the min/max macros to detect unprotected min/max were undefined by some std header,

so let's declare them after and do the respective fixes ;)

Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h

@@ -566,7 +566,7 @@ template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,3
   
     // map the matrix coefficients to [-1:1] to avoid over- and underflow.
     Scalar scale = mat.cwiseAbs().maxCoeff();
-    scale = std::max(scale,Scalar(1));
+    scale = (std::max)(scale,Scalar(1));
     MatrixType scaledMat = mat / scale;
 
     // Compute the eigenvalues
@@ -646,7 +646,7 @@ template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,2
   
     // map the matrix coefficients to [-1:1] to avoid over- and underflow.
     Scalar scale = mat.cwiseAbs().maxCoeff();
-    scale = std::max(scale,Scalar(1));
+    scale = (std::max)(scale,Scalar(1));
     MatrixType scaledMat = mat / scale;
     
     // Compute the eigenvalues

test/householder.cpp

@@ -48,7 +48,7 @@ template<typename MatrixType> void householder(const MatrixType& m)
   typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, Dynamic> VBlockMatrixType;
   typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::RowsAtCompileTime> TMatrixType;
   
-  Matrix<Scalar, EIGEN_SIZE_MAX(MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime), 1> _tmp(std::max(rows,cols));
+  Matrix<Scalar, EIGEN_SIZE_MAX(MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime), 1> _tmp((std::max)(rows,cols));
   Scalar* tmp = &_tmp.coeffRef(0,0);
 
   Scalar beta;

test/jacobisvd.cpp

@@ -66,7 +66,7 @@ void jacobisvd_compare_to_full(const MatrixType& m,
   typedef typename MatrixType::Index Index;
   Index rows = m.rows();
   Index cols = m.cols();
-  Index diagSize = std::min(rows, cols);
+  Index diagSize = (std::min)(rows, cols);
 
   JacobiSVD<MatrixType, QRPreconditioner> svd(m, computationOptions);
 

test/lu.cpp

@@ -64,7 +64,7 @@ template<typename MatrixType> void lu_non_invertible()
   typedef Matrix<typename MatrixType::Scalar, RowsAtCompileTime, RowsAtCompileTime>
           RMatrixType;
 
-  Index rank = internal::random<Index>(1, std::min(rows, cols)-1);
+  Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1);
 
   // The image of the zero matrix should consist of a single (zero) column vector
   VERIFY((MatrixType::Zero(rows,cols).fullPivLu().image(MatrixType::Zero(rows,cols)).cols() == 1));
@@ -84,8 +84,8 @@ template<typename MatrixType> void lu_non_invertible()
   MatrixType u(rows,cols);
   u = lu.matrixLU().template triangularView<Upper>();
   RMatrixType l = RMatrixType::Identity(rows,rows);
-  l.block(0,0,rows,std::min(rows,cols)).template triangularView<StrictlyLower>()
+  l.block(0,0,rows,(std::min)(rows,cols)).template triangularView<StrictlyLower>()
-    = lu.matrixLU().block(0,0,rows,std::min(rows,cols));
+    = lu.matrixLU().block(0,0,rows,(std::min)(rows,cols));
 
   VERIFY_IS_APPROX(lu.permutationP() * m1 * lu.permutationQ(), l*u);
 

test/main.h

@@ -23,9 +23,6 @@
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 
-#define min(A,B) please_protect_your_min_with_parentheses
-#define max(A,B) please_protect_your_max_with_parentheses
-
 #include <cstdlib>
 #include <cerrno>
 #include <ctime>
@@ -33,6 +30,15 @@
 #include <string>
 #include <vector>
 #include <typeinfo>
+#include <limits>
+#include <algorithm>
+#include <sstream>
+#include <complex>
+#include <deque>
+#include <queue>
+
+#define min(A,B) please_protect_your_min_with_parentheses
+#define max(A,B) please_protect_your_max_with_parentheses
 
