Revert "Revert "Adds EIGEN_CONSTEXPR and EIGEN_NOEXCEPT to rows(), cols(), innerStride(), outerStride(), and size()""

This reverts commit 5f0b4a4010af4cbf6161a0d1a03a747addc44a5d.

5 files changed, 130 insertions, 128 deletions

diff --git a/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h b/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
index f66c846ef..a117fc155 100644
--- a/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
+++ b/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
@@ -10,7 +10,7 @@
 #ifndef EIGEN_BASIC_PRECONDITIONERS_H
 #define EIGEN_BASIC_PRECONDITIONERS_H
 
-namespace Eigen { 
+namespace Eigen {
 
 /** \ingroup IterativeLinearSolvers_Module
   * \brief A preconditioner based on the digonal entries
@@ -52,15 +52,15 @@ class DiagonalPreconditioner
       compute(mat);
     }
 
-    Index rows() const { return m_invdiag.size(); }
-    Index cols() const { return m_invdiag.size(); }
-    
+    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_invdiag.size(); }
+    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_invdiag.size(); }
+
     template<typename MatType>
     DiagonalPreconditioner& analyzePattern(const MatType& )
     {
       return *this;
     }
-    
+
     template<typename MatType>
     DiagonalPreconditioner& factorize(const MatType& mat)
     {
@@ -77,7 +77,7 @@ class DiagonalPreconditioner
       m_isInitialized = true;
       return *this;
     }
-    
+
     template<typename MatType>
     DiagonalPreconditioner& compute(const MatType& mat)
     {
@@ -99,7 +99,7 @@ class DiagonalPreconditioner
                 && "DiagonalPreconditioner::solve(): invalid number of rows of the right hand side matrix b");
       return Solve<DiagonalPreconditioner, Rhs>(*this, b.derived());
     }
-    
+
     ComputationInfo info() { return Success; }
 
   protected:
@@ -121,7 +121,7 @@ class DiagonalPreconditioner
   * \implsparsesolverconcept
   *
   * The diagonal entries are pre-inverted and stored into a dense vector.
-  * 
+  *
   * \sa class LeastSquaresConjugateGradient, class DiagonalPreconditioner
   */
 template <typename _Scalar>
@@ -146,7 +146,7 @@ class LeastSquareDiagonalPreconditioner : public DiagonalPreconditioner<_Scalar>
     {
       return *this;
     }
-    
+
     template<typename MatType>
     LeastSquareDiagonalPreconditioner& factorize(const MatType& mat)
     {
@@ -178,13 +178,13 @@ class LeastSquareDiagonalPreconditioner : public DiagonalPreconditioner<_Scalar>
       Base::m_isInitialized = true;
       return *this;
     }
-    
+
     template<typename MatType>
     LeastSquareDiagonalPreconditioner& compute(const MatType& mat)
     {
       return factorize(mat);
     }
-    
+
     ComputationInfo info() { return Success; }
 
   protected:
@@ -205,19 +205,19 @@ class IdentityPreconditioner
 
     template<typename MatrixType>
     explicit IdentityPreconditioner(const MatrixType& ) {}
-    
+
     template<typename MatrixType>
     IdentityPreconditioner& analyzePattern(const MatrixType& ) { return *this; }
-    
+
     template<typename MatrixType>
     IdentityPreconditioner& factorize(const MatrixType& ) { return *this; }
 
     template<typename MatrixType>
     IdentityPreconditioner& compute(const MatrixType& ) { return *this; }
-    
+
     template<typename Rhs>
     inline const Rhs& solve(const Rhs& b) const { return b; }
-    
+
     ComputationInfo info() { return Success; }
 };
 
diff --git a/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h b/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
index e5d0308ec..7803fd817 100644
--- a/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
+++ b/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
@@ -14,8 +14,8 @@
 #include <vector>
 #include <list>
 
