port unsupported modules to new API

9 files changed, 189 insertions, 189 deletions

diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h b/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
index b45aeae58..b5f4e2b6f 100644
--- a/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
+++ b/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
@@ -31,16 +31,16 @@
 
 /** \ingroup MatrixFunctions_Module
  *
- * \brief Compute the matrix exponential. 
+ * \brief Compute the matrix exponential.
  *
- * \param M      matrix whose exponential is to be computed. 
+ * \param M      matrix whose exponential is to be computed.
  * \param result pointer to the matrix in which to store the result.
  *
  * The matrix exponential of \f$ M \f$ is defined by
  * \f[ \exp(M) = \sum_{k=0}^\infty \frac{M^k}{k!}. \f]
  * The matrix exponential can be used to solve linear ordinary
  * differential equations: the solution of \f$ y' = My \f$ with the
- * initial condition \f$ y(0) = y_0 \f$ is given by 
+ * initial condition \f$ y(0) = y_0 \f$ is given by
  * \f$ y(t) = \exp(M) y_0 \f$.
  *
  * The cost of the computation is approximately \f$ 20 n^3 \f$ for
@@ -54,17 +54,17 @@
  * squaring. The degree of the Padé approximant is chosen such
  * that the approximation error is less than the round-off
  * error. However, errors may accumulate during the squaring phase.
- * 
+ *
  * Details of the algorithm can be found in: Nicholas J. Higham, "The
  * scaling and squaring method for the matrix exponential revisited,"
  * <em>SIAM J. %Matrix Anal. Applic.</em>, <b>26</b>:1179&ndash;1193,
- * 2005. 
+ * 2005.
  *
  * Example: The following program checks that
- * \f[ \exp \left[ \begin{array}{ccc} 
- *       0 & \frac14\pi & 0 \\ 
+ * \f[ \exp \left[ \begin{array}{ccc}
+ *       0 & \frac14\pi & 0 \\
  *       -\frac14\pi & 0 & 0 \\
- *       0 & 0 & 0 
+ *       0 & 0 & 0
  *     \end{array} \right] = \left[ \begin{array}{ccc}
  *       \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
  *       \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
@@ -76,11 +76,11 @@
  * \include MatrixExponential.cpp
  * Output: \verbinclude MatrixExponential.out
  *
- * \note \p M has to be a matrix of \c float, \c double, 
+ * \note \p M has to be a matrix of \c float, \c double,
  * \c complex<float> or \c complex<double> .
  */
 template <typename Derived>
-EIGEN_STRONG_INLINE void ei_matrix_exponential(const MatrixBase<Derived> &M, 
+EIGEN_STRONG_INLINE void ei_matrix_exponential(const MatrixBase<Derived> &M,
 					       typename MatrixBase<Derived>::PlainMatrixType* result);
 
 /** \ingroup MatrixFunctions_Module
@@ -90,13 +90,13 @@ template <typename MatrixType>
 class MatrixExponential {
 
   public:
-  
-    /** \brief Compute the matrix exponential. 
+
+    /** \brief Compute the matrix exponential.
      *
-     * \param M      matrix whose exponential is to be computed. 
+     * \param M      matrix whose exponential is to be computed.
      * \param result pointer to the matrix in which to store the result.
      */
-    MatrixExponential(const MatrixType &M, MatrixType *result);  
+    MatrixExponential(const MatrixType &M, MatrixType *result);
 
   private:
 
@@ -105,7 +105,7 @@ class MatrixExponential {
     MatrixExponential& operator=(const MatrixExponential&);
 
     /** \brief Compute the (3,3)-Pad&eacute; approximant to the exponential.
-     *  
+     *
      *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
      *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
      *
@@ -114,7 +114,7 @@ class MatrixExponential {
     void pade3(const MatrixType &A);
 
     /** \brief Compute the (5,5)-Pad&eacute; approximant to the exponential.
-     *  
+     *
      *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
      *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
      *
@@ -123,7 +123,7 @@ class MatrixExponential {
     void pade5(const MatrixType &A);
 
     /** \brief Compute the (7,7)-Pad&eacute; approximant to the exponential.
-     *  
+     *
      *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
      *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
      *
@@ -132,7 +132,7 @@ class MatrixExponential {
     void pade7(const MatrixType &A);
 
     /** \brief Compute the (9,9)-Pad&eacute; approximant to the exponential.
-     *  
+     *
      *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
      *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
      *
@@ -141,7 +141,7 @@ class MatrixExponential {
     void pade9(const MatrixType &A);
 
     /** \brief Compute the (13,13)-Pad&eacute; approximant to the exponential.
-     *  
+     *
      *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
      *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
      *
@@ -149,10 +149,10 @@ class MatrixExponential {
      */
     void pade13(const MatrixType &A);
 