 // the following file is automatically generated by cmake
 #include "split_test_helper.h"

test/nullary.cpp

@@ -38,7 +38,7 @@ bool equalsIdentity(const MatrixType& A)
     }
   }
   for (Index i = 0; i < A.rows(); ++i) {
-    for (Index j = 0; j < std::min(i, A.cols()); ++j) {
+    for (Index j = 0; j < (std::min)(i, A.cols()); ++j) {
       offDiagOK = offDiagOK && (A(i,j) == zero);
     }
   }

test/packetmath.cpp

@@ -128,7 +128,7 @@ template<typename Scalar> void packetmath()
   {
     data1[i] = internal::random<Scalar>()/RealScalar(PacketSize);
     data2[i] = internal::random<Scalar>()/RealScalar(PacketSize);
-    refvalue = std::max(refvalue,internal::abs(data1[i]));
+    refvalue = (std::max)(refvalue,internal::abs(data1[i]));
   }
 
   internal::pstore(data2, internal::pload<Packet>(data1));
@@ -264,16 +264,16 @@ template<typename Scalar> void packetmath_real()
 
   ref[0] = data1[0];
   for (int i=0; i<PacketSize; ++i)
-    ref[0] = std::min(ref[0],data1[i]);
+    ref[0] = (std::min)(ref[0],data1[i]);
   VERIFY(internal::isApprox(ref[0], internal::predux_min(internal::pload<Packet>(data1))) && "internal::predux_min");
 
-  CHECK_CWISE2(std::min, internal::pmin);
+  CHECK_CWISE2((std::min), internal::pmin);
-  CHECK_CWISE2(std::max, internal::pmax);
+  CHECK_CWISE2((std::max), internal::pmax);
   CHECK_CWISE1(internal::abs, internal::pabs);
 
   ref[0] = data1[0];
   for (int i=0; i<PacketSize; ++i)
-    ref[0] = std::max(ref[0],data1[i]);
+    ref[0] = (std::max)(ref[0],data1[i]);
   VERIFY(internal::isApprox(ref[0], internal::predux_max(internal::pload<Packet>(data1))) && "internal::predux_max");
   
   for (int i=0; i<PacketSize; ++i)

test/prec_inverse_4x4.cpp

@@ -58,7 +58,7 @@ template<typename MatrixType> void inverse_general_4x4(int repeat)
     MatrixType inv = m.inverse();
     double error = double( (m*inv-MatrixType::Identity()).norm() * absdet / NumTraits<Scalar>::epsilon() );
     error_sum += error;
-    error_max = std::max(error_max, error);
+    error_max = (std::max)(error_max, error);
   }
   std::cerr << "inverse_general_4x4, Scalar = " << type_name<Scalar>() << std::endl;
   double error_avg = error_sum / repeat;

test/product.h

@@ -29,7 +29,7 @@ template<typename Derived1, typename Derived2>
 bool areNotApprox(const MatrixBase<Derived1>& m1, const MatrixBase<Derived2>& m2, typename Derived1::RealScalar epsilon = NumTraits<typename Derived1::RealScalar>::dummy_precision())
 {
   return !((m1-m2).cwiseAbs2().maxCoeff() < epsilon * epsilon
-                          * std::max(m1.cwiseAbs2().maxCoeff(), m2.cwiseAbs2().maxCoeff()));
+                          * (std::max)(m1.cwiseAbs2().maxCoeff(), m2.cwiseAbs2().maxCoeff()));
 }
 
 template<typename MatrixType> void product(const MatrixType& m)
@@ -102,7 +102,7 @@ template<typename MatrixType> void product(const MatrixType& m)
 