-namespace Eigen {  
-/** 
+namespace Eigen {
+/**
   * \brief Modified Incomplete Cholesky with dual threshold
   *
   * References : C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
@@ -48,15 +48,15 @@ class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_Up
     typedef SparseSolverBase<IncompleteCholesky<Scalar,_UpLo,_OrderingType> > Base;
     using Base::m_isInitialized;
   public:
-    typedef typename NumTraits<Scalar>::Real RealScalar; 
+    typedef typename NumTraits<Scalar>::Real RealScalar;
     typedef _OrderingType OrderingType;
     typedef typename OrderingType::PermutationType PermutationType;
-    typedef typename PermutationType::StorageIndex StorageIndex; 
+    typedef typename PermutationType::StorageIndex StorageIndex;
     typedef SparseMatrix<Scalar,ColMajor,StorageIndex> FactorType;
     typedef Matrix<Scalar,Dynamic,1> VectorSx;
     typedef Matrix<RealScalar,Dynamic,1> VectorRx;
     typedef Matrix<StorageIndex,Dynamic, 1> VectorIx;
-    typedef std::vector<std::list<StorageIndex> > VectorList; 
+    typedef std::vector<std::list<StorageIndex> > VectorList;
     enum { UpLo = _UpLo };
     enum {
       ColsAtCompileTime = Dynamic,
@@ -71,7 +71,7 @@ class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_Up
       * \sa IncompleteCholesky(const MatrixType&)
       */
     IncompleteCholesky() : m_initialShift(1e-3),m_analysisIsOk(false),m_factorizationIsOk(false) {}
-    
+
     /** Constructor computing the incomplete factorization for the given matrix \a matrix.
       */
     template<typename MatrixType>
@@ -79,13 +79,13 @@ class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_Up
     {
       compute(matrix);
     }
-    
+
     /** \returns number of rows of the factored matrix */
-    Index rows() const { return m_L.rows(); }
-    
+    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_L.rows(); }
+
     /** \returns number of columns of the factored matrix */
-    Index cols() const { return m_L.cols(); }
-    
+    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_L.cols(); }
+
 
     /** \brief Reports whether previous computation was successful.
       *
@@ -100,19 +100,19 @@ class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_Up
       eigen_assert(m_isInitialized && "IncompleteCholesky is not initialized.");
       return m_info;
     }
-    
+
     /** \brief Set the initial shift parameter \f$ \sigma \f$.
       */
     void setInitialShift(RealScalar shift) { m_initialShift = shift; }
-    
+
     /** \brief Computes the fill reducing permutation vector using the sparsity pattern of \a mat
       */
     template<typename MatrixType>
     void analyzePattern(const MatrixType& mat)
     {
-      OrderingType ord; 
+      OrderingType ord;
       PermutationType pinv;
-      ord(mat.template selfadjointView<UpLo>(), pinv); 
+      ord(mat.template selfadjointView<UpLo>(), pinv);
       if(pinv.size()>0) m_perm = pinv.inverse();
       else              m_perm.resize(0);
       m_L.resize(mat.rows(), mat.cols());
@@ -120,7 +120,7 @@ class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_Up
       m_isInitialized = true;
       m_info = Success;
     }
-    
+
     /** \brief Performs the numerical factorization of the input matrix \a mat
       *
       * The method analyzePattern() or compute() must have been called beforehand
@@ -130,7 +130,7 @@ class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_Up
       */
     template<typename MatrixType>
     void factorize(const MatrixType& mat);
-    
+
     /** Computes or re-computes the incomplete Cholesky factorization of the input matrix \a mat
       *
       * It is a shortcut for a sequential call to the analyzePattern() and factorize() methods.
@@ -143,7 +143,7 @@ class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_Up
       analyzePattern(mat);
       factorize(mat);
     }
-    
+
     // internal
     template<typename Rhs, typename Dest>
     void _solve_impl(const Rhs& b, Dest& x) const
@@ -170,16 +170,16 @@ class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_Up
 
   protected:
     FactorType m_L;              // The lower part stored in CSC
-    VectorRx m_scale;            // The vector for scaling the matrix 
+    VectorRx m_scale;            // The vector for scaling the matrix
     RealScalar m_initialShift;   // The initial shift parameter
-    bool m_analysisIsOk; 
-    bool m_factorizationIsOk; 
+    bool m_analysisIsOk;
+    bool m_factorizationIsOk;
     ComputationInfo m_info;
-    PermutationType m_perm; 
+    PermutationType m_perm;
 
   private:
-    inline void updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col, const Index& jk, VectorIx& firstElt, VectorList& listCol); 
-}; 
+    inline void updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col, const Index& jk, VectorIx& firstElt, VectorList& listCol);
+};
 