-    /** \brief Compute Pad&eacute; approximant to the exponential. 
-     *  
-     * Computes \c m_U, \c m_V and \c m_squarings such that 
-     * \f$ (V+U)(V-U)^{-1} \f$ is a Pad&eacute; of 
+    /** \brief Compute Pad&eacute; approximant to the exponential.
+     *
+     * Computes \c m_U, \c m_V and \c m_squarings such that
+     * \f$ (V+U)(V-U)^{-1} \f$ is a Pad&eacute; of
      * \f$ \exp(2^{-\mbox{squarings}}M) \f$ around \f$ M = 0 \f$. The
      * degree of the Pad&eacute; approximant and the value of
      * squarings are chosen such that the approximation error is no
@@ -164,7 +164,7 @@ class MatrixExponential {
      */
     void computeUV(double);
 
-    /** \brief Compute Pad&eacute; approximant to the exponential. 
+    /** \brief Compute Pad&eacute; approximant to the exponential.
      *
      *  \sa computeUV(double);
      */
@@ -174,7 +174,7 @@ class MatrixExponential {
     typedef typename NumTraits<typename ei_traits<MatrixType>::Scalar>::Real RealScalar;
 
     /** \brief Pointer to matrix whose exponential is to be computed. */
-    const MatrixType* m_M; 
+    const MatrixType* m_M;
 
     /** \brief Even-degree terms in numerator of Pad&eacute; approximant. */
     MatrixType m_U;
@@ -200,14 +200,14 @@ class MatrixExponential {
 
 template <typename MatrixType>
 MatrixExponential<MatrixType>::MatrixExponential(const MatrixType &M, MatrixType *result) :
-  m_M(&M), 
-  m_U(M.rows(),M.cols()), 
-  m_V(M.rows(),M.cols()), 
-  m_tmp1(M.rows(),M.cols()), 
-  m_tmp2(M.rows(),M.cols()), 
-  m_Id(MatrixType::Identity(M.rows(), M.cols())), 
-  m_squarings(0), 
-  m_l1norm(static_cast<float>(M.cwise().abs().colwise().sum().maxCoeff()))
+  m_M(&M),
+  m_U(M.rows(),M.cols()),
+  m_V(M.rows(),M.cols()),
+  m_tmp1(M.rows(),M.cols()),
+  m_tmp2(M.rows(),M.cols()),
+  m_Id(MatrixType::Identity(M.rows(), M.cols())),
+  m_squarings(0),
+  m_l1norm(static_cast<float>(M.cwiseAbs().colwise().sum().maxCoeff()))
 {
   computeUV(RealScalar());
   m_tmp1 = m_U + m_V;	// numerator of Pade approximant
@@ -267,8 +267,8 @@ EIGEN_STRONG_INLINE void MatrixExponential<MatrixType>::pade9(const MatrixType &
 template <typename MatrixType>
 EIGEN_STRONG_INLINE void MatrixExponential<MatrixType>::pade13(const MatrixType &A)
 {
-  const Scalar b[] = {64764752532480000., 32382376266240000., 7771770303897600., 
-  		      1187353796428800., 129060195264000., 10559470521600., 670442572800., 
+  const Scalar b[] = {64764752532480000., 32382376266240000., 7771770303897600.,
+  		      1187353796428800., 129060195264000., 10559470521600., 670442572800.,
   		      33522128640., 1323241920., 40840800., 960960., 16380., 182., 1.};
   MatrixType A2 = A * A;
   MatrixType A4 = A2 * A2;
@@ -317,7 +317,7 @@ void MatrixExponential<MatrixType>::computeUV(double)
 }
 
 template <typename Derived>
-EIGEN_STRONG_INLINE void ei_matrix_exponential(const MatrixBase<Derived> &M, 
+EIGEN_STRONG_INLINE void ei_matrix_exponential(const MatrixBase<Derived> &M,
 					       typename MatrixBase<Derived>::PlainMatrixType* result)
 {
   ei_assert(M.rows() == M.cols());
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h b/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
index 117ee82d7..a429b3392 100644
--- a/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
+++ b/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
@@ -25,7 +25,7 @@
 #ifndef EIGEN_MATRIX_FUNCTION_ATOMIC
 #define EIGEN_MATRIX_FUNCTION_ATOMIC
 
-/** \ingroup MatrixFunctions_Module 
+/** \ingroup MatrixFunctions_Module
   * \class MatrixFunctionAtomic
   * \brief Helper class for computing matrix functions of atomic matrices.
   *
@@ -110,30 +110,30 @@ void MatrixFunctionAtomic<MatrixType>::computeMu()
   const MatrixType N = MatrixType::Identity(m_Arows, m_Arows) - m_Ashifted;
   VectorType e = VectorType::Ones(m_Arows);
   N.template triangularView<UpperTriangular>().solveInPlace(e);
-  m_mu = e.cwise().abs().maxCoeff();
+  m_mu = e.cwiseAbs().maxCoeff();
 }
 
