diff --git a/bazaar/plugin/Eigen/Eigen/Core b/bazaar/plugin/Eigen/Eigen/Core
index 2ad4dee25..6e855427c 100644
--- a/bazaar/plugin/Eigen/Eigen/Core
+++ b/bazaar/plugin/Eigen/Eigen/Core
@@ -175,13 +175,6 @@
   #include <new>
 #endif
 
-// this needs to be done after all possible windows C header includes and before any Eigen source includes
-// (system C++ includes are supposed to be able to deal with this already):
-// windows.h defines min and max macros which would make Eigen fail to compile.
-#if defined(min) || defined(max)
-#error The preprocessor symbols 'min' or 'max' are defined. If you are compiling on Windows, do #define NOMINMAX to prevent windows.h from defining these symbols.
-#endif
-
 // defined in bits/termios.h
 #undef B0
 
diff --git a/bazaar/plugin/Eigen/Eigen/src/Cholesky/LLT.h b/bazaar/plugin/Eigen/Eigen/src/Cholesky/LLT.h
index a8fc525e8..a4ee5b11c 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Cholesky/LLT.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Cholesky/LLT.h
@@ -233,7 +233,7 @@ template<> struct llt_inplace<Lower>
 
     Index blockSize = size/8;
     blockSize = (blockSize/16)*16;
-    blockSize = std::min(std::max(blockSize,Index(8)), Index(128));
+    blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128));
 
     for (Index k=0; k<size; k+=blockSize)
     {
@@ -241,7 +241,7 @@ template<> struct llt_inplace<Lower>
       //       A00 |  -  |  -
       // lu  = A10 | A11 |  -
       //       A20 | A21 | A22
-      Index bs = std::min(blockSize, size-k);
+      Index bs = (std::min)(blockSize, size-k);
       Index rs = size - k - bs;
       Block<MatrixType,Dynamic,Dynamic> A11(m,k,   k,   bs,bs);
       Block<MatrixType,Dynamic,Dynamic> A21(m,k+bs,k,   rs,bs);
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/BandMatrix.h b/bazaar/plugin/Eigen/Eigen/src/Core/BandMatrix.h
index dda8efba3..2570d7b55 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/BandMatrix.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/BandMatrix.h
@@ -87,7 +87,7 @@ class BandMatrixBase : public EigenBase<Derived>
       if (i<=supers())
       {
         start = supers()-i;
-        len = std::min(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i)));
+        len = (std::min)(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i)));
       }
       else if (i>=rows()-subs())
         len = std::max<Index>(0,coeffs().rows() - (i + 1 - rows() + subs()));
@@ -96,11 +96,11 @@ class BandMatrixBase : public EigenBase<Derived>
 
     /** \returns a vector expression of the main diagonal */
     inline Block<CoefficientsType,1,SizeAtCompileTime> diagonal()
-    { return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,std::min(rows(),cols())); }
+    { return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
 
     /** \returns a vector expression of the main diagonal (const version) */
     inline const Block<const CoefficientsType,1,SizeAtCompileTime> diagonal() const
-    { return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,std::min(rows(),cols())); }
+    { return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
 
     template<int Index> struct DiagonalIntReturnType {
       enum {
@@ -122,13 +122,13 @@ class BandMatrixBase : public EigenBase<Derived>
     /** \returns a vector expression of the \a N -th sub or super diagonal */
     template<int N> inline typename DiagonalIntReturnType<N>::Type diagonal()
     {
-      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, std::max(0,N), 1, diagonalLength(N));
+      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
     }
 
     /** \returns a vector expression of the \a N -th sub or super diagonal */
     template<int N> inline const typename DiagonalIntReturnType<N>::Type diagonal() const
     {
-      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, std::max(0,N), 1, diagonalLength(N));
+      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
     }
 
     /** \returns a vector expression of the \a i -th sub or super diagonal */
@@ -166,7 +166,7 @@ class BandMatrixBase : public EigenBase<Derived>
   protected:
 
     inline Index diagonalLength(Index i) const
-    { return i<0 ? std::min(cols(),rows()+i) : std::min(rows(),cols()-i); }
+    { return i<0 ? (std::min)(cols(),rows()+i) : (std::min)(rows(),cols()-i); }
 };
 
 /**
@@ -284,6 +284,7 @@ class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsT
       : m_coeffs(coeffs),
         m_rows(rows), m_supers(supers), m_subs(subs)
     {
+      EIGEN_UNUSED_VARIABLE(cols);
       //internal::assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
     }
 
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/CwiseNullaryOp.h b/bazaar/plugin/Eigen/Eigen/src/Core/CwiseNullaryOp.h
index a2f504985..c616e7ae1 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/CwiseNullaryOp.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/CwiseNullaryOp.h
@@ -742,7 +742,7 @@ struct setIdentity_impl<Derived, true>
   static EIGEN_STRONG_INLINE Derived& run(Derived& m)
   {
     m.setZero();
-    const Index size = std::min(m.rows(), m.cols());
+    const Index size = (std::min)(m.rows(), m.cols());
     for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1);
     return m;
   }
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/Diagonal.h b/bazaar/plugin/Eigen/Eigen/src/Core/Diagonal.h
index e807a49e4..61d3b063a 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/Diagonal.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/Diagonal.h
@@ -87,7 +87,7 @@ template<typename MatrixType, int DiagIndex> class Diagonal
     EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
 
     inline Index rows() const
-    { return m_index.value()<0 ? std::min(m_matrix.cols(),m_matrix.rows()+m_index.value()) : std::min(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
+    { return m_index.value()<0 ? (std::min)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min)(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
 
     inline Index cols() const { return 1; }
 
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/Dot.h b/bazaar/plugin/Eigen/Eigen/src/Core/Dot.h
index 6e83191c5..42da78498 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/Dot.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/Dot.h
@@ -116,7 +116,9 @@ MatrixBase<Derived>::eigen2_dot(const MatrixBase<OtherDerived>& other) const
 
 //---------- implementation of L2 norm and related functions ----------
 
-/** \returns the squared \em l2 norm of *this, i.e., for vectors, the dot product of *this with itself.
+/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the Frobenius norm.
+  * In both cases, it consists in the sum of the square of all the matrix entries.
+  * For vectors, this is also equals to the dot product of \c *this with itself.
   *
   * \sa dot(), norm()
   */
@@ -126,7 +128,9 @@ EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scala
   return internal::real((*this).cwiseAbs2().sum());
 }
 
-/** \returns the \em l2 norm of *this, i.e., for vectors, the square root of the dot product of *this with itself.
+/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
+  * In both cases, it consists in the square root of the sum of the square of all the matrix entries.
+  * For vectors, this is also equals to the square root of the dot product of \c *this with itself.
   *
   * \sa dot(), squaredNorm()
   */
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/Functors.h b/bazaar/plugin/Eigen/Eigen/src/Core/Functors.h
index e319c978e..54636e0d4 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/Functors.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/Functors.h
@@ -116,7 +116,7 @@ struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > {
   */
 template<typename Scalar> struct scalar_min_op {
   EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op)
-  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::min; return min(a, b); }
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::min; return (min)(a, b); }
   template<typename Packet>
   EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
   { return internal::pmin(a,b); }
@@ -139,7 +139,7 @@ struct functor_traits<scalar_min_op<Scalar> > {
   */
 template<typename Scalar> struct scalar_max_op {
   EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op)
-  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::max; return max(a, b); }
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::max; return (max)(a, b); }
   template<typename Packet>
   EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
   { return internal::pmax(a,b); }
@@ -167,8 +167,8 @@ template<typename Scalar> struct scalar_hypot_op {
   {
     using std::max;
     using std::min;
-    Scalar p = max(_x, _y);
-    Scalar q = min(_x, _y);
+    Scalar p = (max)(_x, _y);
+    Scalar q = (min)(_x, _y);
     Scalar qp = q/p;
     return p * sqrt(Scalar(1) + qp*qp);
   }
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/Fuzzy.h b/bazaar/plugin/Eigen/Eigen/src/Core/Fuzzy.h
index 6eaa26be8..1926d6ab4 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/Fuzzy.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/Fuzzy.h
@@ -37,7 +37,7 @@ struct isApprox_selector
     using std::min;
     const typename internal::nested<Derived,2>::type nested(x);
     const typename internal::nested<OtherDerived,2>::type otherNested(y);
-    return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * min(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
+    return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * (min)(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
   }
 };
 
@@ -94,7 +94,7 @@ struct isMuchSmallerThan_scalar_selector<Derived, true>
   *
   * \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$
   * are considered to be approximately equal within precision \f$ p \f$ if
-  * \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f]
+  * \f[ \Vert v - w \Vert \leqslant p\,\(min)(\Vert v\Vert, \Vert w\Vert). \f]
   * For matrices, the comparison is done using the Hilbert-Schmidt norm (aka Frobenius norm
   * L2 norm).
   *
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/GenericPacketMath.h b/bazaar/plugin/Eigen/Eigen/src/Core/GenericPacketMath.h
index 1366a63c1..8ed835327 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/GenericPacketMath.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/GenericPacketMath.h
@@ -134,12 +134,12 @@ pdiv(const Packet& a,
 /** \internal \returns the min of \a a and \a b  (coeff-wise) */
 template<typename Packet> inline Packet
 pmin(const Packet& a,
-        const Packet& b) { using std::min; return min(a, b); }
+        const Packet& b) { using std::min; return (min)(a, b); }
 
 /** \internal \returns the max of \a a and \a b  (coeff-wise) */
 template<typename Packet> inline Packet
 pmax(const Packet& a,
-        const Packet& b) { using std::max; return max(a, b); }
+        const Packet& b) { using std::max; return (max)(a, b); }
 
 /** \internal \returns the absolute value of \a a */
 template<typename Packet> inline Packet
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/MathFunctions.h b/bazaar/plugin/Eigen/Eigen/src/Core/MathFunctions.h
index c80d30e35..2b454db21 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/MathFunctions.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/MathFunctions.h
@@ -378,8 +378,8 @@ struct hypot_impl
     using std::min;
     RealScalar _x = abs(x);
     RealScalar _y = abs(y);
-    RealScalar p = max(_x, _y);
-    RealScalar q = min(_x, _y);
+    RealScalar p = (max)(_x, _y);
+    RealScalar q = (min)(_x, _y);
     RealScalar qp = q/p;
     return p * sqrt(RealScalar(1) + qp*qp);
   }
@@ -737,7 +737,7 @@ struct scalar_fuzzy_default_impl<Scalar, false, false>
   static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
   {
     using std::min;
-    return abs(x - y) <= min(abs(x), abs(y)) * prec;
+    return abs(x - y) <= (min)(abs(x), abs(y)) * prec;
   }
   static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar& prec)
   {
@@ -776,7 +776,7 @@ struct scalar_fuzzy_default_impl<Scalar, true, false>
   static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
   {
     using std::min;
-    return abs2(x - y) <= min(abs2(x), abs2(y)) * prec * prec;
+    return abs2(x - y) <= (min)(abs2(x), abs2(y)) * prec * prec;
   }
 };
 
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/MatrixBase.h b/bazaar/plugin/Eigen/Eigen/src/Core/MatrixBase.h
index f0c7fc7a1..db156f6e9 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/MatrixBase.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/MatrixBase.h
@@ -111,7 +111,7 @@ template<typename Derived> class MatrixBase
 
     /** \returns the size of the main diagonal, which is min(rows(),cols()).
       * \sa rows(), cols(), SizeAtCompileTime. */
-    inline Index diagonalSize() const { return std::min(rows(),cols()); }
+    inline Index diagonalSize() const { return (std::min)(rows(),cols()); }
 
     /** \brief The plain matrix type corresponding to this expression.
       *
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/NumTraits.h b/bazaar/plugin/Eigen/Eigen/src/Core/NumTraits.h
index 5c7762dae..73ef05dfe 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/NumTraits.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/NumTraits.h
@@ -87,8 +87,8 @@ template<typename T> struct GenericNumTraits
     // make sure to override this for floating-point types
     return Real(0);
   }
-  inline static T highest() { return std::numeric_limits<T>::max(); }
-  inline static T lowest()  { return IsInteger ? std::numeric_limits<T>::min() : (-std::numeric_limits<T>::max()); }
+  inline static T highest() { return (std::numeric_limits<T>::max)(); }
+  inline static T lowest()  { return IsInteger ? (std::numeric_limits<T>::min)() : (-(std::numeric_limits<T>::max)()); }
   
 #ifdef EIGEN2_SUPPORT
   enum {
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/PlainObjectBase.h b/bazaar/plugin/Eigen/Eigen/src/Core/PlainObjectBase.h
index 5358cb572..ed34b0ed9 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/PlainObjectBase.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/PlainObjectBase.h
@@ -647,8 +647,8 @@ struct internal::conservative_resize_like_impl
     {
       // The storage order does not allow us to use reallocation.
       typename Derived::PlainObject tmp(rows,cols);
-      const Index common_rows = std::min(rows, _this.rows());
-      const Index common_cols = std::min(cols, _this.cols());
+      const Index common_rows = (std::min)(rows, _this.rows());
+      const Index common_cols = (std::min)(cols, _this.cols());
       tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
       _this.derived().swap(tmp);
     }
@@ -681,8 +681,8 @@ struct internal::conservative_resize_like_impl
     {
       // The storage order does not allow us to use reallocation.
       typename Derived::PlainObject tmp(other);
-      const Index common_rows = std::min(tmp.rows(), _this.rows());
-      const Index common_cols = std::min(tmp.cols(), _this.cols());
+      const Index common_rows = (std::min)(tmp.rows(), _this.rows());
+      const Index common_cols = (std::min)(tmp.cols(), _this.cols());
       tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
       _this.derived().swap(tmp);
     }
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/StableNorm.h b/bazaar/plugin/Eigen/Eigen/src/Core/StableNorm.h
index e65943ad7..f667272e4 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/StableNorm.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/StableNorm.h
@@ -69,7 +69,7 @@ MatrixBase<Derived>::stableNorm() const
   if (bi>0)
     internal::stable_norm_kernel(this->head(bi), ssq, scale, invScale);
   for (; bi<n; bi+=blockSize)
-    internal::stable_norm_kernel(this->segment(bi,min(blockSize, n - bi)).template forceAlignedAccessIf<Alignment>(), ssq, scale, invScale);
+    internal::stable_norm_kernel(this->segment(bi,(min)(blockSize, n - bi)).template forceAlignedAccessIf<Alignment>(), ssq, scale, invScale);
   return scale * internal::sqrt(ssq);
 }
 