 // Based on the following paper:
 //   C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
@@ -190,10 +190,10 @@ template<typename _MatrixType>
 void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType& mat)
 {
   using std::sqrt;
-  eigen_assert(m_analysisIsOk && "analyzePattern() should be called first"); 
-    
+  eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
+
   // Dropping strategy : Keep only the p largest elements per column, where p is the number of elements in the column of the original matrix. Other strategies will be added
-  
+
   // Apply the fill-reducing permutation computed in analyzePattern()
   if (m_perm.rows() == mat.rows() ) // To detect the null permutation
   {
@@ -206,8 +206,8 @@ void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType
   {
     m_L.template selfadjointView<Lower>() = mat.template selfadjointView<_UpLo>();
   }
-  
-  Index n = m_L.cols(); 
+
+  Index n = m_L.cols();
   Index nnz = m_L.nonZeros();
   Map<VectorSx> vals(m_L.valuePtr(), nnz);         //values
   Map<VectorIx> rowIdx(m_L.innerIndexPtr(), nnz);  //Row indices
@@ -219,9 +219,9 @@ void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType
   VectorIx col_pattern(n);
   col_pattern.fill(-1);
   StorageIndex col_nnz;
-  
-  
-  // Computes the scaling factors 
+
+
+  // Computes the scaling factors
   m_scale.resize(n);
   m_scale.setZero();
   for (Index j = 0; j < n; j++)
@@ -231,7 +231,7 @@ void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType
       if(rowIdx[k]!=j)
         m_scale(rowIdx[k]) += numext::abs2(vals(k));
     }
-  
+
   m_scale = m_scale.cwiseSqrt().cwiseSqrt();
 
   for (Index j = 0; j < n; ++j)
@@ -241,8 +241,8 @@ void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType
       m_scale(j) = 1;
 
   // TODO disable scaling if not needed, i.e., if it is roughly uniform? (this will make solve() faster)
-  
-  // Scale and compute the shift for the matrix 
+
+  // Scale and compute the shift for the matrix
   RealScalar mindiag = NumTraits<RealScalar>::highest();
   for (Index j = 0; j < n; j++)
   {
@@ -253,7 +253,7 @@ void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType
   }
 
   FactorType L_save = m_L;
-  
+
   RealScalar shift = 0;
   if(mindiag <= RealScalar(0.))
     shift = m_initialShift - mindiag;
@@ -375,7 +375,7 @@ inline void IncompleteCholesky<Scalar,_UpLo, OrderingType>::updateList(Ref<const
   if (jk < colPtr(col+1) )
   {
     Index p = colPtr(col+1) - jk;
-    Index minpos; 
+    Index minpos;
     rowIdx.segment(jk,p).minCoeff(&minpos);
     minpos += jk;
     if (rowIdx(minpos) != rowIdx(jk))
@@ -389,6 +389,6 @@ inline void IncompleteCholesky<Scalar,_UpLo, OrderingType>::updateList(Ref<const
   }
 }
 
-} // end namespace Eigen 
+} // end namespace Eigen
 
 #endif
diff --git a/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
index 09436cb67..cdcf709eb 100644
--- a/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
+++ b/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
@@ -12,19 +12,19 @@
 #define EIGEN_INCOMPLETE_LUT_H
 
 
-namespace Eigen { 
+namespace Eigen {
 
 namespace internal {
-    
+
 /** \internal
-  * Compute a quick-sort split of a vector 
+  * Compute a quick-sort split of a vector
   * On output, the vector row is permuted such that its elements satisfy
   * abs(row(i)) >= abs(row(ncut)) if i<ncut
-  * abs(row(i)) <= abs(row(ncut)) if i>ncut 
+  * abs(row(i)) <= abs(row(ncut)) if i>ncut
   * \param row The vector of values
   * \param ind The array of index for the elements in @p row
   * \param ncut  The number of largest elements to keep
-  **/ 
+  **/
 template <typename VectorV, typename VectorI>
 Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
 {
@@ -34,15 +34,15 @@ Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
   Index mid;
   Index n = row.size(); /* length of the vector */
   Index first, last ;
-  
+
   ncut--; /* to fit the zero-based indices */
-  first = 0; 
-  last = n-1; 
+  first = 0;
+  last = n-1;
   if (ncut < first || ncut > last ) return 0;
-  
+
   do {
-    mid = first; 
-    RealScalar abskey = abs(row(mid)); 
+    mid = first;
+    RealScalar abskey = abs(row(mid));
     for (Index j = first + 1; j <= last; j++) {
       if ( abs(row(j)) > abskey) {
         ++mid;
@@ -53,12 +53,12 @@ Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
     /* Interchange for the pivot element */
     swap(row(mid), row(first));
     swap(ind(mid), ind(first));
-    
+
     if (mid > ncut) last = mid - 1;
-    else if (mid < ncut ) first = mid + 1; 
+    else if (mid < ncut ) first = mid + 1;
   } while (mid != ncut );
-  
-  return 0; /* mid is equal to ncut */ 
+
+  return 0; /* mid is equal to ncut */
 }
 