 /** \brief Determine whether Taylor series has converged */
 template <typename MatrixType>
-bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType& F, 
+bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType& F,
 						       const MatrixType& Fincr, const MatrixType& P)
 {
   const int n = F.rows();
-  const RealScalar F_norm = F.cwise().abs().rowwise().sum().maxCoeff();
-  const RealScalar Fincr_norm = Fincr.cwise().abs().rowwise().sum().maxCoeff();
+  const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff();
+  const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff();
   if (Fincr_norm < epsilon<Scalar>() * F_norm) {
     RealScalar delta = 0;
     RealScalar rfactorial = 1;
     for (int r = 0; r < n; r++) {
       RealScalar mx = 0;
-      for (int i = 0; i < n; i++) 
+      for (int i = 0; i < n; i++)
 	mx = std::max(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, s+r)));
        if (r != 0)
 	rfactorial *= r;
       delta = std::max(delta, mx / rfactorial);
     }
-    const RealScalar P_norm = P.cwise().abs().rowwise().sum().maxCoeff();
-    if (m_mu * delta * P_norm < epsilon<Scalar>() * F_norm) 
+    const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
+    if (m_mu * delta * P_norm < epsilon<Scalar>() * F_norm)
       return true;
   }
   return false;
diff --git a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
index bcf1016db..3bf3d12ea 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
@@ -36,7 +36,7 @@
   *
   * The user must provide a subroutine which calculates the
   * functions. The Jacobian is either provided by the user, or approximated
-  * using a forward-difference method. 
+  * using a forward-difference method.
   *
   */
 template<typename FunctorType, typename Scalar=double>
@@ -50,7 +50,7 @@ public:
         Running = -1,
         ImproperInputParameters = 0,
         RelativeErrorTooSmall = 1,
-        TooManyFunctionEvaluation = 2, 
+        TooManyFunctionEvaluation = 2,
         TolTooSmall = 3,
         NotMakingProgressJacobian = 4,
         NotMakingProgressIterations = 5,
@@ -156,7 +156,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
     parameters.xtol = tol;
     diag.setConstant(n, 1.);
     return solve(
-        x, 
+        x,
         2
     );
 }
@@ -241,7 +241,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
 
         /* on the first iteration, calculate the norm of the scaled x */
         /* and initialize the step bound delta. */
-        wa3 = diag.cwise() * x;
+        wa3 = diag.cwiseProduct(x);
         xnorm = wa3.stableNorm();
         delta = parameters.factor * xnorm;
         if (delta == 0.)
@@ -285,7 +285,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
 
     /* Computing MAX */
     if (mode != 2)
-        diag = diag.cwise().max(wa2);
+        diag = diag.cwiseMax(wa2);
 
     /* beginning of the inner loop. */
 
@@ -299,7 +299,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
 
         wa1 = -wa1;
         wa2 = x + wa1;
-        wa3 = diag.cwise() * wa1;
+        wa3 = diag.cwiseProduct(wa1);
         pnorm = wa3.stableNorm();
 
         /* on the first iteration, adjust the initial step bound. */
@@ -364,7 +364,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
         if (ratio >= Scalar(1e-4)) {
             /* successful iteration. update x, fvec, and their norms. */
             x = wa2;
-            wa2 = diag.cwise() * x;
+            wa2 = diag.cwiseProduct(x);
             fvec = wa4;
             xnorm = wa2.stableNorm();
             fnorm = fnorm1;
@@ -555,7 +555,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
 
         /* on the first iteration, calculate the norm of the scaled x */
         /* and initialize the step bound delta. */
-        wa3 = diag.cwise() * x;
+        wa3 = diag.cwiseProduct(x);
         xnorm = wa3.stableNorm();
         delta = parameters.factor * xnorm;
         if (delta == 0.)
@@ -599,7 +599,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
 
     /* Computing MAX */
     if (mode != 2)
-        diag = diag.cwise().max(wa2);
+        diag = diag.cwiseMax(wa2);
 
     /* beginning of the inner loop. */
 
@@ -613,7 +613,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
 
         wa1 = -wa1;
         wa2 = x + wa1;
-        wa3 = diag.cwise() * wa1;
+        wa3 = diag.cwiseProduct(wa1);
         pnorm = wa3.stableNorm();
 