@@ -103,12 +103,12 @@ MatrixBase<Derived>::blueNorm() const
     // For portability, the PORT subprograms "ilmaeh" and "rlmach"
     // are used. For any specific computer, each of the assignment
     // statements can be replaced
-    nbig  = std::numeric_limits<Index>::max();            // largest integer
+    nbig  = (std::numeric_limits<Index>::max)();            // largest integer
     ibeta = std::numeric_limits<RealScalar>::radix;         // base for floating-point numbers
     it    = std::numeric_limits<RealScalar>::digits;        // number of base-beta digits in mantissa
     iemin = std::numeric_limits<RealScalar>::min_exponent;  // minimum exponent
     iemax = std::numeric_limits<RealScalar>::max_exponent;  // maximum exponent
-    rbig  = std::numeric_limits<RealScalar>::max();         // largest floating-point number
+    rbig  = (std::numeric_limits<RealScalar>::max)();         // largest floating-point number
 
     iexp  = -((1-iemin)/2);
     b1    = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp)));  // lower boundary of midrange
@@ -167,8 +167,8 @@ MatrixBase<Derived>::blueNorm() const
   }
   else
     return internal::sqrt(amed);
-  asml = min(abig, amed);
-  abig = max(abig, amed);
+  asml = (min)(abig, amed);
+  abig = (max)(abig, amed);
   if(asml <= abig*relerr)
     return abig;
   else
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/Transpose.h b/bazaar/plugin/Eigen/Eigen/src/Core/Transpose.h
index b521f9319..3f7c7df6e 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/Transpose.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/Transpose.h
@@ -350,15 +350,14 @@ struct blas_traits<SelfCwiseBinaryOp<BinOp,NestedXpr,Rhs> >
 template<bool DestIsTransposed, typename OtherDerived>
 struct check_transpose_aliasing_compile_time_selector
 {
-  enum { ret = blas_traits<OtherDerived>::IsTransposed != DestIsTransposed
-  };
+  enum { ret = bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed };
 };
 
 template<bool DestIsTransposed, typename BinOp, typename DerivedA, typename DerivedB>
 struct check_transpose_aliasing_compile_time_selector<DestIsTransposed,CwiseBinaryOp<BinOp,DerivedA,DerivedB> >
 {
-  enum { ret =    blas_traits<DerivedA>::IsTransposed != DestIsTransposed
-               || blas_traits<DerivedB>::IsTransposed != DestIsTransposed
+  enum { ret =    bool(blas_traits<DerivedA>::IsTransposed) != DestIsTransposed
+               || bool(blas_traits<DerivedB>::IsTransposed) != DestIsTransposed
   };
 };
 
@@ -367,7 +366,7 @@ struct check_transpose_aliasing_run_time_selector
 {
   static bool run(const Scalar* dest, const OtherDerived& src)
   {
-    return (blas_traits<OtherDerived>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(Scalar*)extract_data(src));
+    return (bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed) && (dest!=0 && dest==(Scalar*)extract_data(src));
   }
 };
 
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/TriangularMatrix.h b/bazaar/plugin/Eigen/Eigen/src/Core/TriangularMatrix.h
index c04fa0c32..033e81036 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/TriangularMatrix.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/TriangularMatrix.h
@@ -111,6 +111,7 @@ template<typename Derived> class TriangularBase : public EigenBase<Derived>
       EIGEN_ONLY_USED_FOR_DEBUG(col);
       eigen_assert(col>=0 && col<cols() && row>=0 && row<rows());
       const int mode = int(Mode) & ~SelfAdjoint;
+      EIGEN_ONLY_USED_FOR_DEBUG(mode);
       eigen_assert((mode==Upper && col>=row)
                 || (mode==Lower && col<=row)
                 || ((mode==StrictlyUpper || mode==UnitUpper) && col>row)
@@ -491,7 +492,7 @@ struct triangular_assignment_selector<Derived1, Derived2, Upper, Dynamic, ClearO
   {
     for(Index j = 0; j < dst.cols(); ++j)
     {
-      Index maxi = std::min(j, dst.rows()-1);
+      Index maxi = (std::min)(j, dst.rows()-1);
       for(Index i = 0; i <= maxi; ++i)
         dst.copyCoeff(i, j, src);
       if (ClearOpposite)
@@ -511,7 +512,7 @@ struct triangular_assignment_selector<Derived1, Derived2, Lower, Dynamic, ClearO
     {
       for(Index i = j; i < dst.rows(); ++i)
         dst.copyCoeff(i, j, src);
-      Index maxi = std::min(j, dst.rows());
+      Index maxi = (std::min)(j, dst.rows());
       if (ClearOpposite)
         for(Index i = 0; i < maxi; ++i)
           dst.coeffRef(i, j) = static_cast<typename Derived1::Scalar>(0);
@@ -527,7 +528,7 @@ struct triangular_assignment_selector<Derived1, Derived2, StrictlyUpper, Dynamic
   {
     for(Index j = 0; j < dst.cols(); ++j)
     {
-      Index maxi = std::min(j, dst.rows());
+      Index maxi = (std::min)(j, dst.rows());
       for(Index i = 0; i < maxi; ++i)
         dst.copyCoeff(i, j, src);
       if (ClearOpposite)
@@ -547,7 +548,7 @@ struct triangular_assignment_selector<Derived1, Derived2, StrictlyLower, Dynamic
     {
       for(Index i = j+1; i < dst.rows(); ++i)
         dst.copyCoeff(i, j, src);
-      Index maxi = std::min(j, dst.rows()-1);
+      Index maxi = (std::min)(j, dst.rows()-1);
       if (ClearOpposite)
         for(Index i = 0; i <= maxi; ++i)
           dst.coeffRef(i, j) = static_cast<typename Derived1::Scalar>(0);
@@ -563,7 +564,7 @@ struct triangular_assignment_selector<Derived1, Derived2, UnitUpper, Dynamic, Cl
   {
     for(Index j = 0; j < dst.cols(); ++j)
     {
-      Index maxi = std::min(j, dst.rows());
+      Index maxi = (std::min)(j, dst.rows());
       for(Index i = 0; i < maxi; ++i)
         dst.copyCoeff(i, j, src);
       if (ClearOpposite)
@@ -583,7 +584,7 @@ struct triangular_assignment_selector<Derived1, Derived2, UnitLower, Dynamic, Cl
   {
     for(Index j = 0; j < dst.cols(); ++j)
     {
-      Index maxi = std::min(j, dst.rows());
+      Index maxi = (std::min)(j, dst.rows());
       for(Index i = maxi+1; i < dst.rows(); ++i)
         dst.copyCoeff(i, j, src);
       if (ClearOpposite)
@@ -795,7 +796,7 @@ bool MatrixBase<Derived>::isUpperTriangular(RealScalar prec) const
   RealScalar maxAbsOnUpperPart = static_cast<RealScalar>(-1);
   for(Index j = 0; j < cols(); ++j)
   {
-    Index maxi = std::min(j, rows()-1);
+    Index maxi = (std::min)(j, rows()-1);
     for(Index i = 0; i <= maxi; ++i)
     {
       RealScalar absValue = internal::abs(coeff(i,j));
@@ -827,7 +828,7 @@ bool MatrixBase<Derived>::isLowerTriangular(RealScalar prec) const
   RealScalar threshold = maxAbsOnLowerPart * prec;
   for(Index j = 1; j < cols(); ++j)
   {
-    Index maxi = std::min(j, rows()-1);
+    Index maxi = (std::min)(j, rows()-1);
     for(Index i = 0; i < maxi; ++i)
       if(internal::abs(coeff(i, j)) > threshold) return false;
   }
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h b/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h
index 2116dcc74..6f3f27170 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h
@@ -81,6 +81,7 @@ inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1=0, std::ptrdi
 template<typename LhsScalar, typename RhsScalar, int KcFactor>
 void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrdiff_t& n)
 {
+  EIGEN_UNUSED_VARIABLE(n);
   // Explanations:
   // Let's recall the product algorithms form kc x nc horizontal panels B' on the rhs and
   // mc x kc blocks A' on the lhs. A' has to fit into L2 cache. Moreover, B' is processed
@@ -102,7 +103,6 @@ void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrd
   k = std::min<std::ptrdiff_t>(k, l1/kdiv);
   std::ptrdiff_t _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0;
   if(_m<m) m = _m & mr_mask;
-  n = n;
 }
 
 template<typename LhsScalar, typename RhsScalar>
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixMatrix.h b/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixMatrix.h
index d33f00b20..ae94a2795 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixMatrix.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixMatrix.h
@@ -78,7 +78,7 @@ static void run(Index rows, Index cols, Index depth,
   typedef gebp_traits<LhsScalar,RhsScalar> Traits;
 
   Index kc = blocking.kc();                 // cache block size along the K direction
-  Index mc = std::min(rows,blocking.mc());  // cache block size along the M direction
+  Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
   //Index nc = blocking.nc(); // cache block size along the N direction
 
   gemm_pack_lhs<LhsScalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs;
@@ -103,7 +103,7 @@ static void run(Index rows, Index cols, Index depth,
     // For each horizontal panel of the rhs, and corresponding vertical panel of the lhs...
     for(Index k=0; k<depth; k+=kc)
     {
-      const Index actual_kc = std::min(k+kc,depth)-k; // => rows of B', and cols of the A'
+      const Index actual_kc = (std::min)(k+kc,depth)-k; // => rows of B', and cols of the A'
 
       // In order to reduce the chance that a thread has to wait for the other,
       // let's start by packing A'.
@@ -140,7 +140,7 @@ static void run(Index rows, Index cols, Index depth,
       // Then keep going as usual with the remaining A'
       for(Index i=mc; i<rows; i+=mc)
       {
-        const Index actual_mc = std::min(i+mc,rows)-i;
+        const Index actual_mc = (std::min)(i+mc,rows)-i;
 
         // pack A_i,k to A'
         pack_lhs(blockA, &lhs(i,k), lhsStride, actual_kc, actual_mc);
@@ -174,7 +174,7 @@ static void run(Index rows, Index cols, Index depth,
     // (==GEMM_VAR1)
     for(Index k2=0; k2<depth; k2+=kc)
     {
-      const Index actual_kc = std::min(k2+kc,depth)-k2;
+      const Index actual_kc = (std::min)(k2+kc,depth)-k2;
 
       // OK, here we have selected one horizontal panel of rhs and one vertical panel of lhs.
       // => Pack rhs's panel into a sequential chunk of memory (L2 caching)
@@ -187,7 +187,7 @@ static void run(Index rows, Index cols, Index depth,
       // (==GEPP_VAR1)
       for(Index i2=0; i2<rows; i2+=mc)
       {
-        const Index actual_mc = std::min(i2+mc,rows)-i2;
+        const Index actual_mc = (std::min)(i2+mc,rows)-i2;
 
         // We pack the lhs's block into a sequential chunk of memory (L1 caching)
         // Note that this block will be read a very high number of times, which is equal to the number of
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h b/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
index 562c9c5ad..5043b64fe 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
@@ -96,14 +96,14 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
 
     for(Index k2=0; k2<depth; k2+=kc)
     {
-      const Index actual_kc = std::min(k2+kc,depth)-k2;
+      const Index actual_kc = (std::min)(k2+kc,depth)-k2;
 
       // note that the actual rhs is the transpose/adjoint of mat
       pack_rhs(blockB, &rhs(k2,0), rhsStride, actual_kc, size);
 
       for(Index i2=0; i2<size; i2+=mc)
       {
-        const Index actual_mc = std::min(i2+mc,size)-i2;
+        const Index actual_mc = (std::min)(i2+mc,size)-i2;
 
         pack_lhs(blockA, &lhs(i2, k2), lhsStride, actual_kc, actual_mc);
 
@@ -112,7 +112,7 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
         //  2 - the actual_mc x actual_mc symmetric block => processed with a special kernel
         //  3 - after the diagonal => processed with gebp or skipped
         if (UpLo==Lower)
-          gebp(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, std::min(size,i2), alpha,
+          gebp(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, (std::min)(size,i2), alpha,
                -1, -1, 0, 0, allocatedBlockB);
 
         sybb(res+resStride*i2 + i2, resStride, blockA, blockB + actual_kc*i2, actual_mc, actual_kc, alpha, allocatedBlockB);
@@ -120,7 +120,7 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
         if (UpLo==Upper)
         {
           Index j2 = i2+actual_mc;
-          gebp(res+resStride*j2+i2, resStride, blockA, blockB+actual_kc*j2, actual_mc, actual_kc, std::max(Index(0), size-j2), alpha,
+          gebp(res+resStride*j2+i2, resStride, blockA, blockB+actual_kc*j2, actual_mc, actual_kc, (std::max)(Index(0), size-j2), alpha,
                -1, -1, 0, 0, allocatedBlockB);
         }
       }
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixVector.h b/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixVector.h
index 540638b5a..e0e2cbf8f 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixVector.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/products/GeneralMatrixVector.h
@@ -134,7 +134,7 @@ EIGEN_DONT_INLINE static void run(
     }
     else
     {
-      skipColumns = std::min(skipColumns,cols);
+      skipColumns = (std::min)(skipColumns,cols);
       // note that the skiped columns are processed later.
     }
 
@@ -386,7 +386,7 @@ EIGEN_DONT_INLINE static void run(
     }
     else
     {
-      skipRows = std::min(skipRows,Index(rows));
+      skipRows = (std::min)(skipRows,Index(rows));
       // note that the skiped columns are processed later.
     }
     eigen_internal_assert(  alignmentPattern==NoneAligned
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/products/SelfadjointMatrixMatrix.h b/bazaar/plugin/Eigen/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
index 6eb54d6ef..ccd757cfa 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
@@ -114,7 +114,7 @@ struct symm_pack_rhs
     }
 
     // second part: diagonal block
-    for(Index j2=k2; j2<std::min(k2+rows,packet_cols); j2+=nr)
+    for(Index j2=k2; j2<(std::min)(k2+rows,packet_cols); j2+=nr)
     {
       // again we can split vertically in three different parts (transpose, symmetric, normal)
       // transpose
@@ -179,7 +179,7 @@ struct symm_pack_rhs
     for(Index j2=packet_cols; j2<cols; ++j2)
     {
       // transpose
-      Index half = std::min(end_k,j2);
+      Index half = (std::min)(end_k,j2);
       for(Index k=k2; k<half; k++)
       {
         blockB[count] = conj(rhs(j2,k));
@@ -261,7 +261,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
     Index nc = cols;  // cache block size along the N direction
     computeProductBlockingSizes<Scalar,Scalar>(kc, mc, nc);
     // kc must smaller than mc
-    kc = std::min(kc,mc);
+    kc = (std::min)(kc,mc);
 
     std::size_t sizeW = kc*Traits::WorkSpaceFactor;
     std::size_t sizeB = sizeW + kc*cols;
@@ -276,7 +276,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
 
     for(Index k2=0; k2<size; k2+=kc)
     {
-      const Index actual_kc = std::min(k2+kc,size)-k2;
+      const Index actual_kc = (std::min)(k2+kc,size)-k2;
 