 }// end namespace internal
@@ -71,23 +71,23 @@ Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
   *
   * During the numerical factorization, two dropping rules are used :
   *  1) any element whose magnitude is less than some tolerance is dropped.
-  *    This tolerance is obtained by multiplying the input tolerance @p droptol 
+  *    This tolerance is obtained by multiplying the input tolerance @p droptol
   *    by the average magnitude of all the original elements in the current row.
-  *  2) After the elimination of the row, only the @p fill largest elements in 
-  *    the L part and the @p fill largest elements in the U part are kept 
-  *    (in addition to the diagonal element ). Note that @p fill is computed from 
-  *    the input parameter @p fillfactor which is used the ratio to control the fill_in 
+  *  2) After the elimination of the row, only the @p fill largest elements in
+  *    the L part and the @p fill largest elements in the U part are kept
+  *    (in addition to the diagonal element ). Note that @p fill is computed from
+  *    the input parameter @p fillfactor which is used the ratio to control the fill_in
   *    relatively to the initial number of nonzero elements.
-  * 
+  *
   * The two extreme cases are when @p droptol=0 (to keep all the @p fill*2 largest elements)
-  * and when @p fill=n/2 with @p droptol being different to zero. 
-  * 
-  * References : Yousef Saad, ILUT: A dual threshold incomplete LU factorization, 
+  * and when @p fill=n/2 with @p droptol being different to zero.
+  *
+  * References : Yousef Saad, ILUT: A dual threshold incomplete LU factorization,
   *              Numerical Linear Algebra with Applications, 1(4), pp 387-402, 1994.
-  * 
+  *
   * NOTE : The following implementation is derived from the ILUT implementation
-  * in the SPARSKIT package, Copyright (C) 2005, the Regents of the University of Minnesota 
-  *  released under the terms of the GNU LGPL: 
+  * in the SPARSKIT package, Copyright (C) 2005, the Regents of the University of Minnesota
+  *  released under the terms of the GNU LGPL:
   *    http://www-users.cs.umn.edu/~saad/software/SPARSKIT/README
   * However, Yousef Saad gave us permission to relicense his ILUT code to MPL2.
   * See the Eigen mailing list archive, thread: ILUT, date: July 8, 2012:
@@ -115,24 +115,24 @@ class IncompleteLUT : public SparseSolverBase<IncompleteLUT<_Scalar, _StorageInd
     };
 
   public:
-    
+
     IncompleteLUT()
       : m_droptol(NumTraits<Scalar>::dummy_precision()), m_fillfactor(10),
         m_analysisIsOk(false), m_factorizationIsOk(false)
     {}
-    
+
     template<typename MatrixType>
     explicit IncompleteLUT(const MatrixType& mat, const RealScalar& droptol=NumTraits<Scalar>::dummy_precision(), int fillfactor = 10)
       : m_droptol(droptol),m_fillfactor(fillfactor),
         m_analysisIsOk(false),m_factorizationIsOk(false)
     {
       eigen_assert(fillfactor != 0);
-      compute(mat); 
+      compute(mat);
     }
-    
-    Index rows() const { return m_lu.rows(); }
-    
-    Index cols() const { return m_lu.cols(); }
+
+    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_lu.rows(); }
+
+    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_lu.cols(); }
 
     /** \brief Reports whether previous computation was successful.
       *
@@ -144,36 +144,36 @@ class IncompleteLUT : public SparseSolverBase<IncompleteLUT<_Scalar, _StorageInd
       eigen_assert(m_isInitialized && "IncompleteLUT is not initialized.");
       return m_info;
     }
-    
+
     template<typename MatrixType>
     void analyzePattern(const MatrixType& amat);
-    
+
     template<typename MatrixType>
     void factorize(const MatrixType& amat);
-    
+
     /**
       * Compute an incomplete LU factorization with dual threshold on the matrix mat
       * No pivoting is done in this version
-      * 
+      *
       **/
     template<typename MatrixType>
     IncompleteLUT& compute(const MatrixType& amat)
     {
-      analyzePattern(amat); 
+      analyzePattern(amat);
       factorize(amat);
       return *this;
     }
 