         /* on the first iteration, adjust the initial step bound. */
@@ -678,7 +678,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
         if (ratio >= Scalar(1e-4)) {
             /* successful iteration. update x, fvec, and their norms. */
             x = wa2;
-            wa2 = diag.cwise() * x;
+            wa2 = diag.cwiseProduct(x);
             fvec = wa4;
             xnorm = wa2.stableNorm();
             fnorm = fnorm1;
diff --git a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
index 5ab440863..1609924b9 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
@@ -37,7 +37,7 @@
   * http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
   */
 template<typename FunctorType, typename Scalar=double>
-class LevenbergMarquardt 
+class LevenbergMarquardt
 {
 public:
     LevenbergMarquardt(FunctorType &_functor)
@@ -50,7 +50,7 @@ public:
         RelativeErrorTooSmall = 2,
         RelativeErrorAndReductionTooSmall = 3,
         CosinusTooSmall = 4,
-        TooManyFunctionEvaluation = 5, 
+        TooManyFunctionEvaluation = 5,
         FtolTooSmall = 6,
         XtolTooSmall = 7,
         GtolTooSmall = 8,
@@ -253,7 +253,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
     wa2 = fjac.colwise().blueNorm();
     ei_qrfac<Scalar>(m, n, fjac.data(), fjac.rows(), true, ipvt.data(), wa1.data());
-    ipvt.cwise()-=1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
+    ipvt.array() -= 1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
 
     /* on the first iteration and if mode is 1, scale according */
     /* to the norms of the columns of the initial jacobian. */
@@ -269,7 +269,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
         /* on the first iteration, calculate the norm of the scaled x */
         /* and initialize the step bound delta. */
 
-        wa3 = diag.cwise() * x;
+        wa3 = diag.cwiseProduct(x);
         xnorm = wa3.stableNorm();
         delta = parameters.factor * xnorm;
         if (delta == 0.)
@@ -316,7 +316,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
     /* rescale if necessary. */
 
     if (mode != 2) /* Computing MAX */
-        diag = diag.cwise().max(wa2);
+        diag = diag.cwiseMax(wa2);
 
     /* beginning of the inner loop. */
     do {
@@ -329,7 +329,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
         wa1 = -wa1;
         wa2 = x + wa1;
-        wa3 = diag.cwise() * wa1;
+        wa3 = diag.cwiseProduct(wa1);
         pnorm = wa3.stableNorm();
 
         /* on the first iteration, adjust the initial step bound. */
@@ -395,7 +395,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
         if (ratio >= Scalar(1e-4)) {
             /* successful iteration. update x, fvec, and their norms. */
             x = wa2;
-            wa2 = diag.cwise() * x;
+            wa2 = diag.cwiseProduct(x);
             fvec = wa4;
             xnorm = wa2.stableNorm();
             fnorm = fnorm1;
@@ -538,10 +538,10 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
         wa2[j] = fjac.col(j).head(j).stableNorm();
     }
     if (sing) {
-        ipvt.cwise()+=1;
+        ipvt.array() += 1;
         wa2 = fjac.colwise().blueNorm();
         ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), true, ipvt.data(), wa1.data());
-        ipvt.cwise()-=1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
+        ipvt.array() -= 1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
         for (j = 0; j < n; ++j) {
             if (fjac(j,j) != 0.) {
                 sum = 0.;
@@ -569,7 +569,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
         /* on the first iteration, calculate the norm of the scaled x */
         /* and initialize the step bound delta. */
 
-        wa3 = diag.cwise() * x;
+        wa3 = diag.cwiseProduct(x);
         xnorm = wa3.stableNorm();
         delta = parameters.factor * xnorm;
         if (delta == 0.)
@@ -599,7 +599,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
     /* rescale if necessary. */
 
     if (mode != 2) /* Computing MAX */
-        diag = diag.cwise().max(wa2);
+        diag = diag.cwiseMax(wa2);
 
     /* beginning of the inner loop. */
     do {
@@ -612,7 +612,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
 
         wa1 = -wa1;
         wa2 = x + wa1;
-        wa3 = diag.cwise() * wa1;
+        wa3 = diag.cwiseProduct(wa1);
         pnorm = wa3.stableNorm();
 
         /* on the first iteration, adjust the initial step bound. */
@@ -678,7 +678,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
         if (ratio >= Scalar(1e-4)) {
             /* successful iteration. update x, fvec, and their norms. */
             x = wa2;
-            wa2 = diag.cwise() * x;
+            wa2 = diag.cwiseProduct(x);
             fvec = wa4;
             xnorm = wa2.stableNorm();
             fnorm = fnorm1;
diff --git a/unsupported/Eigen/src/NonLinearOptimization/dogleg.h b/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
index 3485c5796..dd6b39cb4 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
@@ -50,7 +50,7 @@ void ei_dogleg(
     /* test whether the gauss-newton direction is acceptable. */
 
     wa1.fill(0.);
-    wa2 = diag.cwise() * x;
+    wa2 = diag.cwiseProduct(x);
     qnorm = wa2.stableNorm();
     if (qnorm <= delta)
         return;
@@ -80,7 +80,7 @@ void ei_dogleg(
     /* calculate the point along the scaled gradient */
     /* at which the quadratic is minimized. */
 