       // we have selected one row panel of rhs and one column panel of lhs
       // pack rhs's panel into a sequential chunk of memory
@@ -289,7 +289,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
       //  3 - the panel below the diagonal block => generic packed copy
       for(Index i2=0; i2<k2; i2+=mc)
       {
-        const Index actual_mc = std::min(i2+mc,k2)-i2;
+        const Index actual_mc = (std::min)(i2+mc,k2)-i2;
         // transposed packed copy
         pack_lhs_transposed(blockA, &lhs(k2, i2), lhsStride, actual_kc, actual_mc);
 
@@ -297,7 +297,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
       }
       // the block diagonal
       {
-        const Index actual_mc = std::min(k2+kc,size)-k2;
+        const Index actual_mc = (std::min)(k2+kc,size)-k2;
         // symmetric packed copy
         pack_lhs(blockA, &lhs(k2,k2), lhsStride, actual_kc, actual_mc);
 
@@ -306,7 +306,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
 
       for(Index i2=k2+kc; i2<size; i2+=mc)
       {
-        const Index actual_mc = std::min(i2+mc,size)-i2;
+        const Index actual_mc = (std::min)(i2+mc,size)-i2;
         gemm_pack_lhs<Scalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder,false>()
           (blockA, &lhs(i2, k2), lhsStride, actual_kc, actual_mc);
 
@@ -352,14 +352,14 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLh
 
     for(Index k2=0; k2<size; k2+=kc)
     {
-      const Index actual_kc = std::min(k2+kc,size)-k2;
+      const Index actual_kc = (std::min)(k2+kc,size)-k2;
 
       pack_rhs(blockB, _rhs, rhsStride, actual_kc, cols, k2);
 
       // => GEPP
       for(Index i2=0; i2<rows; i2+=mc)
       {
-        const Index actual_mc = std::min(i2+mc,rows)-i2;
+        const Index actual_mc = (std::min)(i2+mc,rows)-i2;
         pack_lhs(blockA, &lhs(i2, k2), lhsStride, actual_kc, actual_mc);
 
         gebp_kernel(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, cols, alpha);
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h b/bazaar/plugin/Eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h
index 62744f98d..d6121fc07 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h
@@ -70,7 +70,7 @@ static EIGEN_DONT_INLINE void product_selfadjoint_vector(
       rhs[i] = *it;
   }
 
-  Index bound = std::max(Index(0),size-8) & 0xfffffffe;
+  Index bound = (std::max)(Index(0),size-8) & 0xfffffffe;
   if (FirstTriangular)
     bound = size - bound;
 
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularMatrixMatrix.h b/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularMatrixMatrix.h
index 4b6301bde..0c48d2efb 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularMatrixMatrix.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularMatrixMatrix.h
@@ -112,7 +112,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
     Scalar alpha)
   {
     // strip zeros
-    Index diagSize  = std::min(_rows,_depth);
+    Index diagSize  = (std::min)(_rows,_depth);
     Index rows      = IsLower ? _rows : diagSize;
     Index depth     = IsLower ? diagSize : _depth;
     Index cols      = _cols;
@@ -145,7 +145,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
         IsLower ? k2>0 : k2<depth;
         IsLower ? k2-=kc : k2+=kc)
     {
-      Index actual_kc = std::min(IsLower ? k2 : depth-k2, kc);
+      Index actual_kc = (std::min)(IsLower ? k2 : depth-k2, kc);
       Index actual_k2 = IsLower ? k2-actual_kc : k2;
 
       // align blocks with the end of the triangular part for trapezoidal lhs
@@ -203,10 +203,10 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
       // the part below (lower case) or above (upper case) the diagonal => GEPP
       {
         Index start = IsLower ? k2 : 0;
-        Index end   = IsLower ? rows : std::min(actual_k2,rows);
+        Index end   = IsLower ? rows : (std::min)(actual_k2,rows);
         for(Index i2=start; i2<end; i2+=mc)
         {
-          const Index actual_mc = std::min(i2+mc,end)-i2;
+          const Index actual_mc = (std::min)(i2+mc,end)-i2;
           gemm_pack_lhs<Scalar, Index, Traits::mr,Traits::LhsProgress, LhsStorageOrder,false>()
             (blockA, &lhs(i2, actual_k2), lhsStride, actual_kc, actual_mc);
 
@@ -240,7 +240,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
     Scalar alpha)
   {
     // strip zeros
-    Index diagSize  = std::min(_cols,_depth);
+    Index diagSize  = (std::min)(_cols,_depth);
     Index rows      = _rows;
     Index depth     = IsLower ? _depth : diagSize;
     Index cols      = IsLower ? diagSize : _cols;
@@ -275,7 +275,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
         IsLower ? k2<depth  : k2>0;
         IsLower ? k2+=kc   : k2-=kc)
     {
-      Index actual_kc = std::min(IsLower ? depth-k2 : k2, kc);
+      Index actual_kc = (std::min)(IsLower ? depth-k2 : k2, kc);
       Index actual_k2 = IsLower ? k2 : k2-actual_kc;
 
       // align blocks with the end of the triangular part for trapezoidal rhs
@@ -286,7 +286,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
       }
 
       // remaining size
-      Index rs = IsLower ? std::min(cols,actual_k2) : cols - k2;
+      Index rs = IsLower ? (std::min)(cols,actual_k2) : cols - k2;
       // size of the triangular part
       Index ts = (IsLower && actual_k2>=cols) ? 0 : actual_kc;
 
@@ -327,7 +327,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
 
       for (Index i2=0; i2<rows; i2+=mc)
       {
-        const Index actual_mc = std::min(mc,rows-i2);
+        const Index actual_mc = (std::min)(mc,rows-i2);
         pack_lhs(blockA, &lhs(i2, actual_k2), lhsStride, actual_kc, actual_mc);
 
         // triangular kernel
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularMatrixVector.h b/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularMatrixVector.h
index 1dd2fafa4..71b4a52ab 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularMatrixVector.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularMatrixVector.h
@@ -56,7 +56,7 @@ struct product_triangular_matrix_vector<Index,Mode,LhsScalar,ConjLhs,RhsScalar,C
 
     for (Index pi=0; pi<cols; pi+=PanelWidth)
     {
-      Index actualPanelWidth = std::min(PanelWidth, cols-pi);
+      Index actualPanelWidth = (std::min)(PanelWidth, cols-pi);
       for (Index k=0; k<actualPanelWidth; ++k)
       {
         Index i = pi + k;
@@ -107,7 +107,7 @@ struct product_triangular_matrix_vector<Index,Mode,LhsScalar,ConjLhs,RhsScalar,C
     
     for (Index pi=0; pi<cols; pi+=PanelWidth)
     {
-      Index actualPanelWidth = std::min(PanelWidth, cols-pi);
+      Index actualPanelWidth = (std::min)(PanelWidth, cols-pi);
       for (Index k=0; k<actualPanelWidth; ++k)
       {
         Index i = pi + k;
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularSolverMatrix.h b/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularSolverMatrix.h
index a6ad9c322..4dced6b0e 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularSolverMatrix.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularSolverMatrix.h
@@ -85,7 +85,7 @@ struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageO
         IsLower ? k2<size : k2>0;
         IsLower ? k2+=kc : k2-=kc)
     {
-      const Index actual_kc = std::min(IsLower ? size-k2 : k2, kc);
+      const Index actual_kc = (std::min)(IsLower ? size-k2 : k2, kc);
 
       // We have selected and packed a big horizontal panel R1 of rhs. Let B be the packed copy of this panel,
       // and R2 the remaining part of rhs. The corresponding vertical panel of lhs is split into
@@ -164,7 +164,7 @@ struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageO
         Index end   = IsLower ? size : k2-kc;
         for(Index i2=start; i2<end; i2+=mc)
         {
-          const Index actual_mc = std::min(mc,end-i2);
+          const Index actual_mc = (std::min)(mc,end-i2);
           if (actual_mc>0)
           {
             pack_lhs(blockA, &tri(i2, IsLower ? k2 : k2-kc), triStride, actual_kc, actual_mc);
@@ -222,7 +222,7 @@ struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorage
         IsLower ? k2>0 : k2<size;
         IsLower ? k2-=kc : k2+=kc)
     {
-      const Index actual_kc = std::min(IsLower ? k2 : size-k2, kc);
+      const Index actual_kc = (std::min)(IsLower ? k2 : size-k2, kc);
       Index actual_k2 = IsLower ? k2-actual_kc : k2 ;
 
       Index startPanel = IsLower ? 0 : k2+actual_kc;
@@ -251,7 +251,7 @@ struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorage
 
       for(Index i2=0; i2<rows; i2+=mc)
       {
-        const Index actual_mc = std::min(mc,rows-i2);
+        const Index actual_mc = (std::min)(mc,rows-i2);
 
         // triangular solver kernel
         {
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularSolverVector.h b/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularSolverVector.h
index e87f6fb0c..639d4a5b4 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularSolverVector.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/products/TriangularSolverVector.h
@@ -60,7 +60,7 @@ struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Con
         IsLower ? pi<size : pi>0;
         IsLower ? pi+=PanelWidth : pi-=PanelWidth)
     {
-      Index actualPanelWidth = std::min(IsLower ? size - pi : pi, PanelWidth);
+      Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
 
       Index r = IsLower ? pi : size - pi; // remaining size
       if (r > 0)
@@ -114,7 +114,7 @@ struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Con
         IsLower ? pi<size : pi>0;
         IsLower ? pi+=PanelWidth : pi-=PanelWidth)
     {
-      Index actualPanelWidth = std::min(IsLower ? size - pi : pi, PanelWidth);
+      Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
       Index startBlock = IsLower ? pi : pi-actualPanelWidth;
       Index endBlock = IsLower ? pi + actualPanelWidth : 0;
 
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/util/Macros.h b/bazaar/plugin/Eigen/Eigen/src/Core/util/Macros.h
index 4e56a006c..6c3f1e421 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/util/Macros.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/util/Macros.h
@@ -28,7 +28,7 @@
 
 #define EIGEN_WORLD_VERSION 3
 #define EIGEN_MAJOR_VERSION 0
-#define EIGEN_MINOR_VERSION 1
+#define EIGEN_MINOR_VERSION 2
 
 #define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
                                       (EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
@@ -399,7 +399,7 @@
 #define EIGEN_MAKE_CWISE_BINARY_OP(METHOD,FUNCTOR) \
   template<typename OtherDerived> \
   EIGEN_STRONG_INLINE const CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived> \
-  METHOD(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
+  (METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
   { \
     return CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived>(derived(), other.derived()); \
   }
diff --git a/bazaar/plugin/Eigen/Eigen/src/Core/util/Memory.h b/bazaar/plugin/Eigen/Eigen/src/Core/util/Memory.h
index e84c664a9..a580b95ad 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Core/util/Memory.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Core/util/Memory.h
@@ -156,7 +156,7 @@ inline void* generic_aligned_realloc(void* ptr, size_t size, size_t old_size)
 
   if (ptr != 0)
   {
-    std::memcpy(newptr, ptr, std::min(size,old_size));
+    std::memcpy(newptr, ptr, (std::min)(size,old_size));
     aligned_free(ptr);
   }
 
@@ -494,12 +494,12 @@ template<typename T> class aligned_stack_memory_handler
     aligned_stack_memory_handler(T* ptr, size_t size, bool dealloc)
       : m_ptr(ptr), m_size(size), m_deallocate(dealloc)
     {
-      if(NumTraits<T>::RequireInitialization)
+      if(NumTraits<T>::RequireInitialization && m_ptr)
         Eigen::internal::construct_elements_of_array(m_ptr, size);
     }
     ~aligned_stack_memory_handler()
     {
-      if(NumTraits<T>::RequireInitialization)
+      if(NumTraits<T>::RequireInitialization && m_ptr)
         Eigen::internal::destruct_elements_of_array<T>(m_ptr, m_size);
       if(m_deallocate)
         Eigen::internal::aligned_free(m_ptr);
@@ -663,12 +663,12 @@ public:
 
     size_type max_size() const throw()
     {
-        return std::numeric_limits<size_type>::max();
+        return (std::numeric_limits<size_type>::max)();
     }
 
-    pointer allocate( size_type num, const_pointer* hint = 0 )
+    pointer allocate( size_type num, const void* hint = 0 )
     {
-        static_cast<void>( hint ); // suppress unused variable warning
+        EIGEN_UNUSED_VARIABLE(hint);
         return static_cast<pointer>( internal::aligned_malloc( num * sizeof(T) ) );
     }
 
@@ -903,7 +903,7 @@ inline int queryTopLevelCacheSize()
 {
   int l1, l2(-1), l3(-1);
   queryCacheSizes(l1,l2,l3);
-  return std::max(l2,l3);
+  return (std::max)(l2,l3);
 }
 
 } // end namespace internal
diff --git a/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/Cwise.h b/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/Cwise.h
index c619d389c..2dc83b6a7 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/Cwise.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/Cwise.h
@@ -82,13 +82,17 @@ template<typename ExpressionType> class Cwise
     const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op)
     operator/(const MatrixBase<OtherDerived> &other) const;
 
+    /** \deprecated ArrayBase::min() */
     template<typename OtherDerived>
     const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)
-    min(const MatrixBase<OtherDerived> &other) const;
+    (min)(const MatrixBase<OtherDerived> &other) const
+    { return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)(_expression(), other.derived()); }
 
+    /** \deprecated ArrayBase::max() */
     template<typename OtherDerived>
     const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)
-    max(const MatrixBase<OtherDerived> &other) const;
+    (max)(const MatrixBase<OtherDerived> &other) const
+    { return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)(_expression(), other.derived()); }
 
     const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs_op)      abs() const;
     const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs2_op)     abs2() const;
diff --git a/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/CwiseOperators.h b/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/CwiseOperators.h
index 0c7e9db6d..9c28559c3 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/CwiseOperators.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/CwiseOperators.h
@@ -96,24 +96,6 @@ inline ExpressionType& Cwise<ExpressionType>::operator/=(const MatrixBase<OtherD
   return m_matrix.const_cast_derived() = *this / other;
 }
 
