fix sparse vector assignment from a sparse matrix

3 files changed, 50 insertions, 1 deletions

diff --git a/test/sparse.h b/test/sparse.h
index 4db0004aa..b6f6e6fce 100644
--- a/test/sparse.h
+++ b/test/sparse.h
@@ -178,5 +178,30 @@ initSparse(double density,
   }
 }
 
+template<typename Scalar> void
+initSparse(double density,
+           Matrix<Scalar,1,Dynamic>& refVec,
+           SparseVector<Scalar,RowMajor>& sparseVec,
+           std::vector<int>* zeroCoords = 0,
+           std::vector<int>* nonzeroCoords = 0)
+{
+  sparseVec.reserve(int(refVec.size()*density));
+  sparseVec.setZero();
+  for(int i=0; i<refVec.size(); i++)
+  {
+    Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
+    if (v!=Scalar(0))
+    {
+      sparseVec.insertBack(i) = v;
+      if (nonzeroCoords)
+        nonzeroCoords->push_back(i);
+    }
+    else if (zeroCoords)
+        zeroCoords->push_back(i);
+    refVec[i] = v;
+  }
+}
+
+
 #include <unsupported/Eigen/SparseExtra>
 #endif // EIGEN_TESTSPARSE_H
diff --git a/test/sparse_product.cpp b/test/sparse_product.cpp
index 4eae263fa..67a59ecd8 100644
--- a/test/sparse_product.cpp
+++ b/test/sparse_product.cpp
@@ -46,6 +46,9 @@ template<typename SparseMatrixType> void sparse_product()
   double density = (std::max)(8./(rows*cols), 0.1);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
+  typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
+  typedef SparseVector<Scalar,0,Index> ColSpVector;
+  typedef SparseVector<Scalar,RowMajor,Index> RowSpVector;
 
   Scalar s1 = internal::random<Scalar>();
   Scalar s2 = internal::random<Scalar>();
@@ -117,8 +120,23 @@ template<typename SparseMatrixType> void sparse_product()
     test_outer<SparseMatrixType,DenseMatrix>::run(m2,m4,refMat2,refMat4);
 
     VERIFY_IS_APPROX(m6=m6*m6, refMat6=refMat6*refMat6);
+    
+    // sparse matrix * sparse vector
+    ColSpVector cv0(cols), cv1;
+    DenseVector dcv0(cols), dcv1;
+    initSparse(2*density,dcv0, cv0);
+    
+    RowSpVector rv0(depth), rv1;
+    RowDenseVector drv0(depth), drv1(rv1);
+    initSparse(2*density,drv0, rv0);
+    
+    VERIFY_IS_APPROX(cv1=rv0*m3, dcv1=drv0*refMat3);
+    VERIFY_IS_APPROX(rv1=rv0*m3, drv1=drv0*refMat3);
+    VERIFY_IS_APPROX(cv1=m3*cv0, dcv1=refMat3*dcv0);
+    VERIFY_IS_APPROX(cv1=m3t.adjoint()*cv0, dcv1=refMat3t.adjoint()*dcv0);
+    VERIFY_IS_APPROX(rv1=m3*cv0, drv1=refMat3*dcv0);
   }
-
+  
   // test matrix - diagonal product
   {
     DenseMatrix refM2 = DenseMatrix::Zero(rows, cols);
diff --git a/test/sparse_vector.cpp b/test/sparse_vector.cpp
index 7973a47dc..d16d42f59 100644
--- a/test/sparse_vector.cpp
+++ b/test/sparse_vector.cpp
@@ -84,6 +84,12 @@ template<typename Scalar> void sparse_vector(int rows, int cols)
   VERIFY_IS_APPROX((v1 = -v1), (refV1 = -refV1));
   VERIFY_IS_APPROX((v1 = v1.transpose()), (refV1 = refV1.transpose().eval()));
   VERIFY_IS_APPROX((v1 += -v1), (refV1 += -refV1));
+  
+  // sparse matrix to sparse vector
+  SparseMatrixType mv1;
+  VERIFY_IS_APPROX((mv1=v1),v1);
+  VERIFY_IS_APPROX(mv1,(v1=mv1));
+  VERIFY_IS_APPROX(mv1,(v1=mv1.transpose()));
 
 }