-    wa1.cwise() /= diag*gnorm;
+    wa1.array() /= (diag*gnorm).array();
     l = 0;
     for (j = 0; j < n; ++j) {
         sum = 0.;
diff --git a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
index e5b66c0d7..ab8549f1a 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
@@ -36,7 +36,7 @@ void ei_lmpar(
     for (j = 0; j < n; ++j) {
         if (r(j,j) == 0. && nsing == n-1)
             nsing = j - 1;
-        if (nsing < n-1) 
+        if (nsing < n-1)
             wa1[j] = 0.;
     }
     for (j = nsing; j>=0; --j) {
@@ -54,7 +54,7 @@ void ei_lmpar(
     /* for acceptance of the gauss-newton direction. */
 
     iter = 0;
-    wa2 = diag.cwise() * x;
+    wa2 = diag.cwiseProduct(x);
     dxnorm = wa2.blueNorm();
     fp = dxnorm - delta;
     if (fp <= Scalar(0.1) * delta) {
@@ -76,7 +76,7 @@ void ei_lmpar(
         // way:
         for (j = 0; j < n; ++j) {
             Scalar sum = 0.;
-            for (i = 0; i < j; ++i) 
+            for (i = 0; i < j; ++i)
                 sum += r(i,j) * wa1[i];
             wa1[j] = (wa1[j] - sum) / r(j,j);
         }
@@ -117,7 +117,7 @@ void ei_lmpar(
         Matrix< Scalar, Dynamic, 1 > sdiag(n);
         ei_qrsolv<Scalar>(r, ipvt, wa1, qtb, x, sdiag);
 
-        wa2 = diag.cwise() * x;
+        wa2 = diag.cwiseProduct(x);
         dxnorm = wa2.blueNorm();
         temp = fp;
         fp = dxnorm - delta;
diff --git a/unsupported/test/BVH.cpp b/unsupported/test/BVH.cpp
index 6d4bb70dc..4abf7f5bd 100644
--- a/unsupported/test/BVH.cpp
+++ b/unsupported/test/BVH.cpp
@@ -45,7 +45,7 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(double, Dim)
 
 template<typename Scalar, int Dim> AlignedBox<Scalar, Dim> ei_bounding_box(const Matrix<Scalar, Dim, 1> &v) { return AlignedBox<Scalar, Dim>(v); }
 template<int Dim> AlignedBox<double, Dim> ei_bounding_box(const Ball<Dim> &b)
-{ return AlignedBox<double, Dim>(b.center.cwise() - b.radius, b.center.cwise() + b.radius); }
+{ return AlignedBox<double, Dim>(b.center.array() - b.radius, b.center.array() + b.radius); }
 
 
 template<int Dim>
diff --git a/unsupported/test/NonLinearOptimization.cpp b/unsupported/test/NonLinearOptimization.cpp
index 4106c1e38..ae587f016 100644
--- a/unsupported/test/NonLinearOptimization.cpp
+++ b/unsupported/test/NonLinearOptimization.cpp
@@ -20,10 +20,10 @@ int fcn_chkder(const VectorXd &x, VectorXd &fvec, MatrixXd &fjac, int iflag)
         3.9e-1, 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34, 2.1, 4.39};
 
 
-    if (iflag == 0) 
+    if (iflag == 0)
         return 0;
 
-    if (iflag != 2) 
+    if (iflag != 2)
         for (i=0; i<15; i++) {
             tmp1 = i+1;
             tmp2 = 16-i-1;
@@ -108,12 +108,12 @@ struct Functor
   typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
   typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
   typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
-  
+
   int m_inputs, m_values;
-  
+
   Functor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
   Functor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
-  
+
   int inputs() const { return m_inputs; }
   int values() const { return m_values; }
 
@@ -219,7 +219,7 @@ void testLmder()
   ei_covar(lm.fjac, lm.ipvt); // TODO : move this as a function of lm
 
   MatrixXd cov_ref(n,n);
-  cov_ref << 
+  cov_ref <<
       0.0001531202,   0.002869941,  -0.002656662,
       0.002869941,    0.09480935,   -0.09098995,
       -0.002656662,   -0.09098995,    0.08778727;
@@ -229,7 +229,7 @@ void testLmder()
   MatrixXd cov;
   cov =  covfac*lm.fjac.corner<n,n>(TopLeft);
   VERIFY_IS_APPROX( cov, cov_ref);
-  // TODO: why isn't this allowed ? : 
+  // TODO: why isn't this allowed ? :
   // VERIFY_IS_APPROX( covfac*fjac.corner<n,n>(TopLeft) , cov_ref);
 }
 
@@ -296,7 +296,7 @@ void testHybrj1()
 
 // check x
   VectorXd x_ref(n);
-  x_ref << 
+  x_ref <<
      -0.5706545,    -0.6816283,    -0.7017325,
      -0.7042129,     -0.701369,    -0.6918656,
      -0.665792,    -0.5960342,    -0.4164121;
@@ -330,7 +330,7 @@ void testHybrj()
 