   // test the previous tests were not screwed up because operator* returns 0
   // (we use the more accurate default epsilon)
-  if (!NumTraits<Scalar>::IsInteger && std::min(rows,cols)>1)
+  if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1)
   {
     VERIFY(areNotApprox(m1.transpose()*m2,m2.transpose()*m1));
   }
@@ -111,7 +111,7 @@ template<typename MatrixType> void product(const MatrixType& m)
   res = square;
   res.noalias() += m1 * m2.transpose();
   VERIFY_IS_APPROX(res, square + m1 * m2.transpose());
-  if (!NumTraits<Scalar>::IsInteger && std::min(rows,cols)>1)
+  if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1)
   {
     VERIFY(areNotApprox(res,square + m2 * m1.transpose()));
   }
@@ -123,7 +123,7 @@ template<typename MatrixType> void product(const MatrixType& m)
   res = square;
   res.noalias() -= m1 * m2.transpose();
   VERIFY_IS_APPROX(res, square - (m1 * m2.transpose()));
-  if (!NumTraits<Scalar>::IsInteger && std::min(rows,cols)>1)
+  if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1)
   {
     VERIFY(areNotApprox(res,square - m2 * m1.transpose()));
   }
@@ -147,7 +147,7 @@ template<typename MatrixType> void product(const MatrixType& m)
   res2 = square2;
   res2.noalias() += m1.transpose() * m2;
   VERIFY_IS_APPROX(res2, square2 + m1.transpose() * m2);
-  if (!NumTraits<Scalar>::IsInteger && std::min(rows,cols)>1)
+  if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1)
   {
     VERIFY(areNotApprox(res2,square2 + m2.transpose() * m1));
   }

test/qr_colpivoting.cpp

@@ -31,7 +31,7 @@ template<typename MatrixType> void qr()
   typedef typename MatrixType::Index Index;
 
   Index rows = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE), cols = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE), cols2 = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE);
-  Index rank = internal::random<Index>(1, std::min(rows, cols)-1);
+  Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1);
 
   typedef typename MatrixType::Scalar Scalar;
   typedef typename MatrixType::RealScalar RealScalar;
@@ -64,7 +64,7 @@ template<typename MatrixType, int Cols2> void qr_fixedsize()
 {
   enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime };
   typedef typename MatrixType::Scalar Scalar;
-  int rank = internal::random<int>(1, std::min(int(Rows), int(Cols))-1);
+  int rank = internal::random<int>(1, (std::min)(int(Rows), int(Cols))-1);
   Matrix<Scalar,Rows,Cols> m1;
   createRandomPIMatrixOfRank(rank,Rows,Cols,m1);
   ColPivHouseholderQR<Matrix<Scalar,Rows,Cols> > qr(m1);

test/qr_fullpivoting.cpp

@@ -31,7 +31,7 @@ template<typename MatrixType> void qr()
   typedef typename MatrixType::Index Index;
 
   Index rows = internal::random<Index>(20,200), cols = internal::random<int>(20,200), cols2 = internal::random<int>(20,200);
-  Index rank = internal::random<Index>(1, std::min(rows, cols)-1);
+  Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1);
 
   typedef typename MatrixType::Scalar Scalar;
   typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> MatrixQType;

test/redux.cpp

@@ -43,8 +43,8 @@ template<typename MatrixType> void matrixRedux(const MatrixType& m)
   {
     s += m1(i,j);
     p *= m1(i,j);
-    minc = std::min(internal::real(minc), internal::real(m1(i,j)));
+    minc = (std::min)(internal::real(minc), internal::real(m1(i,j)));
-    maxc = std::max(internal::real(maxc), internal::real(m1(i,j)));
+    maxc = (std::max)(internal::real(maxc), internal::real(m1(i,j)));
   }
   const Scalar mean = s/Scalar(RealScalar(rows*cols));
 
@@ -86,8 +86,8 @@ template<typename VectorType> void vectorRedux(const VectorType& w)
     {
       s += v[j];
       p *= v[j];
-      minc = std::min(minc, internal::real(v[j]));
+      minc = (std::min)(minc, internal::real(v[j]));
-      maxc = std::max(maxc, internal::real(v[j]));
+      maxc = (std::max)(maxc, internal::real(v[j]));
     }
     VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.head(i).sum()), Scalar(1));
     VERIFY_IS_APPROX(p, v.head(i).prod());
@@ -103,8 +103,8 @@ template<typename VectorType> void vectorRedux(const VectorType& w)
     {
       s += v[j];
       p *= v[j];
-      minc = std::min(minc, internal::real(v[j]));
+      minc = (std::min)(minc, internal::real(v[j]));
-      maxc = std::max(maxc, internal::real(v[j]));
+      maxc = (std::max)(maxc, internal::real(v[j]));
     }
     VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.tail(size-i).sum()), Scalar(1));
     VERIFY_IS_APPROX(p, v.tail(size-i).prod());
@@ -120,8 +120,8 @@ template<typename VectorType> void vectorRedux(const VectorType& w)
     {
       s += v[j];
       p *= v[j];
-      minc = std::min(minc, internal::real(v[j]));
+      minc = (std::min)(minc, internal::real(v[j]));
-      maxc = std::max(maxc, internal::real(v[j]));
+      maxc = (std::max)(maxc, internal::real(v[j]));
     }
     VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
     VERIFY_IS_APPROX(p, v.segment(i, size-2*i).prod());
@@ -140,7 +140,7 @@ template<typename VectorType> void vectorRedux(const VectorType& w)
 void test_redux()
 {
   // the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
-  int maxsize = std::min(100,EIGEN_TEST_MAX_SIZE);
+  int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
   EIGEN_UNUSED_VARIABLE(maxsize);
   for(int i = 0; i < g_repeat; i++) {
     CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );

test/sparse.h

@@ -29,6 +29,15 @@
 #include "main.h"
 
 #if EIGEN_GNUC_AT_LEAST(4,0) && !defined __ICC && !defined(__clang__)
+
+#ifdef min
+#undef min
+#endif
+
+#ifdef max
+#undef max
+#endif
+
 #include <tr1/unordered_map>
 #define EIGEN_UNORDERED_MAP_SUPPORT
 namespace std {

test/sparse_basic.cpp

@@ -34,7 +34,7 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
   typedef typename SparseMatrixType::Scalar Scalar;
   enum { Flags = SparseMatrixType::Flags };
 
-  double density = std::max(8./(rows*cols), 0.01);
+  double density = (std::max)(8./(rows*cols), 0.01);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
   Scalar eps = 1e-6;
@@ -207,7 +207,7 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     initSparse<Scalar>(density, refMat2, m2);
     int j0 = internal::random<int>(0,rows-2);
     int j1 = internal::random<int>(0,rows-2);
-    int n0 = internal::random<int>(1,rows-std::max(j0,j1));
+    int n0 = internal::random<int>(1,rows-(std::max)(j0,j1));
     VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0));
     VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
                      refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));

test/sparse_product.cpp

@@ -58,7 +58,7 @@ template<typename SparseMatrixType> void sparse_product()
   typedef typename SparseMatrixType::Scalar Scalar;
   enum { Flags = SparseMatrixType::Flags };
 
-  double density = std::max(8./(rows*cols), 0.01);
+  double density = (std::max)(8./(rows*cols), 0.01);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
 

test/sparse_solvers.cpp

@@ -47,7 +47,7 @@ initSPD(double density,
 
 template<typename Scalar> void sparse_solvers(int rows, int cols)
 {
-  double density = std::max(8./(rows*cols), 0.01);
+  double density = (std::max)(8./(rows*cols), 0.01);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
   // Scalar eps = 1e-6;

test/sparse_vector.cpp

@@ -26,8 +26,8 @@
 
 template<typename Scalar> void sparse_vector(int rows, int cols)
 {
-  double densityMat = std::max(8./(rows*cols), 0.01);
+  double densityMat = (std::max)(8./(rows*cols), 0.01);
-  double densityVec = std::max(8./float(rows), 0.1);
+  double densityVec = (std::max)(8./float(rows), 0.1);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
   typedef SparseVector<Scalar> SparseVectorType;

test/stable_norm.cpp

@@ -68,8 +68,8 @@ template<typename MatrixType> void stable_norm(const MatrixType& m)
   Index rows = m.rows();
   Index cols = m.cols();
 
-  Scalar big = internal::random<Scalar>() * (std::numeric_limits<RealScalar>::max() * RealScalar(1e-4));
+  Scalar big = internal::random<Scalar>() * ((std::numeric_limits<RealScalar>::max)() * RealScalar(1e-4));
-  Scalar small = internal::random<Scalar>() * (std::numeric_limits<RealScalar>::min() * RealScalar(1e4));
+  Scalar small = internal::random<Scalar>() * ((std::numeric_limits<RealScalar>::min)() * RealScalar(1e4));
 
