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diff --git a/eigen/test/sparse_block.cpp b/eigen/test/sparse_block.cpp
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+++ b/eigen/test/sparse_block.cpp
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "sparse.h"
+
+template<typename T>
+typename Eigen::internal::enable_if<(T::Flags&RowMajorBit)==RowMajorBit, typename T::RowXpr>::type
+innervec(T& A, Index i)
+{
+  return A.row(i);
+}
+
+template<typename T>
+typename Eigen::internal::enable_if<(T::Flags&RowMajorBit)==0, typename T::ColXpr>::type
+innervec(T& A, Index i)
+{
+  return A.col(i);
+}
+
+template<typename SparseMatrixType> void sparse_block(const SparseMatrixType& ref)
+{
+  const Index rows = ref.rows();
+  const Index cols = ref.cols();
+  const Index inner = ref.innerSize();
+  const Index outer = ref.outerSize();
+
+  typedef typename SparseMatrixType::Scalar Scalar;
+  typedef typename SparseMatrixType::StorageIndex StorageIndex;
+
+  double density = (std::max)(8./(rows*cols), 0.01);
+  typedef Matrix<Scalar,Dynamic,Dynamic,SparseMatrixType::IsRowMajor?RowMajor:ColMajor> DenseMatrix;
+  typedef Matrix<Scalar,Dynamic,1> DenseVector;
+  typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
+  typedef SparseVector<Scalar> SparseVectorType;
+
+  Scalar s1 = internal::random<Scalar>();
+  {
+    SparseMatrixType m(rows, cols);
+    DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
+    initSparse<Scalar>(density, refMat, m);
+
+    VERIFY_IS_APPROX(m, refMat);
+
+    // test InnerIterators and Block expressions
+    for (int t=0; t<10; ++t)
+    {
+      Index j = internal::random<Index>(0,cols-2);
+      Index i = internal::random<Index>(0,rows-2);
+      Index w = internal::random<Index>(1,cols-j);
+      Index h = internal::random<Index>(1,rows-i);
+
+      VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
+      for(Index c=0; c<w; c++)
+      {
+        VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
+        for(Index r=0; r<h; r++)
+        {
+          VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
+          VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
+        }
+      }
+      for(Index r=0; r<h; r++)
+      {
+        VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
+        for(Index c=0; c<w; c++)
+        {
+          VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
+          VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
+        }
+      }
+      
+      VERIFY_IS_APPROX(m.middleCols(j,w), refMat.middleCols(j,w));
+      VERIFY_IS_APPROX(m.middleRows(i,h), refMat.middleRows(i,h));
+      for(Index r=0; r<h; r++)
+      {
+        VERIFY_IS_APPROX(m.middleCols(j,w).row(r), refMat.middleCols(j,w).row(r));
+        VERIFY_IS_APPROX(m.middleRows(i,h).row(r), refMat.middleRows(i,h).row(r));
+        for(Index c=0; c<w; c++)
+        {
+          VERIFY_IS_APPROX(m.col(c).coeff(r), refMat.col(c).coeff(r));
+          VERIFY_IS_APPROX(m.row(r).coeff(c), refMat.row(r).coeff(c));
+          
+          VERIFY_IS_APPROX(m.middleCols(j,w).coeff(r,c), refMat.middleCols(j,w).coeff(r,c));
+          VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c));
+          if(m.middleCols(j,w).coeff(r,c) != Scalar(0))
+          {
+            VERIFY_IS_APPROX(m.middleCols(j,w).coeffRef(r,c), refMat.middleCols(j,w).coeff(r,c));
+          }
+          if(m.middleRows(i,h).coeff(r,c) != Scalar(0))
+          {
+            VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c));
+          }
+        }
+      }
+      for(Index c=0; c<w; c++)
+      {
+        VERIFY_IS_APPROX(m.middleCols(j,w).col(c), refMat.middleCols(j,w).col(c));
+        VERIFY_IS_APPROX(m.middleRows(i,h).col(c), refMat.middleRows(i,h).col(c));
+      }
+    }
+
+    for(Index c=0; c<cols; c++)
+    {
+      VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
+      VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