-    void setDroptol(const RealScalar& droptol); 
-    void setFillfactor(int fillfactor); 
-    
+    void setDroptol(const RealScalar& droptol);
+    void setFillfactor(int fillfactor);
+
     template<typename Rhs, typename Dest>
     void _solve_impl(const Rhs& b, Dest& x) const
     {
       x = m_Pinv * b;
       x = m_lu.template triangularView<UnitLower>().solve(x);
       x = m_lu.template triangularView<Upper>().solve(x);
-      x = m_P * x; 
+      x = m_P * x;
     }
 
 protected:
@@ -200,22 +200,22 @@ protected:
 
 /**
  * Set control parameter droptol
- *  \param droptol   Drop any element whose magnitude is less than this tolerance 
- **/ 
+ *  \param droptol   Drop any element whose magnitude is less than this tolerance
+ **/
 template<typename Scalar, typename StorageIndex>
 void IncompleteLUT<Scalar,StorageIndex>::setDroptol(const RealScalar& droptol)
 {
-  this->m_droptol = droptol;   
+  this->m_droptol = droptol;
 }
 
 /**
  * Set control parameter fillfactor
- * \param fillfactor  This is used to compute the  number @p fill_in of largest elements to keep on each row. 
- **/ 
+ * \param fillfactor  This is used to compute the  number @p fill_in of largest elements to keep on each row.
+ **/
 template<typename Scalar, typename StorageIndex>
 void IncompleteLUT<Scalar,StorageIndex>::setFillfactor(int fillfactor)
 {
-  this->m_fillfactor = fillfactor;   
+  this->m_fillfactor = fillfactor;
 }
 
 template <typename Scalar, typename StorageIndex>
diff --git a/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h b/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
index 13ba9a55b..28a0c5109 100644
--- a/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
+++ b/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
@@ -10,7 +10,7 @@
 #ifndef EIGEN_ITERATIVE_SOLVER_BASE_H
 #define EIGEN_ITERATIVE_SOLVER_BASE_H
 
-namespace Eigen { 
+namespace Eigen {
 
 namespace internal {
 
@@ -145,7 +145,7 @@ class IterativeSolverBase : public SparseSolverBase<Derived>
 protected:
   typedef SparseSolverBase<Derived> Base;
   using Base::m_isInitialized;
-  
+
 public:
   typedef typename internal::traits<Derived>::MatrixType MatrixType;
   typedef typename internal::traits<Derived>::Preconditioner Preconditioner;
@@ -169,10 +169,10 @@ public:
   }
 
   /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    * 
+    *
     * This constructor is a shortcut for the default constructor followed
     * by a call to compute().
-    * 
+    *
     * \warning this class stores a reference to the matrix A as well as some
     * precomputed values that depend on it. Therefore, if \a A is changed
     * this class becomes invalid. Call compute() to update it with the new
@@ -187,7 +187,7 @@ public:
   }
 
   ~IterativeSolverBase() {}
-  
+
   /** Initializes the iterative solver for the sparsity pattern of the matrix \a A for further solving \c Ax=b problems.
     *
     * Currently, this function mostly calls analyzePattern on the preconditioner. In the future
@@ -203,7 +203,7 @@ public:
     m_info = m_preconditioner.info();
     return derived();
   }
-  
+
   /** Initializes the iterative solver with the numerical values of the matrix \a A for further solving \c Ax=b problems.
     *
     * Currently, this function mostly calls factorize on the preconditioner.
@@ -216,7 +216,7 @@ public:
   template<typename MatrixDerived>
   Derived& factorize(const EigenBase<MatrixDerived>& A)
   {
-    eigen_assert(m_analysisIsOk && "You must first call analyzePattern()"); 
+    eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
     grab(A.derived());
     m_preconditioner.factorize(matrix());
     m_factorizationIsOk = true;
@@ -247,16 +247,16 @@ public:
   }
 
   /** \internal */
-  Index rows() const { return matrix().rows(); }
+  EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return matrix().rows(); }
 