-/** \deprecated ArrayBase::min() */
-template<typename ExpressionType>
-template<typename OtherDerived>
-EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)
-Cwise<ExpressionType>::min(const MatrixBase<OtherDerived> &other) const
-{
-  return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)(_expression(), other.derived());
-}
-
-/** \deprecated ArrayBase::max() */
-template<typename ExpressionType>
-template<typename OtherDerived>
-EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)
-Cwise<ExpressionType>::max(const MatrixBase<OtherDerived> &other) const
-{
-  return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)(_expression(), other.derived());
-}
-
 /***************************************************************************
 * The following functions were defined in Array
 ***************************************************************************/
diff --git a/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/Geometry/AlignedBox.h b/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/Geometry/AlignedBox.h
index 1c915be22..6ad846d35 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/Geometry/AlignedBox.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/Geometry/AlignedBox.h
@@ -51,14 +51,14 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
   { if (AmbientDimAtCompileTime!=Dynamic) setNull(); }
 
   /** Constructs a null box with \a _dim the dimension of the ambient space. */
-  inline explicit AlignedBox(int _dim) : m_min(_dim), m_max(_dim)
+  inline explicit AlignedBox(int _dim) : m_(min)(_dim), m_(max)(_dim)
   { setNull(); }
 
   /** Constructs a box with extremities \a _min and \a _max. */
-  inline AlignedBox(const VectorType& _min, const VectorType& _max) : m_min(_min), m_max(_max) {}
+  inline AlignedBox(const VectorType& _min, const VectorType& _max) : m_(min)(_min), m_(max)(_max) {}
 
   /** Constructs a box containing a single point \a p. */
-  inline explicit AlignedBox(const VectorType& p) : m_min(p), m_max(p) {}
+  inline explicit AlignedBox(const VectorType& p) : m_(min)(p), m_(max)(p) {}
 
   ~AlignedBox() {}
 
@@ -71,18 +71,18 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
   /** Makes \c *this a null/empty box. */
   inline void setNull()
   {
-    m_min.setConstant( std::numeric_limits<Scalar>::max());
-    m_max.setConstant(-std::numeric_limits<Scalar>::max());
+    m_min.setConstant( std::numeric_limits<Scalar>::(max)());
+    m_max.setConstant(-std::numeric_limits<Scalar>::(max)());
   }
 
   /** \returns the minimal corner */
-  inline const VectorType& min() const { return m_min; }
+  inline const VectorType& (min)() const { return m_min; }
   /** \returns a non const reference to the minimal corner */
-  inline VectorType& min() { return m_min; }
+  inline VectorType& (min)() { return m_min; }
   /** \returns the maximal corner */
-  inline const VectorType& max() const { return m_max; }
+  inline const VectorType& (max)() const { return m_max; }
   /** \returns a non const reference to the maximal corner */
-  inline VectorType& max() { return m_max; }
+  inline VectorType& (max)() { return m_max; }
 
   /** \returns true if the point \a p is inside the box \c *this. */
   inline bool contains(const VectorType& p) const
@@ -90,19 +90,19 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
 
   /** \returns true if the box \a b is entirely inside the box \c *this. */
   inline bool contains(const AlignedBox& b) const
-  { return (m_min.cwise()<=b.min()).all() && (b.max().cwise()<=m_max).all(); }
+  { return (m_min.cwise()<=b.(min)()).all() && (b.(max)().cwise()<=m_max).all(); }
 
   /** Extends \c *this such that it contains the point \a p and returns a reference to \c *this. */
   inline AlignedBox& extend(const VectorType& p)
-  { m_min = m_min.cwise().min(p); m_max = m_max.cwise().max(p); return *this; }
+  { m_min = m_min.cwise().(min)(p); m_max = m_max.cwise().(max)(p); return *this; }
 
   /** Extends \c *this such that it contains the box \a b and returns a reference to \c *this. */
   inline AlignedBox& extend(const AlignedBox& b)
-  { m_min = m_min.cwise().min(b.m_min); m_max = m_max.cwise().max(b.m_max); return *this; }
+  { m_min = m_min.cwise().(min)(b.m_min); m_max = m_max.cwise().(max)(b.m_max); return *this; }
 
   /** Clamps \c *this by the box \a b and returns a reference to \c *this. */
   inline AlignedBox& clamp(const AlignedBox& b)
-  { m_min = m_min.cwise().max(b.m_min); m_max = m_max.cwise().min(b.m_max); return *this; }
+  { m_min = m_min.cwise().(max)(b.m_min); m_max = m_max.cwise().(min)(b.m_max); return *this; }
 
   /** Translate \c *this by the vector \a t and returns a reference to \c *this. */
   inline AlignedBox& translate(const VectorType& t)
@@ -138,8 +138,8 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
   template<typename OtherScalarType>
   inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other)
   {
-    m_min = other.min().template cast<Scalar>();
-    m_max = other.max().template cast<Scalar>();
+    m_min = other.(min)().template cast<Scalar>();
+    m_max = other.(max)().template cast<Scalar>();
   }
 
   /** \returns \c true if \c *this is approximately equal to \a other, within the precision
diff --git a/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/SVD.h b/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/SVD.h
index 528a0dcd0..16b4b488f 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/SVD.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Eigen2Support/SVD.h
@@ -64,9 +64,9 @@ template<typename MatrixType> class SVD
     SVD() {} // a user who relied on compiler-generated default compiler reported problems with MSVC in 2.0.7
     
     SVD(const MatrixType& matrix)
-      : m_matU(matrix.rows(), std::min(matrix.rows(), matrix.cols())),
+      : m_matU(matrix.rows(), (std::min)(matrix.rows(), matrix.cols())),
         m_matV(matrix.cols(),matrix.cols()),
-        m_sigma(std::min(matrix.rows(),matrix.cols()))
+        m_sigma((std::min)(matrix.rows(),matrix.cols()))
     {
       compute(matrix);
     }
@@ -108,13 +108,13 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
 {
   const int m = matrix.rows();
   const int n = matrix.cols();
-  const int nu = std::min(m,n);
+  const int nu = (std::min)(m,n);
   ei_assert(m>=n && "In Eigen 2.0, SVD only works for MxN matrices with M>=N. Sorry!");
   ei_assert(m>1 && "In Eigen 2.0, SVD doesn't work on 1x1 matrices");
 
   m_matU.resize(m, nu);
   m_matU.setZero();
-  m_sigma.resize(std::min(m,n));
+  m_sigma.resize((std::min)(m,n));
   m_matV.resize(n,n);
 
   RowVector e(n);
@@ -126,9 +126,9 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
 
   // Reduce A to bidiagonal form, storing the diagonal elements
   // in s and the super-diagonal elements in e.
-  int nct = std::min(m-1,n);
-  int nrt = std::max(0,std::min(n-2,m));
-  for (k = 0; k < std::max(nct,nrt); ++k)
+  int nct = (std::min)(m-1,n);
+  int nrt = (std::max)(0,(std::min)(n-2,m));
+  for (k = 0; k < (std::max)(nct,nrt); ++k)
   {
     if (k < nct)
     {
@@ -193,7 +193,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
 
 
   // Set up the final bidiagonal matrix or order p.
-  int p = std::min(n,m+1);
+  int p = (std::min)(n,m+1);
   if (nct < n)
     m_sigma[nct] = matA(nct,nct);
   if (m < p)
@@ -380,7 +380,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
       case 3:
       {
         // Calculate the shift.
-        Scalar scale = std::max(std::max(std::max(std::max(
+        Scalar scale = (std::max)((std::max)((std::max)((std::max)(
                         ei_abs(m_sigma[p-1]),ei_abs(m_sigma[p-2])),ei_abs(e[p-2])),
                         ei_abs(m_sigma[k])),ei_abs(e[k]));
         Scalar sp = m_sigma[p-1]/scale;
diff --git a/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/ComplexSchur.h b/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/ComplexSchur.h
index b1830f642..ec93af2e5 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/ComplexSchur.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/ComplexSchur.h
@@ -423,7 +423,7 @@ void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
     JacobiRotation<ComplexScalar> rot;
     rot.makeGivens(m_matT.coeff(il,il) - shift, m_matT.coeff(il+1,il));
     m_matT.rightCols(m_matT.cols()-il).applyOnTheLeft(il, il+1, rot.adjoint());
-    m_matT.topRows(std::min(il+2,iu)+1).applyOnTheRight(il, il+1, rot);
+    m_matT.topRows((std::min)(il+2,iu)+1).applyOnTheRight(il, il+1, rot);
     if(computeU) m_matU.applyOnTheRight(il, il+1, rot);
 
     for(Index i=il+1 ; i<iu ; i++)
@@ -431,7 +431,7 @@ void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
       rot.makeGivens(m_matT.coeffRef(i,i-1), m_matT.coeffRef(i+1,i-1), &m_matT.coeffRef(i,i-1));
       m_matT.coeffRef(i+1,i-1) = ComplexScalar(0);
       m_matT.rightCols(m_matT.cols()-i).applyOnTheLeft(i, i+1, rot.adjoint());
-      m_matT.topRows(std::min(i+2,iu)+1).applyOnTheRight(i, i+1, rot);
+      m_matT.topRows((std::min)(i+2,iu)+1).applyOnTheRight(i, i+1, rot);
       if(computeU) m_matU.applyOnTheRight(i, i+1, rot);
     }
   }
diff --git a/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/EigenSolver.h b/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/EigenSolver.h
index 9b133a010..ac4c4242d 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/EigenSolver.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/EigenSolver.h
@@ -435,7 +435,7 @@ void EigenSolver<MatrixType>::doComputeEigenvectors()
   Scalar norm = 0.0;
   for (Index j = 0; j < size; ++j)
   {
-    norm += m_matT.row(j).segment(std::max(j-1,Index(0)), size-std::max(j-1,Index(0))).cwiseAbs().sum();
+    norm += m_matT.row(j).segment((std::max)(j-1,Index(0)), size-(std::max)(j-1,Index(0))).cwiseAbs().sum();
   }
   
   // Backsubstitute to find vectors of upper triangular form
@@ -564,7 +564,7 @@ void EigenSolver<MatrixType>::doComputeEigenvectors()
 
           // Overflow control
           using std::max;
-          Scalar t = max(internal::abs(m_matT.coeff(i,n-1)),internal::abs(m_matT.coeff(i,n)));
+          Scalar t = (max)(internal::abs(m_matT.coeff(i,n-1)),internal::abs(m_matT.coeff(i,n)));
           if ((eps * t) * t > Scalar(1))
             m_matT.block(i, n-1, size-i, 2) /= t;
 
diff --git a/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/RealSchur.h b/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/RealSchur.h
index c61ae444c..cc9af11c1 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/RealSchur.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/RealSchur.h
@@ -290,7 +290,7 @@ inline typename MatrixType::Scalar RealSchur<MatrixType>::computeNormOfT()
   //               + m_matT.bottomLeftCorner(size-1,size-1).diagonal().cwiseAbs().sum();
   Scalar norm = 0.0;
   for (Index j = 0; j < size; ++j)
-    norm += m_matT.row(j).segment(std::max(j-1,Index(0)), size-std::max(j-1,Index(0))).cwiseAbs().sum();
+    norm += m_matT.row(j).segment((std::max)(j-1,Index(0)), size-(std::max)(j-1,Index(0))).cwiseAbs().sum();
   return norm;
 }
 
@@ -442,7 +442,7 @@ inline void RealSchur<MatrixType>::performFrancisQRStep(Index il, Index im, Inde
 
       // These Householder transformations form the O(n^3) part of the algorithm
       m_matT.block(k, k, 3, size-k).applyHouseholderOnTheLeft(ess, tau, workspace);
-      m_matT.block(0, k, std::min(iu,k+3) + 1, 3).applyHouseholderOnTheRight(ess, tau, workspace);
+      m_matT.block(0, k, (std::min)(iu,k+3) + 1, 3).applyHouseholderOnTheRight(ess, tau, workspace);
       if (computeU)
         m_matU.block(0, k, size, 3).applyHouseholderOnTheRight(ess, tau, workspace);
     }
diff --git a/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h b/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
index eeab325cc..965dda88b 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
@@ -387,7 +387,7 @@ SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
   {
     m_eivalues.coeffRef(0,0) = internal::real(matrix.coeff(0,0));
     if(computeEigenvectors)
-      m_eivec.setOnes();
+      m_eivec.setOnes(n,n);
     m_info = Success;
     m_isInitialized = true;
     m_eigenvectorsOk = computeEigenvectors;
diff --git a/bazaar/plugin/Eigen/Eigen/src/Geometry/AlignedBox.h b/bazaar/plugin/Eigen/Eigen/src/Geometry/AlignedBox.h
index d81dcad9e..b51deb3f3 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Geometry/AlignedBox.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Geometry/AlignedBox.h
@@ -111,13 +111,13 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
   }
 
   /** \returns the minimal corner */
-  inline const VectorType& min() const { return m_min; }
+  inline const VectorType& (min)() const { return m_min; }
   /** \returns a non const reference to the minimal corner */
-  inline VectorType& min() { return m_min; }
+  inline VectorType& (min)() { return m_min; }
   /** \returns the maximal corner */
-  inline const VectorType& max() const { return m_max; }
+  inline const VectorType& (max)() const { return m_max; }
   /** \returns a non const reference to the maximal corner */
-  inline VectorType& max() { return m_max; }
+  inline VectorType& (max)() { return m_max; }
 
   /** \returns the center of the box */
   inline const CwiseUnaryOp<internal::scalar_quotient1_op<Scalar>,
@@ -196,7 +196,7 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
 
   /** \returns true if the box \a b is entirely inside the box \c *this. */
   inline bool contains(const AlignedBox& b) const
-  { return (m_min.array()<=b.min().array()).all() && (b.max().array()<=m_max.array()).all(); }
+  { return (m_min.array()<=(b.min)().array()).all() && ((b.max)().array()<=m_max.array()).all(); }
 
   /** Extends \c *this such that it contains the point \a p and returns a reference to \c *this. */
   template<typename Derived>
@@ -287,8 +287,8 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
   template<typename OtherScalarType>
   inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other)
   {
-    m_min = other.min().template cast<Scalar>();
-    m_max = other.max().template cast<Scalar>();
+    m_min = (other.min)().template cast<Scalar>();
+    m_max = (other.max)().template cast<Scalar>();
   }
 