+    }
+
+    for(Index r=0; r<rows; r++)
+    {
+      VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
+      VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
+    }
+  }
+
+  // test innerVector()
+  {
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+    SparseMatrixType m2(rows, cols);
+    initSparse<Scalar>(density, refMat2, m2);
+    Index j0 = internal::random<Index>(0,outer-1);
+    Index j1 = internal::random<Index>(0,outer-1);
+    Index r0 = internal::random<Index>(0,rows-1);
+    Index c0 = internal::random<Index>(0,cols-1);
+
+    VERIFY_IS_APPROX(m2.innerVector(j0), innervec(refMat2,j0));
+    VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), innervec(refMat2,j0)+innervec(refMat2,j1));
+
+    m2.innerVector(j0) *= Scalar(2);
+    innervec(refMat2,j0) *= Scalar(2);
+    VERIFY_IS_APPROX(m2, refMat2);
+
+    m2.row(r0) *= Scalar(3);
+    refMat2.row(r0) *= Scalar(3);
+    VERIFY_IS_APPROX(m2, refMat2);
+
+    m2.col(c0) *= Scalar(4);
+    refMat2.col(c0) *= Scalar(4);
+    VERIFY_IS_APPROX(m2, refMat2);
+
+    m2.row(r0) /= Scalar(3);
+    refMat2.row(r0) /= Scalar(3);
+    VERIFY_IS_APPROX(m2, refMat2);
+
+    m2.col(c0) /= Scalar(4);
+    refMat2.col(c0) /= Scalar(4);
+    VERIFY_IS_APPROX(m2, refMat2);
+
+    SparseVectorType v1;
+    VERIFY_IS_APPROX(v1 = m2.col(c0) * 4, refMat2.col(c0)*4);
+    VERIFY_IS_APPROX(v1 = m2.row(r0) * 4, refMat2.row(r0).transpose()*4);
+
+    SparseMatrixType m3(rows,cols);
+    m3.reserve(VectorXi::Constant(outer,int(inner/2)));
+    for(Index j=0; j<outer; ++j)
+      for(Index k=0; k<(std::min)(j,inner); ++k)
+        m3.insertByOuterInner(j,k) = internal::convert_index<StorageIndex>(k+1);
+    for(Index j=0; j<(std::min)(outer, inner); ++j)
+    {
+      VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
+      if(j>0)
+        VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
+    }
+    m3.makeCompressed();
+    for(Index j=0; j<(std::min)(outer, inner); ++j)
+    {
+      VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
+      if(j>0)
+        VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
+    }
+
+    VERIFY(m3.innerVector(j0).nonZeros() == m3.transpose().innerVector(j0).nonZeros());
+
+//     m2.innerVector(j0) = 2*m2.innerVector(j1);
+//     refMat2.col(j0) = 2*refMat2.col(j1);
+//     VERIFY_IS_APPROX(m2, refMat2);
+  }
+
+  // test innerVectors()
+  {
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+    SparseMatrixType m2(rows, cols);
+    initSparse<Scalar>(density, refMat2, m2);
+    if(internal::random<float>(0,1)>0.5f) m2.makeCompressed();
+    Index j0 = internal::random<Index>(0,outer-2);
+    Index j1 = internal::random<Index>(0,outer-2);
+    Index n0 = internal::random<Index>(1,outer-(std::max)(j0,j1));
+    if(SparseMatrixType::IsRowMajor)
+      VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(j0,0,n0,cols));
+    else
+      VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0));
+    if(SparseMatrixType::IsRowMajor)
+      VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
+                       refMat2.middleRows(j0,n0)+refMat2.middleRows(j1,n0));
+    else
+      VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
+                      refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
+    
+    VERIFY_IS_APPROX(m2, refMat2);
+    
+    VERIFY(m2.innerVectors(j0,n0).nonZeros() == m2.transpose().innerVectors(j0,n0).nonZeros());
+    
+    m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0);
+    if(SparseMatrixType::IsRowMajor)
+      refMat2.middleRows(j0,n0) = (refMat2.middleRows(j0,n0) + refMat2.middleRows(j1,n0)).eval();
+    else
+      refMat2.middleCols(j0,n0) = (refMat2.middleCols(j0,n0) + refMat2.middleCols(j1,n0)).eval();
+    
+    VERIFY_IS_APPROX(m2, refMat2);
+  }
+
+  // test generic blocks
+  {
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+    SparseMatrixType m2(rows, cols);