 // check x
   VectorXd x_ref(n);
-  x_ref << 
+  x_ref <<
      -0.5706545,    -0.6816283,    -0.7017325,
      -0.7042129,     -0.701369,    -0.6918656,
      -0.665792,    -0.5960342,    -0.4164121;
@@ -412,7 +412,7 @@ void testHybrd()
 
   // check x
   VectorXd x_ref(n);
-  x_ref << 
+  x_ref <<
       -0.5706545,    -0.6816283,    -0.7017325,
       -0.7042129,     -0.701369,    -0.6918656,
       -0.665792,    -0.5960342,    -0.4164121;
@@ -608,7 +608,7 @@ void testLmdif()
   ei_covar(lm.fjac, lm.ipvt);
 
   MatrixXd cov_ref(n,n);
-  cov_ref << 
+  cov_ref <<
       0.0001531202,   0.002869942,  -0.002656662,
       0.002869942,    0.09480937,   -0.09098997,
       -0.002656662,   -0.09098997,    0.08778729;
@@ -618,7 +618,7 @@ void testLmdif()
   MatrixXd cov;
   cov =  covfac*lm.fjac.corner<n,n>(TopLeft);
   VERIFY_IS_APPROX( cov, cov_ref);
-  // TODO: why isn't this allowed ? : 
+  // TODO: why isn't this allowed ? :
   // VERIFY_IS_APPROX( covfac*fjac.corner<n,n>(TopLeft) , cov_ref);
 }
 
@@ -676,11 +676,11 @@ void testNistChwirut2(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 10 == lm.nfev); 
-  VERIFY( 8 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 10 == lm.nfev);
+  VERIFY( 8 == lm.njev);
   // check norm^2
-  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02); 
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
   // check x
   VERIFY_IS_APPROX(x[0], 1.6657666537E-01);
   VERIFY_IS_APPROX(x[1], 5.1653291286E-03);
@@ -697,11 +697,11 @@ void testNistChwirut2(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 7 == lm.nfev); 
-  VERIFY( 6 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 7 == lm.nfev);
+  VERIFY( 6 == lm.njev);
   // check norm^2
-  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02); 
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
   // check x
   VERIFY_IS_APPROX(x[0], 1.6657666537E-01);
   VERIFY_IS_APPROX(x[1], 5.1653291286E-03);
@@ -756,11 +756,11 @@ void testNistMisra1a(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 19 == lm.nfev); 
-  VERIFY( 15 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 19 == lm.nfev);
+  VERIFY( 15 == lm.njev);
   // check norm^2
-  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01); 
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
   // check x
   VERIFY_IS_APPROX(x[0], 2.3894212918E+02);
   VERIFY_IS_APPROX(x[1], 5.5015643181E-04);
@@ -773,11 +773,11 @@ void testNistMisra1a(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 5 == lm.nfev); 
-  VERIFY( 4 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 5 == lm.nfev);
+  VERIFY( 4 == lm.njev);
   // check norm^2
-  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01); 
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
   // check x
   VERIFY_IS_APPROX(x[0], 2.3894212918E+02);
   VERIFY_IS_APPROX(x[1], 5.5015643181E-04);
@@ -842,11 +842,11 @@ void testNistHahn1(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 11== lm.nfev); 
-  VERIFY( 10== lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 11== lm.nfev);
+  VERIFY( 10== lm.njev);
   // check norm^2
-  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00); 
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
   // check x
   VERIFY_IS_APPROX(x[0], 1.0776351733E+00  );
   VERIFY_IS_APPROX(x[1],-1.2269296921E-01  );
@@ -864,18 +864,18 @@ void testNistHahn1(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 11 == lm.nfev); 
-  VERIFY( 10 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 11 == lm.nfev);
+  VERIFY( 10 == lm.njev);
   // check norm^2
-  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00); 
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
   // check x
   VERIFY_IS_APPROX(x[0], 1.077640); // should be :  1.0776351733E+00
   VERIFY_IS_APPROX(x[1], -0.1226933); // should be : -1.2269296921E-01
   VERIFY_IS_APPROX(x[2], 0.004086383); // should be : 4.0863750610E-03
   VERIFY_IS_APPROX(x[3], -1.426277e-06); // shoulde be : -1.4262662514E-06
   VERIFY_IS_APPROX(x[4],-5.7609940901E-03  );
-  VERIFY_IS_APPROX(x[5], 0.00024053772); // should be : 2.4053735503E-04 
+  VERIFY_IS_APPROX(x[5], 0.00024053772); // should be : 2.4053735503E-04
   VERIFY_IS_APPROX(x[6], -1.231450e-07); // should be : -1.2314450199E-07
 