   MatrixType  vzero = MatrixType::Zero(rows, cols),
               vrand = MatrixType::Random(rows, cols),

test/triangular.cpp

@@ -242,7 +242,7 @@ void bug_159()
 
 void test_triangular()
 {
-  int maxsize = std::min(EIGEN_TEST_MAX_SIZE,20);
+  int maxsize = (std::min)(EIGEN_TEST_MAX_SIZE,20);
   for(int i = 0; i < g_repeat ; i++)
   {
     int r = internal::random<int>(2,maxsize); EIGEN_UNUSED_VARIABLE(r);

unsupported/Eigen/FFT

@@ -331,7 +331,7 @@ class FFT
         // if the vector is strided, then we need to copy it to a packed temporary
         Matrix<src_type,1,Dynamic> tmp;
         if ( resize_input ) {
-          size_t ncopy = std::min(src.size(),src.size() + resize_input);
+          size_t ncopy = (std::min)(src.size(),src.size() + resize_input);
           tmp.setZero(src.size() + resize_input);
           if ( realfft && HasFlag(HalfSpectrum) ) {
             // pad at the Nyquist bin

unsupported/Eigen/src/BVH/BVAlgorithms.h

@@ -231,7 +231,7 @@ private:
 template<typename BVH, typename Minimizer>
 typename Minimizer::Scalar BVMinimize(const BVH &tree, Minimizer &minimizer)
 {
-  return internal::minimize_helper(tree, minimizer, tree.getRootIndex(), std::numeric_limits<typename Minimizer::Scalar>::max());
+  return internal::minimize_helper(tree, minimizer, tree.getRootIndex(), (std::numeric_limits<typename Minimizer::Scalar>::max)());
 }
 
 /**  Given two BVH's, runs the query on their cartesian product encapsulated by \a minimizer.
@@ -264,7 +264,7 @@ typename Minimizer::Scalar BVMinimize(const BVH1 &tree1, const BVH2 &tree2, Mini
   ObjIter2 oBegin2 = ObjIter2(), oEnd2 = ObjIter2(), oCur2 = ObjIter2();
   std::priority_queue<QueueElement, std::vector<QueueElement>, std::greater<QueueElement> > todo; //smallest is at the top
 
-  Scalar minimum = std::numeric_limits<Scalar>::max();
+  Scalar minimum = (std::numeric_limits<Scalar>::max)();
   todo.push(std::make_pair(Scalar(), std::make_pair(tree1.getRootIndex(), tree2.getRootIndex())));
 
   while(!todo.empty()) {

unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h

@@ -259,7 +259,7 @@ void MatrixExponential<MatrixType>::computeUV(float)
     pade5(m_M);
   } else {
     const float maxnorm = 3.925724783138660f;
-    m_squarings = max(0, (int)ceil(log2(m_l1norm / maxnorm)));
+    m_squarings = (max)(0, (int)ceil(log2(m_l1norm / maxnorm)));
     MatrixType A = m_M / pow(Scalar(2), Scalar(static_cast<RealScalar>(m_squarings)));
     pade7(A);
   }
@@ -281,7 +281,7 @@ void MatrixExponential<MatrixType>::computeUV(double)
     pade9(m_M);
   } else {
     const double maxnorm = 5.371920351148152;
-    m_squarings = max(0, (int)ceil(log2(m_l1norm / maxnorm)));
+    m_squarings = (max)(0, (int)ceil(log2(m_l1norm / maxnorm)));
     MatrixType A = m_M / pow(Scalar(2), Scalar(m_squarings));
     pade13(A);
   }

unsupported/test/BVH.cpp

@@ -90,13 +90,13 @@ struct BallPointStuff //this class provides functions to be both an intersector
   }
 