   /** \internal */
-  Index cols() const { return matrix().cols(); }
+  EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return matrix().cols(); }
 
   /** \returns the tolerance threshold used by the stopping criteria.
     * \sa setTolerance()
     */
   RealScalar tolerance() const { return m_tolerance; }
-  
+
   /** Sets the tolerance threshold used by the stopping criteria.
     *
     * This value is used as an upper bound to the relative residual error: |Ax-b|/|b|.
@@ -270,7 +270,7 @@ public:
 
   /** \returns a read-write reference to the preconditioner for custom configuration. */
   Preconditioner& preconditioner() { return m_preconditioner; }
-  
+
   /** \returns a read-only reference to the preconditioner. */
   const Preconditioner& preconditioner() const { return m_preconditioner; }
 
@@ -282,7 +282,7 @@ public:
   {
     return (m_maxIterations<0) ? 2*matrix().cols() : m_maxIterations;
   }
-  
+
   /** Sets the max number of iterations.
     * Default is twice the number of columns of the matrix.
     */
@@ -328,13 +328,13 @@ public:
     eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
     return m_info;
   }
-  
+
   /** \internal */
   template<typename Rhs, typename DestDerived>
   void _solve_with_guess_impl(const Rhs& b, SparseMatrixBase<DestDerived> &aDest) const
   {
     eigen_assert(rows()==b.rows());
-    
+
     Index rhsCols = b.cols();
     Index size = b.rows();
     DestDerived& dest(aDest.derived());
@@ -368,7 +368,7 @@ public:
   _solve_with_guess_impl(const Rhs& b, MatrixBase<DestDerived> &aDest) const
   {
     eigen_assert(rows()==b.rows());
-    
+
     Index rhsCols = b.cols();
     DestDerived& dest(aDest.derived());
     ComputationInfo global_info = Success;
@@ -420,19 +420,19 @@ protected:
   {
     return m_matrixWrapper.matrix();
   }
-  
+
   template<typename InputType>
   void grab(const InputType &A)
   {
     m_matrixWrapper.grab(A);
   }
-  
+
   MatrixWrapper m_matrixWrapper;
   Preconditioner m_preconditioner;
 
   Index m_maxIterations;
   RealScalar m_tolerance;
-  
+
   mutable RealScalar m_error;
   mutable Index m_iterations;
   mutable ComputationInfo m_info;
diff --git a/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h b/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
index 79e1e4819..7b8965754 100644
--- a/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
+++ b/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
@@ -13,7 +13,7 @@
 namespace Eigen {
 
 template<typename Decomposition, typename RhsType, typename GuessType> class SolveWithGuess;
-  
+
 /** \class SolveWithGuess
   * \ingroup IterativeLinearSolvers_Module
   *
@@ -45,13 +45,15 @@ public:
   typedef typename internal::traits<SolveWithGuess>::PlainObject PlainObject;
   typedef typename internal::generic_xpr_base<SolveWithGuess<Decomposition,RhsType,GuessType>, MatrixXpr, typename internal::traits<RhsType>::StorageKind>::type Base;
   typedef typename internal::ref_selector<SolveWithGuess>::type Nested;
-  
+
   SolveWithGuess(const Decomposition &dec, const RhsType &rhs, const GuessType &guess)
     : m_dec(dec), m_rhs(rhs), m_guess(guess)
   {}
-  
-  EIGEN_DEVICE_FUNC Index rows() const { return m_dec.cols(); }
-  EIGEN_DEVICE_FUNC Index cols() const { return m_rhs.cols(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  Index rows() const EIGEN_NOEXCEPT { return m_dec.cols(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
 
   EIGEN_DEVICE_FUNC const Decomposition& dec()   const { return m_dec; }
   EIGEN_DEVICE_FUNC const RhsType&       rhs()   const { return m_rhs; }
@@ -61,7 +63,7 @@ protected:
   const Decomposition &m_dec;
   const RhsType       &m_rhs;
   const GuessType     &m_guess;
-  
+
 private:
   Scalar coeff(Index row, Index col) const;
   Scalar coeff(Index i) const;
@@ -85,8 +87,8 @@ struct evaluator<SolveWithGuess<Decomposition,RhsType, GuessType> >
     m_result = solve.guess();
     solve.dec()._solve_with_guess_impl(solve.rhs(), m_result);
   }
-  
-protected:  
+
+protected:
   PlainObject m_result;
 };