   /** \returns \c true if \c *this is approximately equal to \a other, within the precision
diff --git a/bazaar/plugin/Eigen/Eigen/src/Geometry/AngleAxis.h b/bazaar/plugin/Eigen/Eigen/src/Geometry/AngleAxis.h
index 0a7046608..0ec4624cf 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Geometry/AngleAxis.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Geometry/AngleAxis.h
@@ -182,7 +182,7 @@ AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const QuaternionBase<QuatDerived
   }
   else
   {
-    m_angle = Scalar(2)*acos(min(max(Scalar(-1),q.w()),Scalar(1)));
+    m_angle = Scalar(2)*acos((min)((max)(Scalar(-1),q.w()),Scalar(1)));
     m_axis = q.vec() / internal::sqrt(n2);
   }
   return *this;
diff --git a/bazaar/plugin/Eigen/Eigen/src/Geometry/Hyperplane.h b/bazaar/plugin/Eigen/Eigen/src/Geometry/Hyperplane.h
index 00e027040..eb0a58771 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Geometry/Hyperplane.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Geometry/Hyperplane.h
@@ -189,7 +189,7 @@ public:
     *
     * \note If \a other is approximately parallel to *this, this method will return any point on *this.
     */
-  VectorType intersection(const Hyperplane& other)
+  VectorType intersection(const Hyperplane& other) const
   {
     EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
     Scalar det = coeffs().coeff(0) * other.coeffs().coeff(1) - coeffs().coeff(1) * other.coeffs().coeff(0);
diff --git a/bazaar/plugin/Eigen/Eigen/src/Geometry/ParametrizedLine.h b/bazaar/plugin/Eigen/Eigen/src/Geometry/ParametrizedLine.h
index edad5f8ee..b90f9c088 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Geometry/ParametrizedLine.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Geometry/ParametrizedLine.h
@@ -34,7 +34,7 @@
   *
   * A parametrized line is defined by an origin point \f$ \mathbf{o} \f$ and a unit
   * direction vector \f$ \mathbf{d} \f$ such that the line corresponds to
-  * the set \f$ l(t) = \mathbf{o} + t \mathbf{d} \f$, \f$ l \in \mathbf{R} \f$.
+  * the set \f$ l(t) = \mathbf{o} + t \mathbf{d} \f$, \f$ t \in \mathbf{R} \f$.
   *
   * \param _Scalar the scalar type, i.e., the type of the coefficients
   * \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
@@ -107,7 +107,7 @@ public:
   { return origin() + direction().dot(p-origin()) * direction(); }
 
   template <int OtherOptions>
-  Scalar intersection(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane);
+  Scalar intersection(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const;
 
   /** \returns \c *this with scalar type casted to \a NewScalarType
     *
@@ -159,7 +159,7 @@ inline ParametrizedLine<_Scalar, _AmbientDim,_Options>::ParametrizedLine(const H
   */
 template <typename _Scalar, int _AmbientDim, int _Options>
 template <int OtherOptions>
-inline _Scalar ParametrizedLine<_Scalar, _AmbientDim,_Options>::intersection(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane)
+inline _Scalar ParametrizedLine<_Scalar, _AmbientDim,_Options>::intersection(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const
 {
   return -(hyperplane.offset()+hyperplane.normal().dot(origin()))
           / hyperplane.normal().dot(direction());
diff --git a/bazaar/plugin/Eigen/Eigen/src/LU/FullPivLU.h b/bazaar/plugin/Eigen/Eigen/src/LU/FullPivLU.h
index 339d7845c..633fb23fd 100644
--- a/bazaar/plugin/Eigen/Eigen/src/LU/FullPivLU.h
+++ b/bazaar/plugin/Eigen/Eigen/src/LU/FullPivLU.h
@@ -533,7 +533,7 @@ template<typename MatrixType>
 MatrixType FullPivLU<MatrixType>::reconstructedMatrix() const
 {
   eigen_assert(m_isInitialized && "LU is not initialized.");
-  const Index smalldim = std::min(m_lu.rows(), m_lu.cols());
+  const Index smalldim = (std::min)(m_lu.rows(), m_lu.cols());
   // LU
   MatrixType res(m_lu.rows(),m_lu.cols());
   // FIXME the .toDenseMatrix() should not be needed...
@@ -695,7 +695,7 @@ struct solve_retval<FullPivLU<_MatrixType>, Rhs>
     const Index rows = dec().rows(), cols = dec().cols(),
               nonzero_pivots = dec().nonzeroPivots();
     eigen_assert(rhs().rows() == rows);
-    const Index smalldim = std::min(rows, cols);
+    const Index smalldim = (std::min)(rows, cols);
 
     if(nonzero_pivots == 0)
     {
diff --git a/bazaar/plugin/Eigen/Eigen/src/LU/PartialPivLU.h b/bazaar/plugin/Eigen/Eigen/src/LU/PartialPivLU.h
index 4cae510b0..09394b01f 100644
--- a/bazaar/plugin/Eigen/Eigen/src/LU/PartialPivLU.h
+++ b/bazaar/plugin/Eigen/Eigen/src/LU/PartialPivLU.h
@@ -253,7 +253,7 @@ struct partial_lu_impl
   {
     const Index rows = lu.rows();
     const Index cols = lu.cols();
-    const Index size = std::min(rows,cols);
+    const Index size = (std::min)(rows,cols);
     nb_transpositions = 0;
     int first_zero_pivot = -1;
     for(Index k = 0; k < size; ++k)
@@ -313,7 +313,7 @@ struct partial_lu_impl
     MapLU lu1(lu_data,StorageOrder==RowMajor?rows:luStride,StorageOrder==RowMajor?luStride:cols);
     MatrixType lu(lu1,0,0,rows,cols);
 
-    const Index size = std::min(rows,cols);
+    const Index size = (std::min)(rows,cols);
 
     // if the matrix is too small, no blocking:
     if(size<=16)
@@ -327,14 +327,14 @@ struct partial_lu_impl
     {
       blockSize = size/8;
       blockSize = (blockSize/16)*16;
-      blockSize = std::min(std::max(blockSize,Index(8)), maxBlockSize);
+      blockSize = (std::min)((std::max)(blockSize,Index(8)), maxBlockSize);
     }
 
     nb_transpositions = 0;
     int first_zero_pivot = -1;
     for(Index k = 0; k < size; k+=blockSize)
     {
-      Index bs = std::min(size-k,blockSize); // actual size of the block
+      Index bs = (std::min)(size-k,blockSize); // actual size of the block
       Index trows = rows - k - bs; // trailing rows
       Index tsize = size - k - bs; // trailing size
 
diff --git a/bazaar/plugin/Eigen/Eigen/src/LU/arch/Inverse_SSE.h b/bazaar/plugin/Eigen/Eigen/src/LU/arch/Inverse_SSE.h
index 0fe9be388..176c349ce 100644
--- a/bazaar/plugin/Eigen/Eigen/src/LU/arch/Inverse_SSE.h
+++ b/bazaar/plugin/Eigen/Eigen/src/LU/arch/Inverse_SSE.h
@@ -182,8 +182,8 @@ struct compute_inverse_size4<Architecture::SSE, double, MatrixType, ResultType>
   };
   static void run(const MatrixType& matrix, ResultType& result)
   {
-    const EIGEN_ALIGN16 long long int _Sign_NP[2] = { 0x8000000000000000ll, 0x0000000000000000ll };
-    const EIGEN_ALIGN16 long long int _Sign_PN[2] = { 0x0000000000000000ll, 0x8000000000000000ll };
+    const __m128d _Sign_NP = _mm_castsi128_pd(_mm_set_epi32(0x0,0x0,0x80000000,0x0));
+    const __m128d _Sign_PN = _mm_castsi128_pd(_mm_set_epi32(0x80000000,0x0,0x0,0x0));
 
     // The inverse is calculated using "Divide and Conquer" technique. The
     // original matrix is divide into four 2x2 sub-matrices. Since each
@@ -316,8 +316,8 @@ struct compute_inverse_size4<Architecture::SSE, double, MatrixType, ResultType>
     iB1 = _mm_sub_pd(_mm_mul_pd(C1, dB), iB1);
     iB2 = _mm_sub_pd(_mm_mul_pd(C2, dB), iB2);
 
-    d1 = _mm_xor_pd(rd, _mm_load_pd((double*)_Sign_PN));
-    d2 = _mm_xor_pd(rd, _mm_load_pd((double*)_Sign_NP));
+    d1 = _mm_xor_pd(rd, _Sign_PN);
+    d2 = _mm_xor_pd(rd, _Sign_NP);
 
     //  iC = B*|C| - A*C#*D;
     dC = _mm_shuffle_pd(dC,dC,0);
diff --git a/bazaar/plugin/Eigen/Eigen/src/QR/ColPivHouseholderQR.h b/bazaar/plugin/Eigen/Eigen/src/QR/ColPivHouseholderQR.h
index 0cdcaa1cb..f04c6038d 100644
--- a/bazaar/plugin/Eigen/Eigen/src/QR/ColPivHouseholderQR.h
+++ b/bazaar/plugin/Eigen/Eigen/src/QR/ColPivHouseholderQR.h
@@ -93,7 +93,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
       */
     ColPivHouseholderQR(Index rows, Index cols)
       : m_qr(rows, cols),
-        m_hCoeffs(std::min(rows,cols)),
+        m_hCoeffs((std::min)(rows,cols)),
         m_colsPermutation(cols),
         m_colsTranspositions(cols),
         m_temp(cols),
@@ -103,7 +103,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
 
     ColPivHouseholderQR(const MatrixType& matrix)
       : m_qr(matrix.rows(), matrix.cols()),
-        m_hCoeffs(std::min(matrix.rows(),matrix.cols())),
+        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
         m_colsPermutation(matrix.cols()),
         m_colsTranspositions(matrix.cols()),
         m_temp(matrix.cols()),
diff --git a/bazaar/plugin/Eigen/Eigen/src/QR/FullPivHouseholderQR.h b/bazaar/plugin/Eigen/Eigen/src/QR/FullPivHouseholderQR.h
index 7f1d98c54..dde3013be 100644
--- a/bazaar/plugin/Eigen/Eigen/src/QR/FullPivHouseholderQR.h
+++ b/bazaar/plugin/Eigen/Eigen/src/QR/FullPivHouseholderQR.h
@@ -93,21 +93,21 @@ template<typename _MatrixType> class FullPivHouseholderQR
       */
     FullPivHouseholderQR(Index rows, Index cols)
       : m_qr(rows, cols),
-        m_hCoeffs(std::min(rows,cols)),
+        m_hCoeffs((std::min)(rows,cols)),
         m_rows_transpositions(rows),
         m_cols_transpositions(cols),
         m_cols_permutation(cols),
-        m_temp(std::min(rows,cols)),
+        m_temp((std::min)(rows,cols)),
         m_isInitialized(false),
         m_usePrescribedThreshold(false) {}
 
     FullPivHouseholderQR(const MatrixType& matrix)
       : m_qr(matrix.rows(), matrix.cols()),
-        m_hCoeffs(std::min(matrix.rows(), matrix.cols())),
+        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
         m_rows_transpositions(matrix.rows()),
         m_cols_transpositions(matrix.cols()),
         m_cols_permutation(matrix.cols()),
-        m_temp(std::min(matrix.rows(), matrix.cols())),
+        m_temp((std::min)(matrix.rows(), matrix.cols())),
         m_isInitialized(false),
         m_usePrescribedThreshold(false)
     {
@@ -379,7 +379,7 @@ FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(cons
 {
   Index rows = matrix.rows();
   Index cols = matrix.cols();
-  Index size = std::min(rows,cols);
+  Index size = (std::min)(rows,cols);
 
   m_qr = matrix;
   m_hCoeffs.resize(size);
@@ -493,7 +493,7 @@ struct solve_retval<FullPivHouseholderQR<_MatrixType>, Rhs>
       RealScalar biggest_in_upper_part_of_c = c.topRows(   dec().rank()     ).cwiseAbs().maxCoeff();
       RealScalar biggest_in_lower_part_of_c = c.bottomRows(rows-dec().rank()).cwiseAbs().maxCoeff();
       // FIXME brain dead
-      const RealScalar m_precision = NumTraits<Scalar>::epsilon() * std::min(rows,cols);
+      const RealScalar m_precision = NumTraits<Scalar>::epsilon() * (std::min)(rows,cols);
       // this internal:: prefix is needed by at least gcc 3.4 and ICC
       if(!internal::isMuchSmallerThan(biggest_in_lower_part_of_c, biggest_in_upper_part_of_c, m_precision))
         return;
@@ -520,7 +520,7 @@ typename FullPivHouseholderQR<MatrixType>::MatrixQType FullPivHouseholderQR<Matr
   // and v_k is the k-th Householder vector [1,m_qr(k+1,k), m_qr(k+2,k), ...]
   Index rows = m_qr.rows();
   Index cols = m_qr.cols();
-  Index size = std::min(rows,cols);
+  Index size = (std::min)(rows,cols);
   MatrixQType res = MatrixQType::Identity(rows, rows);
   Matrix<Scalar,1,MatrixType::RowsAtCompileTime> temp(rows);
   for (Index k = size-1; k >= 0; k--)
diff --git a/bazaar/plugin/Eigen/Eigen/src/QR/HouseholderQR.h b/bazaar/plugin/Eigen/Eigen/src/QR/HouseholderQR.h
index 0d2b74893..9ee96de26 100644
--- a/bazaar/plugin/Eigen/Eigen/src/QR/HouseholderQR.h
+++ b/bazaar/plugin/Eigen/Eigen/src/QR/HouseholderQR.h
@@ -88,13 +88,13 @@ template<typename _MatrixType> class HouseholderQR
       */
     HouseholderQR(Index rows, Index cols)
       : m_qr(rows, cols),
-        m_hCoeffs(std::min(rows,cols)),
+        m_hCoeffs((std::min)(rows,cols)),
         m_temp(cols),
         m_isInitialized(false) {}
 
     HouseholderQR(const MatrixType& matrix)
       : m_qr(matrix.rows(), matrix.cols()),
-        m_hCoeffs(std::min(matrix.rows(),matrix.cols())),
+        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
         m_temp(matrix.cols()),
         m_isInitialized(false)
     {
@@ -210,7 +210,7 @@ void householder_qr_inplace_unblocked(MatrixQR& mat, HCoeffs& hCoeffs, typename
   typedef typename MatrixQR::RealScalar RealScalar;
   Index rows = mat.rows();
   Index cols = mat.cols();
-  Index size = std::min(rows,cols);
+  Index size = (std::min)(rows,cols);
 
   eigen_assert(hCoeffs.size() == size);
 
