Fix many long to int conversion warnings:

 - fix usage of Index (API) versus StorageIndex (when multiple indexes are stored)
 - use StorageIndex(val) when the input has already been check
 - use internal::convert_index<StorageIndex>(val) when val is potentially unsafe (directly comes from user input)

3 files changed, 19 insertions, 19 deletions

diff --git a/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
index a715c7285..e67f09184 100644
--- a/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
+++ b/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
@@ -27,7 +27,7 @@ namespace internal {
   */
 template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
 bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
-              const Preconditioner& precond, int& iters,
+              const Preconditioner& precond, Index& iters,
               typename Dest::RealScalar& tol_error)
 {
   using std::sqrt;
@@ -36,9 +36,9 @@ bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
   typedef typename Dest::Scalar Scalar;
   typedef Matrix<Scalar,Dynamic,1> VectorType;
   RealScalar tol = tol_error;
-  int maxIters = iters;
+  Index maxIters = iters;
 
-  int n = mat.cols();
+  Index n = mat.cols();
   VectorType r  = rhs - mat * x;
   VectorType r0 = r;
   
@@ -61,8 +61,8 @@ bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
 
   RealScalar tol2 = tol*tol;
   RealScalar eps2 = NumTraits<Scalar>::epsilon()*NumTraits<Scalar>::epsilon();
-  int i = 0;
-  int restarts = 0;
+  Index i = 0;
+  Index restarts = 0;
 
   while ( r.squaredNorm()/rhs_sqnorm > tol2 && i<maxIters )
   {
@@ -182,7 +182,7 @@ public:
   void _solve_with_guess_impl(const Rhs& b, Dest& x) const
   {    
     bool failed = false;
-    for(int j=0; j<b.cols(); ++j)
+    for(Index j=0; j<b.cols(); ++j)
     {
       m_iterations = Base::maxIterations();
       m_error = Base::m_tolerance;
diff --git a/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
index d8bc13f5f..a799c3ef5 100644
--- a/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
+++ b/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
@@ -26,7 +26,7 @@ namespace internal {
 template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
 EIGEN_DONT_INLINE
 void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
-                        const Preconditioner& precond, int& iters,
+                        const Preconditioner& precond, Index& iters,
                         typename Dest::RealScalar& tol_error)
 {
   using std::sqrt;
@@ -36,9 +36,9 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
   typedef Matrix<Scalar,Dynamic,1> VectorType;
   
   RealScalar tol = tol_error;
-  int maxIters = iters;
+  Index maxIters = iters;
   
-  int n = mat.cols();
+  Index n = mat.cols();
 
   VectorType residual = rhs - mat * x; //initial residual
 
@@ -64,7 +64,7 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
 
   VectorType z(n), tmp(n);
   RealScalar absNew = numext::real(residual.dot(p));  // the square of the absolute value of r scaled by invM
-  int i = 0;
+  Index i = 0;
   while(i < maxIters)
   {
     tmp.noalias() = mat * p;              // the bottleneck of the algorithm
@@ -190,7 +190,7 @@ public:
     m_iterations = Base::maxIterations();
     m_error = Base::m_tolerance;
 
-    for(int j=0; j<b.cols(); ++j)
+    for(Index j=0; j<b.cols(); ++j)
     {
       m_iterations = Base::maxIterations();
       m_error = Base::m_tolerance;
diff --git a/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h b/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
index 2afd80f4d..38334028a 100644
--- a/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
+++ b/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
@@ -145,7 +145,7 @@ public:
     * It is either the value setted by setMaxIterations or, by default,
     * twice the number of columns of the matrix.
     */
-  int maxIterations() const
+  Index maxIterations() const
   {
     return (m_maxIterations<0) ? 2*mp_matrix.cols() : m_maxIterations;
   }
@@ -153,14 +153,14 @@ public:
   /** Sets the max number of iterations.
     * Default is twice the number of columns of the matrix.
     */
-  Derived& setMaxIterations(int maxIters)
+  Derived& setMaxIterations(Index maxIters)
   {
     m_maxIterations = maxIters;
     return derived();
   }
 
   /** \returns the number of iterations performed during the last solve */
-  int iterations() const
+  Index iterations() const
   {
     eigen_assert(m_isInitialized && "ConjugateGradient is not initialized.");
     return m_iterations;
@@ -200,11 +200,11 @@ public:
   {
     eigen_assert(rows()==b.rows());
     
-    int rhsCols = b.cols();
-    int size = b.rows();
+    Index rhsCols = b.cols();
+    Index size = b.rows();
     Eigen::Matrix<DestScalar,Dynamic,1> tb(size);
     Eigen::Matrix<DestScalar,Dynamic,1> tx(size);
-    for(int k=0; k<rhsCols; ++k)
+    for(Index k=0; k<rhsCols; ++k)
     {
       tb = b.col(k);
       tx = derived().solve(tb);
@@ -233,11 +233,11 @@ protected:
   Ref<const MatrixType> mp_matrix;
   Preconditioner m_preconditioner;
 
-  int m_maxIterations;
+  Index m_maxIterations;
   RealScalar m_tolerance;
   
   mutable RealScalar m_error;
-  mutable int m_iterations;
+  mutable Index m_iterations;
   mutable ComputationInfo m_info;
   mutable bool m_analysisIsOk, m_factorizationIsOk;
 };