 }
@@ -928,11 +928,11 @@ void testNistMisra1d(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 3 == info); 
-  VERIFY( 9 == lm.nfev); 
-  VERIFY( 7 == lm.njev); 
+  VERIFY( 3 == info);
+  VERIFY( 9 == lm.nfev);
+  VERIFY( 7 == lm.njev);
   // check norm^2
-  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02); 
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
   // check x
   VERIFY_IS_APPROX(x[0], 4.3736970754E+02);
   VERIFY_IS_APPROX(x[1], 3.0227324449E-04);
@@ -945,11 +945,11 @@ void testNistMisra1d(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 4 == lm.nfev); 
-  VERIFY( 3 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 4 == lm.nfev);
+  VERIFY( 3 == lm.njev);
   // check norm^2
-  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02); 
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
   // check x
   VERIFY_IS_APPROX(x[0], 4.3736970754E+02);
   VERIFY_IS_APPROX(x[1], 3.0227324449E-04);
@@ -1006,9 +1006,9 @@ void testNistLanczos1(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 2 == info); 
-  VERIFY( 79 == lm.nfev); 
-  VERIFY( 72 == lm.njev); 
+  VERIFY( 2 == info);
+  VERIFY( 79 == lm.nfev);
+  VERIFY( 72 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.429604433690E-25);  // should be 1.4307867721E-25, but nist results are on 128-bit floats
   // check x
@@ -1027,9 +1027,9 @@ void testNistLanczos1(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 2 == info); 
-  VERIFY( 9 == lm.nfev); 
-  VERIFY( 8 == lm.njev); 
+  VERIFY( 2 == info);
+  VERIFY( 9 == lm.nfev);
+  VERIFY( 8 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.43049947737308E-25);  // should be 1.4307867721E-25, but nist results are on 128-bit floats
   // check x
@@ -1092,9 +1092,9 @@ void testNistRat42(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 10 == lm.nfev); 
-  VERIFY( 8 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 10 == lm.nfev);
+  VERIFY( 8 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.0565229338E+00);
   // check x
@@ -1110,9 +1110,9 @@ void testNistRat42(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 6 == lm.nfev); 
-  VERIFY( 5 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 6 == lm.nfev);
+  VERIFY( 5 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.0565229338E+00);
   // check x
@@ -1170,9 +1170,9 @@ void testNistMGH10(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 2 == info); 
-  VERIFY( 285 == lm.nfev); 
-  VERIFY( 250 == lm.njev); 
+  VERIFY( 2 == info);
+  VERIFY( 285 == lm.nfev);
+  VERIFY( 250 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7945855171E+01);
   // check x
@@ -1188,9 +1188,9 @@ void testNistMGH10(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 2 == info); 
-  VERIFY( 126 == lm.nfev); 
-  VERIFY( 116 == lm.njev); 
+  VERIFY( 2 == info);
+  VERIFY( 126 == lm.nfev);
+  VERIFY( 116 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7945855171E+01);
   // check x
@@ -1249,9 +1249,9 @@ void testNistBoxBOD(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 31 == lm.nfev); 
-  VERIFY( 25  == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 31 == lm.nfev);
+  VERIFY( 25  == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.1680088766E+03);
   // check x
@@ -1269,9 +1269,9 @@ void testNistBoxBOD(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 15 == lm.nfev); 
-  VERIFY( 14 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 15 == lm.nfev);
+  VERIFY( 14 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.1680088766E+03);
   // check x
@@ -1288,7 +1288,7 @@ struct MGH17_functor : Functor<double>
     {
         assert(b.size()==5);
         assert(fvec.size()==33);
-        for(int i=0; i<33; i++) 
+        for(int i=0; i<33; i++)
             fvec[i] =  b[0] + b[1]*exp(-b[3]*x[i]) +  b[2]*exp(-b[4]*x[i]) - y[i];
         return 0;
     }
@@ -1331,9 +1331,9 @@ void testNistMGH17(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 599 == lm.nfev); 
-  VERIFY( 544 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 599 == lm.nfev);
+  VERIFY( 544 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.4648946975E-05);
   // check x
@@ -1352,9 +1352,9 @@ void testNistMGH17(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 18 == lm.nfev); 
-  VERIFY( 15 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 18 == lm.nfev);
+  VERIFY( 15 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.4648946975E-05);
   // check x
@@ -1418,9 +1418,9 @@ void testNistMGH09(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 503== lm.nfev); 
-  VERIFY( 385 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 503== lm.nfev);
+  VERIFY( 385 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 3.0750560385E-04);
   // check x
@@ -1438,9 +1438,9 @@ void testNistMGH09(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 18 == lm.nfev); 
-  VERIFY( 16 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 18 == lm.nfev);
+  VERIFY( 16 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 3.0750560385E-04);
   // check x
@@ -1501,9 +1501,9 @@ void testNistBennett5(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 758 == lm.nfev); 
-  VERIFY( 744 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 758 == lm.nfev);
+  VERIFY( 744 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.2404744073E-04);
   // check x
@@ -1519,9 +1519,9 @@ void testNistBennett5(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 203 == lm.nfev); 
-  VERIFY( 192 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 203 == lm.nfev);
+  VERIFY( 192 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.2404744073E-04);
   // check x
@@ -1589,11 +1589,11 @@ void testNistThurber(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 39 == lm.nfev); 
-  VERIFY( 36== lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 39 == lm.nfev);
+  VERIFY( 36== lm.njev);
   // check norm^2
-  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03); 
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03);
   // check x
   VERIFY_IS_APPROX(x[0], 1.2881396800E+03);
   VERIFY_IS_APPROX(x[1], 1.4910792535E+03);
@@ -1614,11 +1614,11 @@ void testNistThurber(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 29 == lm.nfev); 
-  VERIFY( 28 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 29 == lm.nfev);
+  VERIFY( 28 == lm.njev);
   // check norm^2
-  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03); 
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03);
   // check x
   VERIFY_IS_APPROX(x[0], 1.2881396800E+03);
   VERIFY_IS_APPROX(x[1], 1.4910792535E+03);
@@ -1681,9 +1681,9 @@ void testNistRat43(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 27 == lm.nfev); 
-  VERIFY( 20 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 27 == lm.nfev);
+  VERIFY( 20 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7864049080E+03);
   // check x
@@ -1703,9 +1703,9 @@ void testNistRat43(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 9 == lm.nfev); 
-  VERIFY( 8 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 9 == lm.nfev);
+  VERIFY( 8 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7864049080E+03);
   // check x
@@ -1766,9 +1766,9 @@ void testNistEckerle4(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 18 == lm.nfev); 
-  VERIFY( 15 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 18 == lm.nfev);
+  VERIFY( 15 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.4635887487E-03);
   // check x
@@ -1784,9 +1784,9 @@ void testNistEckerle4(void)
   info = lm.minimize(x);
 