@@ -250,7 +250,7 @@ void householder_qr_inplace_blocked(MatrixQR& mat, HCoeffs& hCoeffs,
 
   Index rows = mat.rows();
   Index cols = mat.cols();
-  Index size = std::min(rows, cols);
+  Index size = (std::min)(rows, cols);
 
   typedef Matrix<Scalar,Dynamic,1,ColMajor,MatrixQR::MaxColsAtCompileTime,1> TempType;
   TempType tempVector;
@@ -260,12 +260,12 @@ void householder_qr_inplace_blocked(MatrixQR& mat, HCoeffs& hCoeffs,
     tempData = tempVector.data();
   }
 
-  Index blockSize = std::min(maxBlockSize,size);
+  Index blockSize = (std::min)(maxBlockSize,size);
 
-  int k = 0;
+  Index k = 0;
   for (k = 0; k < size; k += blockSize)
   {
-    Index bs = std::min(size-k,blockSize);  // actual size of the block
+    Index bs = (std::min)(size-k,blockSize);  // actual size of the block
     Index tcols = cols - k - bs;            // trailing columns
     Index brows = rows-k;                   // rows of the block
 
@@ -299,7 +299,7 @@ struct solve_retval<HouseholderQR<_MatrixType>, Rhs>
   template<typename Dest> void evalTo(Dest& dst) const
   {
     const Index rows = dec().rows(), cols = dec().cols();
-    const Index rank = std::min(rows, cols);
+    const Index rank = (std::min)(rows, cols);
     eigen_assert(rhs().rows() == rows);
 
     typename Rhs::PlainObject c(rhs());
@@ -327,7 +327,7 @@ HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const MatrixType&
 {
   Index rows = matrix.rows();
   Index cols = matrix.cols();
-  Index size = std::min(rows,cols);
+  Index size = (std::min)(rows,cols);
 
   m_qr = matrix;
   m_hCoeffs.resize(size);
diff --git a/bazaar/plugin/Eigen/Eigen/src/SVD/JacobiSVD.h b/bazaar/plugin/Eigen/Eigen/src/SVD/JacobiSVD.h
index 5539a72dc..5f6139998 100644
--- a/bazaar/plugin/Eigen/Eigen/src/SVD/JacobiSVD.h
+++ b/bazaar/plugin/Eigen/Eigen/src/SVD/JacobiSVD.h
@@ -569,7 +569,7 @@ void JacobiSVD<MatrixType, QRPreconditioner>::allocate(Index rows, Index cols, u
               "JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
               "Use the ColPivHouseholderQR preconditioner instead.");
   }
-  m_diagSize = std::min(m_rows, m_cols);
+  m_diagSize = (std::min)(m_rows, m_cols);
   m_singularValues.resize(m_diagSize);
   m_matrixU.resize(m_rows, m_computeFullU ? m_rows
                           : m_computeThinU ? m_diagSize
@@ -619,8 +619,8 @@ JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsig
         // notice that this comparison will evaluate to false if any NaN is involved, ensuring that NaN's don't
         // keep us iterating forever.
         using std::max;
-        if(max(internal::abs(m_workMatrix.coeff(p,q)),internal::abs(m_workMatrix.coeff(q,p)))
-            > max(internal::abs(m_workMatrix.coeff(p,p)),internal::abs(m_workMatrix.coeff(q,q)))*precision)
+        if((max)(internal::abs(m_workMatrix.coeff(p,q)),internal::abs(m_workMatrix.coeff(q,p)))
+            > (max)(internal::abs(m_workMatrix.coeff(p,p)),internal::abs(m_workMatrix.coeff(q,q)))*precision)
         {
           finished = false;
 
@@ -689,7 +689,7 @@ struct solve_retval<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
     // A = U S V^*
     // So A^{-1} = V S^{-1} U^*
 
-    Index diagSize = std::min(dec().rows(), dec().cols());
+    Index diagSize = (std::min)(dec().rows(), dec().cols());
     typename JacobiSVDType::SingularValuesType invertedSingVals(diagSize);
 
     Index nonzeroSingVals = dec().nonzeroSingularValues();
diff --git a/bazaar/plugin/Eigen/Eigen/src/Sparse/AmbiVector.h b/bazaar/plugin/Eigen/Eigen/src/Sparse/AmbiVector.h
index 01c93fbd7..2ea8ba309 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Sparse/AmbiVector.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Sparse/AmbiVector.h
@@ -97,7 +97,7 @@ class AmbiVector
     void reallocateSparse()
     {
       Index copyElements = m_allocatedElements;
-      m_allocatedElements = std::min(Index(m_allocatedElements*1.5),m_size);
+      m_allocatedElements = (std::min)(Index(m_allocatedElements*1.5),m_size);
       Index allocSize = m_allocatedElements * sizeof(ListEl);
       allocSize = allocSize/sizeof(Scalar) + (allocSize%sizeof(Scalar)>0?1:0);
       Scalar* newBuffer = new Scalar[allocSize];
diff --git a/bazaar/plugin/Eigen/Eigen/src/Sparse/CompressedStorage.h b/bazaar/plugin/Eigen/Eigen/src/Sparse/CompressedStorage.h
index 1c36a2632..b3bde272e 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Sparse/CompressedStorage.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Sparse/CompressedStorage.h
@@ -216,7 +216,7 @@ class CompressedStorage
     {
       Scalar* newValues  = new Scalar[size];
       Index* newIndices = new Index[size];
-      size_t copySize = std::min(size, m_size);
+      size_t copySize = (std::min)(size, m_size);
       // copy
       memcpy(newValues,  m_values,  copySize * sizeof(Scalar));
       memcpy(newIndices, m_indices, copySize * sizeof(Index));
diff --git a/bazaar/plugin/Eigen/Eigen/src/Sparse/DynamicSparseMatrix.h b/bazaar/plugin/Eigen/Eigen/src/Sparse/DynamicSparseMatrix.h
index abbc995cc..93e75f4c6 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Sparse/DynamicSparseMatrix.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Sparse/DynamicSparseMatrix.h
@@ -141,7 +141,7 @@ class DynamicSparseMatrix
     {
       if (outerSize()>0)
       {
-        Index reserveSizePerVector = std::max(reserveSize/outerSize(),Index(4));
+        Index reserveSizePerVector = (std::max)(reserveSize/outerSize(),Index(4));
         for (Index j=0; j<outerSize(); ++j)
         {
           m_data[j].reserve(reserveSizePerVector);
diff --git a/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseFuzzy.h b/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseFuzzy.h
index ddcef88ee..f00b3d646 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseFuzzy.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseFuzzy.h
@@ -35,7 +35,7 @@
 //   const typename internal::nested<Derived,2>::type nested(derived());
 //   const typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
 //   return    (nested - otherNested).cwise().abs2().sum()
-//          <= prec * prec * std::min(nested.cwise().abs2().sum(), otherNested.cwise().abs2().sum());
+//          <= prec * prec * (std::min)(nested.cwise().abs2().sum(), otherNested.cwise().abs2().sum());
 // }
 
 #endif // EIGEN_SPARSE_FUZZY_H
diff --git a/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseMatrix.h b/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseMatrix.h
index 9e7802736..0e175ec6e 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseMatrix.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseMatrix.h
@@ -257,7 +257,7 @@ class SparseMatrix
           // furthermore we bound the realloc ratio to:
           //   1) reduce multiple minor realloc when the matrix is almost filled
           //   2) avoid to allocate too much memory when the matrix is almost empty
-          reallocRatio = std::min(std::max(reallocRatio,1.5f),8.f);
+          reallocRatio = (std::min)((std::max)(reallocRatio,1.5f),8.f);
         }
       }
       m_data.resize(m_data.size()+1,reallocRatio);
diff --git a/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseMatrixBase.h b/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseMatrixBase.h
index 8695f7343..c01981bc9 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseMatrixBase.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseMatrixBase.h
@@ -223,7 +223,7 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
       // thanks to shallow copies, we always eval to a tempary
       Derived temp(other.rows(), other.cols());
 
-      temp.reserve(std::max(this->rows(),this->cols())*2);
+      temp.reserve((std::max)(this->rows(),this->cols())*2);
       for (Index j=0; j<outerSize; ++j)
       {
         temp.startVec(j);
@@ -253,7 +253,7 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
         // eval without temporary
         derived().resize(other.rows(), other.cols());
         derived().setZero();
-        derived().reserve(std::max(this->rows(),this->cols())*2);
+        derived().reserve((std::max)(this->rows(),this->cols())*2);
         for (Index j=0; j<outerSize; ++j)
         {
           derived().startVec(j);
diff --git a/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseSelfAdjointView.h b/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseSelfAdjointView.h
index 0f0abe749..d82044c78 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseSelfAdjointView.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseSelfAdjointView.h
@@ -383,7 +383,7 @@ void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixTyp
         continue;
                   
       Index ip = perm ? perm[i] : i;
-      count[DstUpLo==Lower ? std::min(ip,jp) : std::max(ip,jp)]++;
+      count[DstUpLo==Lower ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
     }
   }
   dest._outerIndexPtr()[0] = 0;
@@ -403,8 +403,8 @@ void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixTyp
         continue;
                   
       Index ip = perm? perm[i] : i;
-      Index k = count[DstUpLo==Lower ? std::min(ip,jp) : std::max(ip,jp)]++;
-      dest._innerIndexPtr()[k] = DstUpLo==Lower ? std::max(ip,jp) : std::min(ip,jp);
+      Index k = count[DstUpLo==Lower ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
+      dest._innerIndexPtr()[k] = DstUpLo==Lower ? (std::max)(ip,jp) : (std::min)(ip,jp);
       
       if((DstUpLo==Lower && ip<jp) || (DstUpLo==Upper && ip>jp))
         dest._valuePtr()[k] = conj(it.value());
diff --git a/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseSparseProduct.h b/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseSparseProduct.h
index cade6fd54..19abcd1f8 100644
--- a/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseSparseProduct.h
+++ b/bazaar/plugin/Eigen/Eigen/src/Sparse/SparseSparseProduct.h
@@ -45,7 +45,7 @@ static void sparse_product_impl2(const Lhs& lhs, const Rhs& rhs, ResultType& res
   // estimate the number of non zero entries
   float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
   float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
-  float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
+  float ratioRes = (std::min)(ratioLhs * avgNnzPerRhsColumn, 1.f);
 
 //  int t200 = rows/(log2(200)*1.39);
 //  int t = (rows*100)/139;
@@ -131,7 +131,7 @@ static void sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   // estimate the number of non zero entries
   float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
   float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
-  float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
+  float ratioRes = (std::min)(ratioLhs * avgNnzPerRhsColumn, 1.f);
 
   // mimics a resizeByInnerOuter:
   if(ResultType::IsRowMajor)
@@ -143,7 +143,7 @@ static void sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   for (Index j=0; j<cols; ++j)
   {
     // let's do a more accurate determination of the nnz ratio for the current column j of res
-    //float ratioColRes = std::min(ratioLhs * rhs.innerNonZeros(j), 1.f);
+    //float ratioColRes = (std::min)(ratioLhs * rhs.innerNonZeros(j), 1.f);
     // FIXME find a nice way to get the number of nonzeros of a sub matrix (here an inner vector)
     float ratioColRes = ratioRes;
     tempVector.init(ratioColRes);
diff --git a/bazaar/plugin/Eigen/Eigen/src/StlSupport/StdVector.h b/bazaar/plugin/Eigen/Eigen/src/StlSupport/StdVector.h
index 81a2d7762..27d6ab539 100644
--- a/bazaar/plugin/Eigen/Eigen/src/StlSupport/StdVector.h
+++ b/bazaar/plugin/Eigen/Eigen/src/StlSupport/StdVector.h
@@ -28,32 +28,24 @@
 
 #include "Eigen/src/StlSupport/details.h"
 
-// Define the explicit instantiation (e.g. necessary for the Intel compiler)
-#if defined(__INTEL_COMPILER) || defined(__GNUC__)
-  #define EIGEN_EXPLICIT_STL_VECTOR_INSTANTIATION(...) template class std::vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> >;
-#else
-  #define EIGEN_EXPLICIT_STL_VECTOR_INSTANTIATION(...)
-#endif
-
 /**
  * This section contains a convenience MACRO which allows an easy specialization of
  * std::vector such that for data types with alignment issues the correct allocator
  * is used automatically.
  */
 #define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...) \
-EIGEN_EXPLICIT_STL_VECTOR_INSTANTIATION(__VA_ARGS__) \
 namespace std \
 { \
-  template<typename _Ay> \
-  class vector<__VA_ARGS__, _Ay>  \
+  template<> \
+  class vector<__VA_ARGS__, std::allocator<__VA_ARGS__> >  \
     : public vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \
   { \
     typedef vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > vector_base; \
   public: \
     typedef __VA_ARGS__ value_type; \
-    typedef typename vector_base::allocator_type allocator_type; \
-    typedef typename vector_base::size_type size_type;  \
-    typedef typename vector_base::iterator iterator;  \
+    typedef vector_base::allocator_type allocator_type; \
+    typedef vector_base::size_type size_type;  \
+    typedef vector_base::iterator iterator;  \
     explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {}  \
     template<typename InputIterator> \
     vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : vector_base(first, last, a) {} \
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/MPRealSupport b/bazaar/plugin/Eigen/unsupported/Eigen/MPRealSupport
index 6bded0c2c..10fa23b35 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/MPRealSupport
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/MPRealSupport
@@ -34,7 +34,7 @@
 #include <Eigen/Core>
 
 namespace Eigen {
-  
+
   /** \defgroup MPRealSupport_Module MPFRC++ Support module
     *
     * \code
@@ -45,6 +45,8 @@ namespace Eigen {
     * via the <a href="http://www.holoborodko.com/pavel/?page_id=12">MPFR C++</a>
     * library which itself is built upon <a href="http://www.mpfr.org/">MPFR</a>/<a href="http://gmplib.org/">GMP</a>.
     *
+    * You can find a copy of MPFR C++ that is known to be compatible in the unsupported/test/mpreal folder.
+    *
     * Here is an example:
     *
 \code
@@ -129,18 +131,6 @@ int main()
     return a + (b-a) * random<mpfr::mpreal>();
   }
 