   double minimumOnVolume(const BoxType &r) { ++calls; return r.squaredExteriorDistance(p); }
-  double minimumOnObject(const BallType &b) { ++calls; return std::max(0., (b.center - p).squaredNorm() - SQR(b.radius)); }
+  double minimumOnObject(const BallType &b) { ++calls; return (std::max)(0., (b.center - p).squaredNorm() - SQR(b.radius)); }
   double minimumOnVolumeVolume(const BoxType &r1, const BoxType &r2) { ++calls; return r1.squaredExteriorDistance(r2); }
-  double minimumOnVolumeObject(const BoxType &r, const BallType &b) { ++calls; return SQR(std::max(0., r.exteriorDistance(b.center) - b.radius)); }
+  double minimumOnVolumeObject(const BoxType &r, const BallType &b) { ++calls; return SQR((std::max)(0., r.exteriorDistance(b.center) - b.radius)); }
-  double minimumOnObjectVolume(const BallType &b, const BoxType &r) { ++calls; return SQR(std::max(0., r.exteriorDistance(b.center) - b.radius)); }
+  double minimumOnObjectVolume(const BallType &b, const BoxType &r) { ++calls; return SQR((std::max)(0., r.exteriorDistance(b.center) - b.radius)); }
-  double minimumOnObjectObject(const BallType &b1, const BallType &b2){ ++calls; return SQR(std::max(0., (b1.center - b2.center).norm() - b1.radius - b2.radius)); }
+  double minimumOnObjectObject(const BallType &b1, const BallType &b2){ ++calls; return SQR((std::max)(0., (b1.center - b2.center).norm() - b1.radius - b2.radius)); }
   double minimumOnVolumeObject(const BoxType &r, const VectorType &v) { ++calls; return r.squaredExteriorDistance(v); }
-  double minimumOnObjectObject(const BallType &b, const VectorType &v){ ++calls; return SQR(std::max(0., (b.center - v).norm() - b.radius)); }
+  double minimumOnObjectObject(const BallType &b, const VectorType &v){ ++calls; return SQR((std::max)(0., (b.center - v).norm() - b.radius)); }
 
   VectorType p;
   int calls;

unsupported/test/cg.cpp

@@ -27,7 +27,7 @@
 
 template<typename Scalar,typename Index> void cg(int size)
 {
-  double density = std::max(8./(size*size), 0.01);
+  double density = (std::max)(8./(size*size), 0.01);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
   typedef SparseMatrix<Scalar,ColMajor,Index> SparseMatrixType;  

unsupported/test/matrix_exponential.cpp

@@ -36,7 +36,7 @@ double binom(int n, int k)
 template <typename Derived, typename OtherDerived>
 double relerr(const MatrixBase<Derived>& A, const MatrixBase<OtherDerived>& B)
 {
-  return std::sqrt((A - B).cwiseAbs2().sum() / std::min(A.cwiseAbs2().sum(), B.cwiseAbs2().sum()));
+  return std::sqrt((A - B).cwiseAbs2().sum() / (std::min)(A.cwiseAbs2().sum(), B.cwiseAbs2().sum()));
 }
 
 template <typename T>

unsupported/test/sparse_extra.cpp

@@ -67,7 +67,7 @@ template<typename SparseMatrixType> void sparse_extra(const SparseMatrixType& re
   typedef typename SparseMatrixType::Scalar Scalar;
   enum { Flags = SparseMatrixType::Flags };
 
-  double density = std::max(8./(rows*cols), 0.01);
+  double density = (std::max)(8./(rows*cols), 0.01);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
   Scalar eps = 1e-6;

unsupported/test/sparse_ldlt.cpp

@@ -33,7 +33,7 @@ template<typename Scalar,typename Index> void sparse_ldlt(int rows, int cols)
 {
   static bool odd = true;
   odd = !odd;
-  double density = std::max(8./(rows*cols), 0.01);
+  double density = (std::max)(8./(rows*cols), 0.01);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
   typedef SparseMatrix<Scalar,ColMajor,Index> SparseMatrixType;

unsupported/test/sparse_llt.cpp

@@ -31,7 +31,7 @@
 
 template<typename Scalar,typename Index> void sparse_llt(int rows, int cols)
 {
-  double density = std::max(8./(rows*cols), 0.01);
+  double density = (std::max)(8./(rows*cols), 0.01);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
   typedef SparseMatrix<Scalar,ColMajor,Index> SparseMatrixType;

unsupported/test/sparse_lu.cpp

@@ -35,7 +35,7 @@
 
 template<typename Scalar> void sparse_lu(int rows, int cols)
 {
-  double density = std::max(8./(rows*cols), 0.01);
+  double density = (std::max)(8./(rows*cols), 0.01);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;