlignedScaling3(v0) * Translation3(v0));
@@ -295,18 +295,18 @@ template<typename Scalar, int Mode, int Options> void transformations()
 
   t0.setIdentity();
   t0.scale(s0).translate(v0);
-  t1 = Scaling(s0) * Translation3(v0);
+  t1 = Eigen::Scaling(s0) * Translation3(v0);
   VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
   t0.prescale(s0);
-  t1 = Scaling(s0) * t1;
+  t1 = Eigen::Scaling(s0) * t1;
   VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
   
   t0 = t3;
   t0.scale(s0);
-  t1 = t3 * Scaling(s0,s0,s0);
+  t1 = t3 * Eigen::Scaling(s0,s0,s0);
   VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
   t0.prescale(s0);
-  t1 = Scaling(s0,s0,s0) * t1;
+  t1 = Eigen::Scaling(s0,s0,s0) * t1;
   VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
 
 
diff --git a/test/sparse_basic.cpp b/test/sparse_basic.cpp
index 5b22876b0..637c5db51 100644
--- a/test/sparse_basic.cpp
+++ b/test/sparse_basic.cpp
@@ -335,11 +335,11 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     refMat.setZero();
     for(int i=0;i<ntriplets;++i)
     {
-      int i = internal::random<int>(0,rows-1);
-      int j = internal::random<int>(0,cols-1);
+      int r = internal::random<int>(0,rows-1);
+      int c = internal::random<int>(0,cols-1);
       Scalar v = internal::random<Scalar>();
-      triplets.push_back(TripletType(i,j,v));
-      refMat(i,j) += v;
+      triplets.push_back(TripletType(r,c,v));
+      refMat(r,c) += v;
     }
     SparseMatrixType m(rows,cols);
     m.setFromTriplets(triplets.begin(), triplets.end());