-  template<> struct conj_impl<mpfr::mpreal> { inline static const mpfr::mpreal& run(const mpfr::mpreal& x) { return x; } };
-  template<> struct real_impl<mpfr::mpreal> { inline static const mpfr::mpreal& run(const mpfr::mpreal& x) { return x; } };
-  template<> struct imag_impl<mpfr::mpreal> { inline static const mpfr::mpreal run(const mpfr::mpreal&)   { return mpfr::mpreal(0); } };
-  template<> struct abs_impl<mpfr::mpreal> { inline static const mpfr::mpreal run(const mpfr::mpreal& x)  { return mpfr::fabs(x); } };
-  template<> struct abs2_impl<mpfr::mpreal> { inline static const mpfr::mpreal run(const mpfr::mpreal& x) { return x*x; } };
-  template<> struct sqrt_impl<mpfr::mpreal> { inline static const mpfr::mpreal run(const mpfr::mpreal& x) { return mpfr::sqrt(x); } };
-  template<> struct exp_impl<mpfr::mpreal> { inline static const mpfr::mpreal run(const mpfr::mpreal& x)  { return mpfr::exp(x); } };
-  template<> struct log_impl<mpfr::mpreal> { inline static const mpfr::mpreal run(const mpfr::mpreal& x)  { return mpfr::log(x); } };
-  template<> struct sin_impl<mpfr::mpreal> { inline static const mpfr::mpreal run(const mpfr::mpreal& x)  { return mpfr::sin(x); } };
-  template<> struct cos_impl<mpfr::mpreal> { inline static const mpfr::mpreal run(const mpfr::mpreal& x)  { return mpfr::cos(x); } };
-  template<> struct pow_impl<mpfr::mpreal> { inline static const mpfr::mpreal run(const mpfr::mpreal& x, const mpfr::mpreal& y)  { return mpfr::pow(x, y); } };
-
   bool isMuchSmallerThan(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& prec)
   {
     return mpfr::abs(a) <= mpfr::abs(b) * prec;
@@ -148,7 +138,7 @@ int main()
 
   inline bool isApprox(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& prec)
   {
-  return mpfr::abs(a - b) <= mpfr::min(mpfr::abs(a), mpfr::abs(b)) * prec;
+    return mpfr::abs(a - b) <= (mpfr::min)(mpfr::abs(a), mpfr::abs(b)) * prec;
   }
 
   inline bool isApproxOrLessThan(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& prec)
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/OpenGLSupport b/bazaar/plugin/Eigen/unsupported/Eigen/OpenGLSupport
index 0f09cee14..e5938e21c 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/OpenGLSupport
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/OpenGLSupport
@@ -188,11 +188,11 @@ template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,Affine>& t)
 template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,Projective>& t)    { glLoadMatrix(t.matrix()); }
 template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,AffineCompact>& t) { glLoadMatrix(Transform<Scalar,3,Affine>(t).matrix()); }
 
-void glRotate(const Rotation2D<float>& rot)
+static void glRotate(const Rotation2D<float>& rot)
 {
   glRotatef(rot.angle()*180.f/float(M_PI), 0.f, 0.f, 1.f);
 }
-void glRotate(const Rotation2D<double>& rot)
+static void glRotate(const Rotation2D<double>& rot)
 {
   glRotated(rot.angle()*180.0/M_PI, 0.0, 0.0, 1.0);
 }
@@ -256,18 +256,18 @@ EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glGet,GLenum,_,double, 4,4,Doublev)
 
 #ifdef GL_VERSION_2_0
 
-void glUniform2fv_ei  (GLint loc, const float* v)         { glUniform2fv(loc,1,v); }
-void glUniform2iv_ei  (GLint loc, const int* v)           { glUniform2iv(loc,1,v); }
+static void glUniform2fv_ei  (GLint loc, const float* v)         { glUniform2fv(loc,1,v); }
+static void glUniform2iv_ei  (GLint loc, const int* v)           { glUniform2iv(loc,1,v); }
 
-void glUniform3fv_ei  (GLint loc, const float* v)         { glUniform3fv(loc,1,v); }
-void glUniform3iv_ei  (GLint loc, const int* v)           { glUniform3iv(loc,1,v); }
+static void glUniform3fv_ei  (GLint loc, const float* v)         { glUniform3fv(loc,1,v); }
+static void glUniform3iv_ei  (GLint loc, const int* v)           { glUniform3iv(loc,1,v); }
 
-void glUniform4fv_ei  (GLint loc, const float* v)         { glUniform4fv(loc,1,v); }
-void glUniform4iv_ei  (GLint loc, const int* v)           { glUniform4iv(loc,1,v); }
+static void glUniform4fv_ei  (GLint loc, const float* v)         { glUniform4fv(loc,1,v); }
+static void glUniform4iv_ei  (GLint loc, const int* v)           { glUniform4iv(loc,1,v); }
 
-void glUniformMatrix2fv_ei  (GLint loc, const float* v)         { glUniformMatrix2fv(loc,1,false,v); }
-void glUniformMatrix3fv_ei  (GLint loc, const float* v)         { glUniformMatrix3fv(loc,1,false,v); }
-void glUniformMatrix4fv_ei  (GLint loc, const float* v)         { glUniformMatrix4fv(loc,1,false,v); }
+static void glUniformMatrix2fv_ei  (GLint loc, const float* v)         { glUniformMatrix2fv(loc,1,false,v); }
+static void glUniformMatrix3fv_ei  (GLint loc, const float* v)         { glUniformMatrix3fv(loc,1,false,v); }
+static void glUniformMatrix4fv_ei  (GLint loc, const float* v)         { glUniformMatrix4fv(loc,1,false,v); }
 
 
 EIGEN_GL_FUNC1_DECLARATION       (glUniform,GLint,const)
@@ -286,12 +286,12 @@ EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        4,4,Matrix
 
 #ifdef GL_VERSION_2_1
 
-void glUniformMatrix2x3fv_ei(GLint loc, const float* v)         { glUniformMatrix2x3fv(loc,1,false,v); }
-void glUniformMatrix3x2fv_ei(GLint loc, const float* v)         { glUniformMatrix3x2fv(loc,1,false,v); }
-void glUniformMatrix2x4fv_ei(GLint loc, const float* v)         { glUniformMatrix2x4fv(loc,1,false,v); }
-void glUniformMatrix4x2fv_ei(GLint loc, const float* v)         { glUniformMatrix4x2fv(loc,1,false,v); }
-void glUniformMatrix3x4fv_ei(GLint loc, const float* v)         { glUniformMatrix3x4fv(loc,1,false,v); }
-void glUniformMatrix4x3fv_ei(GLint loc, const float* v)         { glUniformMatrix4x3fv(loc,1,false,v); }
+static void glUniformMatrix2x3fv_ei(GLint loc, const float* v)         { glUniformMatrix2x3fv(loc,1,false,v); }
+static void glUniformMatrix3x2fv_ei(GLint loc, const float* v)         { glUniformMatrix3x2fv(loc,1,false,v); }
+static void glUniformMatrix2x4fv_ei(GLint loc, const float* v)         { glUniformMatrix2x4fv(loc,1,false,v); }
+static void glUniformMatrix4x2fv_ei(GLint loc, const float* v)         { glUniformMatrix4x2fv(loc,1,false,v); }
+static void glUniformMatrix3x4fv_ei(GLint loc, const float* v)         { glUniformMatrix3x4fv(loc,1,false,v); }
+static void glUniformMatrix4x3fv_ei(GLint loc, const float* v)         { glUniformMatrix4x3fv(loc,1,false,v); }
 
 EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        2,3,Matrix2x3fv_ei)
 EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        3,2,Matrix3x2fv_ei)
@@ -304,9 +304,9 @@ EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        4,3,Matrix
 
 #ifdef GL_VERSION_3_0
 
-void glUniform2uiv_ei (GLint loc, const unsigned int* v)  { glUniform2uiv(loc,1,v); }
-void glUniform3uiv_ei (GLint loc, const unsigned int* v)  { glUniform3uiv(loc,1,v); }
-void glUniform4uiv_ei (GLint loc, const unsigned int* v)  { glUniform4uiv(loc,1,v); }
+static void glUniform2uiv_ei (GLint loc, const unsigned int* v)  { glUniform2uiv(loc,1,v); }
+static void glUniform3uiv_ei (GLint loc, const unsigned int* v)  { glUniform3uiv(loc,1,v); }
+static void glUniform4uiv_ei (GLint loc, const unsigned int* v)  { glUniform4uiv(loc,1,v); }
 
 EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 2,2uiv_ei)
 EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 3,3uiv_ei)
@@ -315,9 +315,9 @@ EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 4,4uiv_ei)
 #endif
 
 #ifdef GL_ARB_gpu_shader_fp64
-void glUniform2dv_ei  (GLint loc, const double* v)        { glUniform2dv(loc,1,v); }
-void glUniform3dv_ei  (GLint loc, const double* v)        { glUniform3dv(loc,1,v); }
-void glUniform4dv_ei  (GLint loc, const double* v)        { glUniform4dv(loc,1,v); }
+static void glUniform2dv_ei  (GLint loc, const double* v)        { glUniform2dv(loc,1,v); }
+static void glUniform3dv_ei  (GLint loc, const double* v)        { glUniform3dv(loc,1,v); }
+static void glUniform4dv_ei  (GLint loc, const double* v)        { glUniform4dv(loc,1,v); }
 
 EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,double,       2,2dv_ei)
 EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,double,       3,3dv_ei)
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/BVH/BVAlgorithms.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/BVH/BVAlgorithms.h
index 2412680e7..e6fdf4737 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/BVH/BVAlgorithms.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/BVH/BVAlgorithms.h
@@ -178,7 +178,7 @@ typename Minimizer::Scalar minimize_helper(const BVH &tree, Minimizer &minimizer
     todo.pop();
 
     for(; oBegin != oEnd; ++oBegin) //go through child objects
-      minimum = std::min(minimum, minimizer.minimumOnObject(*oBegin));
+      minimum = (std::min)(minimum, minimizer.minimumOnObject(*oBegin));
 
     for(; vBegin != vEnd; ++vBegin) { //go through child volumes
       Scalar val = minimizer.minimumOnVolume(tree.getVolume(*vBegin));
@@ -274,12 +274,12 @@ typename Minimizer::Scalar BVMinimize(const BVH1 &tree1, const BVH2 &tree2, Mini
 
     for(; oBegin1 != oEnd1; ++oBegin1) { //go through child objects of first tree
       for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
-        minimum = std::min(minimum, minimizer.minimumOnObjectObject(*oBegin1, *oCur2));
+        minimum = (std::min)(minimum, minimizer.minimumOnObjectObject(*oBegin1, *oCur2));
       }
 
       for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree
         Helper2 helper(*oBegin1, minimizer);
-        minimum = std::min(minimum, internal::minimize_helper(tree2, helper, *vCur2, minimum));
+        minimum = (std::min)(minimum, internal::minimize_helper(tree2, helper, *vCur2, minimum));
       }
     }
 
@@ -288,7 +288,7 @@ typename Minimizer::Scalar BVMinimize(const BVH1 &tree1, const BVH2 &tree2, Mini
 
       for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
         Helper1 helper(*oCur2, minimizer);
-        minimum = std::min(minimum, internal::minimize_helper(tree1, helper, *vBegin1, minimum));
+        minimum = (std::min)(minimum, internal::minimize_helper(tree1, helper, *vBegin1, minimum));
       }
 
       for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/FFT/ei_kissfft_impl.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/FFT/ei_kissfft_impl.h
index 667638f40..04b98b083 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/FFT/ei_kissfft_impl.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/FFT/ei_kissfft_impl.h
@@ -294,11 +294,19 @@ struct kissfft_impl
   inline
     void fwd2( Complex * dst,const Complex *src,int n0,int n1)
     {
+        EIGEN_UNUSED_VARIABLE(dst);
+        EIGEN_UNUSED_VARIABLE(src);
+        EIGEN_UNUSED_VARIABLE(n0);
+        EIGEN_UNUSED_VARIABLE(n1);
     }
 
   inline
     void inv2( Complex * dst,const Complex *src,int n0,int n1)
     {
+        EIGEN_UNUSED_VARIABLE(dst);
+        EIGEN_UNUSED_VARIABLE(src);
+        EIGEN_UNUSED_VARIABLE(n0);
+        EIGEN_UNUSED_VARIABLE(n1);
     }
 
   // real-to-complex forward FFT
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h
index f4851d316..4d8e183ee 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h
@@ -172,7 +172,7 @@ void constrained_cg(const TMatrix& A, const CMatrix& C, VectorX& x,
 
     if (iter.noiseLevel() > 0 && transition) std::cerr << "CCG: transition\n";
     if (transition || iter.first()) gamma = 0.0;
-    else gamma = std::max(0.0, (rho - old_z.dot(z)) / rho_1);
+    else gamma = (std::max)(0.0, (rho - old_z.dot(z)) / rho_1);
     p = z + gamma*p;
 
     ++iter;
@@ -185,7 +185,7 @@ void constrained_cg(const TMatrix& A, const CMatrix& C, VectorX& x,
       {
         Scalar bb = C.row(i).dot(p) - f[i];
         if (bb > 0.0)
-          lambda = std::min(lambda, (f.coeff(i)-C.row(i).dot(x)) / bb);
+          lambda = (std::min)(lambda, (f.coeff(i)-C.row(i).dot(x)) / bb);
       }
     }
     x += lambda * p;
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/IterativeSolvers/IterationController.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/IterativeSolvers/IterationController.h
index 385d8c546..a65793cd5 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/IterativeSolvers/IterationController.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/IterativeSolvers/IterationController.h
@@ -141,7 +141,7 @@ class IterationController
     bool converged(double nr)
     {
       m_res = internal::abs(nr); 
-      m_resminreach = std::min(m_resminreach, m_res);
+      m_resminreach = (std::min)(m_resminreach, m_res);
       return converged();
     }
     template<typename VectorType> bool converged(const VectorType &v)
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
index 87dc64ce1..d08766921 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
@@ -127,10 +127,10 @@ bool MatrixFunctionAtomic<MatrixType>::taylorConverged(Index s, const MatrixType
     for (Index r = 0; r < n; r++) {
       RealScalar mx = 0;
       for (Index i = 0; i < n; i++)
-        mx = std::max(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, static_cast<int>(s+r))));
+        mx = (std::max)(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, static_cast<int>(s+r))));
       if (r != 0)
         rfactorial *= RealScalar(r);
-      delta = std::max(delta, mx / rfactorial);
+      delta = (std::max)(delta, mx / rfactorial);
     }
     const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
     if (m_mu * delta * P_norm < NumTraits<Scalar>::epsilon() * F_norm)
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
index a4863700e..37abb6117 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
@@ -37,7 +37,7 @@ namespace HybridNonLinearSolverSpace {
         TolTooSmall = 3,
         NotMakingProgressJacobian = 4,
         NotMakingProgressIterations = 5,
-        UserAksed = 6
+        UserAsked = 6
     };
 }
 