+    initSparse<Scalar>(density, refMat2, m2);
+    Index j0 = internal::random<Index>(0,outer-2);
+    Index j1 = internal::random<Index>(0,outer-2);
+    Index n0 = internal::random<Index>(1,outer-(std::max)(j0,j1));
+    if(SparseMatrixType::IsRowMajor)
+      VERIFY_IS_APPROX(m2.block(j0,0,n0,cols), refMat2.block(j0,0,n0,cols));
+    else
+      VERIFY_IS_APPROX(m2.block(0,j0,rows,n0), refMat2.block(0,j0,rows,n0));
+    
+    if(SparseMatrixType::IsRowMajor)
+      VERIFY_IS_APPROX(m2.block(j0,0,n0,cols)+m2.block(j1,0,n0,cols),
+                      refMat2.block(j0,0,n0,cols)+refMat2.block(j1,0,n0,cols));
+    else
+      VERIFY_IS_APPROX(m2.block(0,j0,rows,n0)+m2.block(0,j1,rows,n0),
+                      refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
+      
+    Index i = internal::random<Index>(0,m2.outerSize()-1);
+    if(SparseMatrixType::IsRowMajor) {
+      m2.innerVector(i) = m2.innerVector(i) * s1;
+      refMat2.row(i) = refMat2.row(i) * s1;
+      VERIFY_IS_APPROX(m2,refMat2);
+    } else {
+      m2.innerVector(i) = m2.innerVector(i) * s1;
+      refMat2.col(i) = refMat2.col(i) * s1;
+      VERIFY_IS_APPROX(m2,refMat2);
+    }
+    
+    Index r0 = internal::random<Index>(0,rows-2);
+    Index c0 = internal::random<Index>(0,cols-2);
+    Index r1 = internal::random<Index>(1,rows-r0);
+    Index c1 = internal::random<Index>(1,cols-c0);
+    
+    VERIFY_IS_APPROX(DenseVector(m2.col(c0)), refMat2.col(c0));
+    VERIFY_IS_APPROX(m2.col(c0), refMat2.col(c0));
+    
+    VERIFY_IS_APPROX(RowDenseVector(m2.row(r0)), refMat2.row(r0));
+    VERIFY_IS_APPROX(m2.row(r0), refMat2.row(r0));
+
+    VERIFY_IS_APPROX(m2.block(r0,c0,r1,c1), refMat2.block(r0,c0,r1,c1));
+    VERIFY_IS_APPROX((2*m2).block(r0,c0,r1,c1), (2*refMat2).block(r0,c0,r1,c1));
+
+    if(m2.nonZeros()>0)
+    {
+      VERIFY_IS_APPROX(m2, refMat2);
+      SparseMatrixType m3(rows, cols);
+      DenseMatrix refMat3(rows, cols); refMat3.setZero();
+      Index n = internal::random<Index>(1,10);
+      for(Index k=0; k<n; ++k)
+      {
+        Index o1 = internal::random<Index>(0,outer-1);
+        Index o2 = internal::random<Index>(0,outer-1);
+        if(SparseMatrixType::IsRowMajor)
+        {
+          m3.innerVector(o1) = m2.row(o2);
+          refMat3.row(o1) = refMat2.row(o2);
+        }
+        else
+        {
+          m3.innerVector(o1) = m2.col(o2);
+          refMat3.col(o1) = refMat2.col(o2);
+        }
+        if(internal::random<bool>())
+          m3.makeCompressed();
+      }
+      if(m3.nonZeros()>0)
+      VERIFY_IS_APPROX(m3, refMat3);
+    }
+  }
+}
+
+void test_sparse_block()
+{
+  for(int i = 0; i < g_repeat; i++) {
+    int r = Eigen::internal::random<int>(1,200), c = Eigen::internal::random<int>(1,200);
+    if(Eigen::internal::random<int>(0,4) == 0) {
+      r = c; // check square matrices in 25% of tries
+    }
+    EIGEN_UNUSED_VARIABLE(r+c);
+    CALL_SUBTEST_1(( sparse_block(SparseMatrix<double>(1, 1)) ));
+    CALL_SUBTEST_1(( sparse_block(SparseMatrix<double>(8, 8)) ));
+    CALL_SUBTEST_1(( sparse_block(SparseMatrix<double>(r, c)) ));
+    CALL_SUBTEST_2(( sparse_block(SparseMatrix<std::complex<double>, ColMajor>(r, c)) ));
+    CALL_SUBTEST_2(( sparse_block(SparseMatrix<std::complex<double>, RowMajor>(r, c)) ));
+    
+    CALL_SUBTEST_3(( sparse_block(SparseMatrix<double,ColMajor,long int>(r, c)) ));
+    CALL_SUBTEST_3(( sparse_block(SparseMatrix<double,RowMajor,long int>(r, c)) ));
+    
+    r = Eigen::internal::random<int>(1,100);
+    c = Eigen::internal::random<int>(1,100);
+    if(Eigen::internal::random<int>(0,4) == 0) {
+      r = c; // check square matrices in 25% of tries
+    }
+    
+    CALL_SUBTEST_4(( sparse_block(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) ));
+    CALL_SUBTEST_4(( sparse_block(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) ));
+  }
+}