   // check return value
-  VERIFY( 1 == info); 
-  VERIFY( 7 == lm.nfev); 
-  VERIFY( 6 == lm.njev); 
+  VERIFY( 1 == info);
+  VERIFY( 7 == lm.nfev);
+  VERIFY( 6 == lm.njev);
   // check norm^2
   VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.4635887487E-03);
   // check x
diff --git a/unsupported/test/matrix_exponential.cpp b/unsupported/test/matrix_exponential.cpp
index f155e5f98..a5b40adde 100644
--- a/unsupported/test/matrix_exponential.cpp
+++ b/unsupported/test/matrix_exponential.cpp
@@ -25,7 +25,7 @@
 #include "main.h"
 #include <unsupported/Eigen/MatrixFunctions>
 
-double binom(int n, int k) 
+double binom(int n, int k)
 {
   double res = 1;
   for (int i=0; i<k; i++)
@@ -36,7 +36,7 @@ double binom(int n, int k)
 template <typename Derived, typename OtherDerived>
 double relerr(const MatrixBase<Derived>& A, const MatrixBase<OtherDerived>& B)
 {
-  return std::sqrt((A - B).cwise().abs2().sum() / std::min(A.cwise().abs2().sum(), B.cwise().abs2().sum()));
+  return std::sqrt((A - B).cwiseAbs2().sum() / std::min(A.cwiseAbs2().sum(), B.cwiseAbs2().sum()));
 }
 
 template <typename T>
@@ -52,7 +52,7 @@ void test2dRotation(double tol)
   T angle;
 
   A << 0, 1, -1, 0;
-  for (int i=0; i<=20; i++) 
+  for (int i=0; i<=20; i++)
   {
     angle = static_cast<T>(pow(10, i / 5. - 2));
     B << cos(angle), sin(angle), -sin(angle), cos(angle);
@@ -74,7 +74,7 @@ void test2dHyperbolicRotation(double tol)
   std::complex<T> imagUnit(0,1);
   T angle, ch, sh;
 
-  for (int i=0; i<=20; i++) 
+  for (int i=0; i<=20; i++)
   {
     angle = static_cast<T>((i-10) / 2.0);
     ch = std::cosh(angle);
@@ -116,7 +116,7 @@ void testPascal(double tol)
   }
 }
 
-template<typename MatrixType> 
+template<typename MatrixType>
 void randomTest(const MatrixType& m, double tol)
 {
   /* this test covers the following files:
@@ -157,7 +157,7 @@ void test_matrix_exponential()
   CALL_SUBTEST_3(randomTest(Matrix4cd(), 1e-13));
   CALL_SUBTEST_4(randomTest(MatrixXd(8,8), 1e-13));
   CALL_SUBTEST_1(randomTest(Matrix2f(), 1e-4));
-  CALL_SUBTEST_5(randomTest(Matrix3cf(), 1e-4)); 
+  CALL_SUBTEST_5(randomTest(Matrix3cf(), 1e-4));
   CALL_SUBTEST_1(randomTest(Matrix4f(), 1e-4));
   CALL_SUBTEST_6(randomTest(MatrixXf(8,8), 1e-4));
 }