@@ -181,7 +181,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveInit(FVectorType  &x)
     /*     and calculate its norm. */
     nfev = 1;
     if ( functor(x, fvec) < 0)
-        return HybridNonLinearSolverSpace::UserAksed;
+        return HybridNonLinearSolverSpace::UserAsked;
     fnorm = fvec.stableNorm();
 
     /*     initialize iteration counter and monitors. */
@@ -207,7 +207,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType  &x)
 
     /* calculate the jacobian matrix. */
     if ( functor.df(x, fjac) < 0)
-        return HybridNonLinearSolverSpace::UserAksed;
+        return HybridNonLinearSolverSpace::UserAsked;
     ++njev;
 
     wa2 = fjac.colwise().blueNorm();
@@ -228,7 +228,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType  &x)
     }
 
     /* compute the qr factorization of the jacobian. */
-    wa2 = fjac.colwise().blueNorm();
     HouseholderQR<JacobianType> qrfac(fjac); // no pivoting:
 
     /* copy the triangular factor of the qr factorization into r. */
@@ -255,11 +254,11 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType  &x)
 
         /* on the first iteration, adjust the initial step bound. */
         if (iter == 1)
-            delta = std::min(delta,pnorm);
+            delta = (std::min)(delta,pnorm);
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
-            return HybridNonLinearSolverSpace::UserAksed;
+            return HybridNonLinearSolverSpace::UserAsked;
         ++nfev;
         fnorm1 = wa4.stableNorm();
 
@@ -289,7 +288,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType  &x)
             ncfail = 0;
             ++ncsuc;
             if (ratio >= Scalar(.5) || ncsuc > 1)
-                delta = std::max(delta, pnorm / Scalar(.5));
+                delta = (std::max)(delta, pnorm / Scalar(.5));
             if (internal::abs(ratio - 1.) <= Scalar(.1)) {
                 delta = pnorm / Scalar(.5);
             }
@@ -322,7 +321,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType  &x)
         /* tests for termination and stringent tolerances. */
         if (nfev >= parameters.maxfev)
             return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
-        if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
+        if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
             return HybridNonLinearSolverSpace::TolTooSmall;
         if (nslow2 == 5)
             return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
@@ -420,7 +419,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(FVectorType  &
     /*     and calculate its norm. */
     nfev = 1;
     if ( functor(x, fvec) < 0)
-        return HybridNonLinearSolverSpace::UserAksed;
+        return HybridNonLinearSolverSpace::UserAsked;
     fnorm = fvec.stableNorm();
 
     /*     initialize iteration counter and monitors. */
@@ -448,8 +447,8 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
 
     /* calculate the jacobian matrix. */
     if (internal::fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
-        return HybridNonLinearSolverSpace::UserAksed;
-    nfev += std::min(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
+        return HybridNonLinearSolverSpace::UserAsked;
+    nfev += (std::min)(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
 
     wa2 = fjac.colwise().blueNorm();
 
@@ -469,7 +468,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
     }
 
     /* compute the qr factorization of the jacobian. */
-    wa2 = fjac.colwise().blueNorm();
     HouseholderQR<JacobianType> qrfac(fjac); // no pivoting:
 
     /* copy the triangular factor of the qr factorization into r. */
@@ -496,11 +494,11 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
 
         /* on the first iteration, adjust the initial step bound. */
         if (iter == 1)
-            delta = std::min(delta,pnorm);
+            delta = (std::min)(delta,pnorm);
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
-            return HybridNonLinearSolverSpace::UserAksed;
+            return HybridNonLinearSolverSpace::UserAsked;
         ++nfev;
         fnorm1 = wa4.stableNorm();
 
@@ -530,7 +528,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
             ncfail = 0;
             ++ncsuc;
             if (ratio >= Scalar(.5) || ncsuc > 1)
-                delta = std::max(delta, pnorm / Scalar(.5));
+                delta = (std::max)(delta, pnorm / Scalar(.5));
             if (internal::abs(ratio - 1.) <= Scalar(.1)) {
                 delta = pnorm / Scalar(.5);
             }
@@ -563,7 +561,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
         /* tests for termination and stringent tolerances. */
         if (nfev >= parameters.maxfev)
             return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
-        if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
+        if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
             return HybridNonLinearSolverSpace::TolTooSmall;
         if (nslow2 == 5)
             return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
index c43df3f7a..0ae681b1c 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
@@ -263,7 +263,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType  &x)
     if (fnorm != 0.)
         for (Index j = 0; j < n; ++j)
             if (wa2[permutation.indices()[j]] != 0.)
-                gnorm = std::max(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
+                gnorm = (std::max)(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
 
     /* test for convergence of the gradient norm. */
     if (gnorm <= parameters.gtol)
@@ -285,7 +285,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType  &x)
 
         /* on the first iteration, adjust the initial step bound. */
         if (iter == 1)
-            delta = std::min(delta,pnorm);
+            delta = (std::min)(delta,pnorm);
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
@@ -321,7 +321,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType  &x)
             if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1))
                 temp = Scalar(.1);
             /* Computing MIN */
-            delta = temp * std::min(delta, pnorm / Scalar(.1));
+            delta = temp * (std::min)(delta, pnorm / Scalar(.1));
             par /= temp;
         } else if (!(par != 0. && ratio < Scalar(.75))) {
             delta = pnorm / Scalar(.5);
@@ -510,7 +510,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
     if (fnorm != 0.)
         for (j = 0; j < n; ++j)
             if (wa2[permutation.indices()[j]] != 0.)
-                gnorm = std::max(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
+                gnorm = (std::max)(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
 
     /* test for convergence of the gradient norm. */
     if (gnorm <= parameters.gtol)
@@ -532,7 +532,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
 
         /* on the first iteration, adjust the initial step bound. */
         if (iter == 1)
-            delta = std::min(delta,pnorm);
+            delta = (std::min)(delta,pnorm);
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
@@ -568,7 +568,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
             if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1))
                 temp = Scalar(.1);
             /* Computing MIN */
-            delta = temp * std::min(delta, pnorm / Scalar(.1));
+            delta = temp * (std::min)(delta, pnorm / Scalar(.1));
             par /= temp;
         } else if (!(par != 0. && ratio < Scalar(.75))) {
             delta = pnorm / Scalar(.5);
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/dogleg.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
index fffd9e0be..cbdcf4b71 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
@@ -93,7 +93,7 @@ algo_end:
 
     /* form appropriate convex combination of the gauss-newton */
     /* direction and the scaled gradient direction. */
-    temp = (1.-alpha) * std::min(sgnorm,delta);
+    temp = (1.-alpha) * (std::min)(sgnorm,delta);
     x = temp * wa1 + alpha * x;
 }
 
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
index 887b76fa1..0a26c2061 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
@@ -26,7 +26,7 @@ DenseIndex fdjac1(
     Matrix< Scalar, Dynamic, 1 >  wa1(n);
     Matrix< Scalar, Dynamic, 1 >  wa2(n);
 
-    eps = sqrt(std::max(epsfcn,epsmch));
+    eps = sqrt((std::max)(epsfcn,epsmch));
     msum = ml + mu + 1;
     if (msum >= n) {
         /* computation of dense approximate jacobian. */
@@ -61,7 +61,7 @@ DenseIndex fdjac1(
                 if (h == 0.) h = eps;
                 fjac.col(j).setZero();
                 start = std::max<Index>(0,j-mu);
-                length = std::min(n-1, j+ml) - start + 1;
+                length = (std::min)(n-1, j+ml) - start + 1;
                 fjac.col(j).segment(start, length) = ( wa1.segment(start, length)-fvec.segment(start, length))/h;
             }
         }
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
index a6bbc50ba..62f4aabc9 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
@@ -91,12 +91,12 @@ void lmpar(
     gnorm = wa1.stableNorm();
     paru = gnorm / delta;
     if (paru == 0.)
-        paru = dwarf / std::min(delta,Scalar(0.1));
+        paru = dwarf / (std::min)(delta,Scalar(0.1));
 
     /* if the input par lies outside of the interval (parl,paru), */
     /* set par to the closer endpoint. */
-    par = std::max(par,parl);
-    par = std::min(par,paru);
+    par = (std::max)(par,parl);
+    par = (std::min)(par,paru);
     if (par == 0.)
         par = gnorm / dxnorm;
 
@@ -106,7 +106,7 @@ void lmpar(
 
         /* evaluate the function at the current value of par. */
         if (par == 0.)
-            par = std::max(dwarf,Scalar(.001) * paru); /* Computing MAX */
+            par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
         wa1 = sqrt(par)* diag;
 
         Matrix< Scalar, Dynamic, 1 > sdiag(n);
@@ -139,13 +139,13 @@ void lmpar(
 
         /* depending on the sign of the function, update parl or paru. */
         if (fp > 0.)
-            parl = std::max(parl,par);
+            parl = (std::max)(parl,par);
         if (fp < 0.)
-            paru = std::min(paru,par);
+            paru = (std::min)(paru,par);
 
         /* compute an improved estimate for par. */
         /* Computing MAX */
-        par = std::max(parl,par+parc);
+        par = (std::max)(parl,par+parc);
 
         /* end of an iteration. */
     }
@@ -227,12 +227,12 @@ void lmpar2(
     gnorm = wa1.stableNorm();
     paru = gnorm / delta;
     if (paru == 0.)
-        paru = dwarf / std::min(delta,Scalar(0.1));
+        paru = dwarf / (std::min)(delta,Scalar(0.1));
 
     /* if the input par lies outside of the interval (parl,paru), */
     /* set par to the closer endpoint. */
-    par = std::max(par,parl);
-    par = std::min(par,paru);
+    par = (std::max)(par,parl);
+    par = (std::min)(par,paru);
     if (par == 0.)
         par = gnorm / dxnorm;
 
@@ -243,7 +243,7 @@ void lmpar2(
 
         /* evaluate the function at the current value of par. */
         if (par == 0.)
-            par = std::max(dwarf,Scalar(.001) * paru); /* Computing MAX */
+            par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
         wa1 = sqrt(par)* diag;
 
         Matrix< Scalar, Dynamic, 1 > sdiag(n);
@@ -275,12 +275,12 @@ void lmpar2(
 
         /* depending on the sign of the function, update parl or paru. */
         if (fp > 0.)
-            parl = std::max(parl,par);
+            parl = (std::max)(parl,par);
         if (fp < 0.)
-            paru = std::min(paru,par);
+            paru = (std::min)(paru,par);
 
         /* compute an improved estimate for par. */
-        par = std::max(parl,par+parc);
+        par = (std::max)(parl,par+parc);
     }
     if (iter == 0)
         par = 0.;
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/r1updt.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
index 0b8ede119..528f8eef9 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
@@ -11,6 +11,7 @@ void r1updt(
         bool *sing)
 {
     typedef DenseIndex Index;
+    const JacobiRotation<Scalar> IdentityRotation = JacobiRotation<Scalar>(1,0);
 
     /* Local variables */
     const Index m = s.rows();
@@ -49,7 +50,8 @@ void r1updt(
                 w[i] = givens.s() * s(j,i) + givens.c() * w[i];
                 s(j,i) = temp;
             }
-        }
+        } else
+            v_givens[j] = IdentityRotation;
     }
 
     /* add the spike from the rank 1 update to w. */
@@ -73,7 +75,8 @@ void r1updt(
             /* store the information necessary to recover the */
             /* givens rotation. */
             w_givens[j] = givens;
-        }
+        } else
+            v_givens[j] = IdentityRotation;
 
         /* test for zero diagonal elements in the output s. */
         if (s(j,j) == 0.) {
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
index dbf27c481..52dc0ec01 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
@@ -80,7 +80,7 @@ public:
         Scalar h;
         int nfev=0;
         const typename InputType::Index n = _x.size();
-        const Scalar eps = internal::sqrt((std::max(epsfcn,NumTraits<Scalar>::epsilon() )));
+        const Scalar eps = internal::sqrt(((std::max)(epsfcn,NumTraits<Scalar>::epsilon() )));
         ValueType val1, val2;
         InputType x = _x;
         // TODO : we should do this only if the size is not already known
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/Skyline/SkylineStorage.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/Skyline/SkylineStorage.h
index 13f3e0ced..62806bfb6 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/Skyline/SkylineStorage.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/Skyline/SkylineStorage.h
@@ -221,11 +221,11 @@ protected:
         Index* upperProfile = new Index[upperProfileSize];
         Index* lowerProfile = new Index[lowerProfileSize];
 
-        Index copyDiagSize = std::min(diagSize, m_diagSize);
-        Index copyUpperSize = std::min(upperSize, m_upperSize);
-        Index copyLowerSize = std::min(lowerSize, m_lowerSize);
-        Index copyUpperProfileSize = std::min(upperProfileSize, m_upperProfileSize);
-        Index copyLowerProfileSize = std::min(lowerProfileSize, m_lowerProfileSize);
+        Index copyDiagSize = (std::min)(diagSize, m_diagSize);
+        Index copyUpperSize = (std::min)(upperSize, m_upperSize);
+        Index copyLowerSize = (std::min)(lowerSize, m_lowerSize);
+        Index copyUpperProfileSize = (std::min)(upperProfileSize, m_upperProfileSize);
+        Index copyLowerProfileSize = (std::min)(lowerProfileSize, m_lowerProfileSize);
 
         // copy
         memcpy(diag, m_diag, copyDiagSize * sizeof (Scalar));
diff --git a/bazaar/plugin/Eigen/unsupported/Eigen/src/SparseExtra/UmfPackSupport.h b/bazaar/plugin/Eigen/unsupported/Eigen/src/SparseExtra/UmfPackSupport.h
index df6b3cdf9..beb18f6cd 100644
--- a/bazaar/plugin/Eigen/unsupported/Eigen/src/SparseExtra/UmfPackSupport.h
+++ b/bazaar/plugin/Eigen/unsupported/Eigen/src/SparseExtra/UmfPackSupport.h
@@ -295,10 +295,10 @@ void SparseLU<MatrixType,UmfPack>::extractData() const
     umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
 
     // allocate data
-    m_l.resize(rows,std::min(rows,cols));
+    m_l.resize(rows,(std::min)(rows,cols));
     m_l.resizeNonZeros(lnz);
     
-    m_u.resize(std::min(rows,cols),cols);
+    m_u.resize((std::min)(rows,cols),cols);
     m_u.resizeNonZeros(unz);
 
     m_p.resize(rows);