Pulled the latest changes from the trunk

38 files changed, 1434 insertions, 602 deletions

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 47aefddb8..f57d8ce36 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -139,17 +139,12 @@ endif(TEST_LIB)
 set_property(GLOBAL PROPERTY EIGEN_CURRENT_SUBPROJECT "Official")
 add_custom_target(BuildOfficial)
 
-option(EIGEN_TEST_EVALUATORS "Enable work in progress evaluators" OFF)
-if(EIGEN_TEST_EVALUATORS)
-  add_definitions("-DEIGEN_TEST_EVALUATORS=1")
-  add_definitions("-DEIGEN_ENABLE_EVALUATORS=1")
-endif(EIGEN_TEST_EVALUATORS)
-
 ei_add_test(meta)
 ei_add_test(sizeof)
 ei_add_test(dynalloc)
 ei_add_test(nomalloc)
 ei_add_test(first_aligned)
+ei_add_test(nullary)
 ei_add_test(mixingtypes)
 ei_add_test(packetmath)
 ei_add_test(unalignedassert)
@@ -165,6 +160,9 @@ ei_add_test(redux)
 ei_add_test(visitor)
 ei_add_test(block)
 ei_add_test(corners)
+ei_add_test(swap)
+ei_add_test(resize)
+ei_add_test(conservative_resize)
 ei_add_test(product_small)
 ei_add_test(product_large)
 ei_add_test(product_extra)
@@ -193,6 +191,7 @@ ei_add_test(product_trsolve)
 ei_add_test(product_mmtr)
 ei_add_test(product_notemporary)
 ei_add_test(stable_norm)
+ei_add_test(permutationmatrices)
 ei_add_test(bandmatrix)
 ei_add_test(cholesky)
 ei_add_test(lu)
@@ -212,30 +211,31 @@ ei_add_test(real_qz)
 ei_add_test(eigensolver_generalized_real)
 ei_add_test(jacobi)
 ei_add_test(jacobisvd)
+ei_add_test(bdcsvd)
+ei_add_test(householder)
 ei_add_test(geo_orthomethods)
-ei_add_test(geo_homogeneous)
 ei_add_test(geo_quaternion)
-ei_add_test(geo_transformations)
 ei_add_test(geo_eulerangles)
-ei_add_test(geo_hyperplane)
 ei_add_test(geo_parametrizedline)
 ei_add_test(geo_alignedbox)
+ei_add_test(geo_hyperplane)
+ei_add_test(geo_transformations)
+ei_add_test(geo_homogeneous)
 ei_add_test(stdvector)
 ei_add_test(stdvector_overload)
 ei_add_test(stdlist)
 ei_add_test(stddeque)
-ei_add_test(resize)
-ei_add_test(sparse_vector)
 ei_add_test(sparse_basic)
+ei_add_test(sparse_vector)
 ei_add_test(sparse_product)
 ei_add_test(sparse_solvers)
-ei_add_test(umeyama)
-ei_add_test(householder)
-ei_add_test(swap)
-ei_add_test(conservative_resize)
-ei_add_test(permutationmatrices)
 ei_add_test(sparse_permutations)
-ei_add_test(nullary)
+ei_add_test(simplicial_cholesky)
+ei_add_test(conjugate_gradient)
+ei_add_test(bicgstab)
+ei_add_test(sparselu)
+ei_add_test(sparseqr)
+ei_add_test(umeyama)
 ei_add_test(nesting_ops "${CMAKE_CXX_FLAGS_DEBUG}")
 ei_add_test(zerosized)
 ei_add_test(dontalign)
@@ -249,13 +249,7 @@ ei_add_test(special_numbers)
 ei_add_test(rvalue_types)
 ei_add_test(dense_storage)
 
-ei_add_test(simplicial_cholesky)
-ei_add_test(conjugate_gradient)
-ei_add_test(bicgstab)
-ei_add_test(sparselu)
-ei_add_test(sparseqr)
-
-# ei_add_test(denseLM)
+# # ei_add_test(denseLM)
 
 if(QT4_FOUND)
   ei_add_test(qtvector "" "${QT_QTCORE_LIBRARY}")
@@ -313,7 +307,7 @@ endif()
 option(EIGEN_TEST_NVCC "Enable NVCC support in unit tests" OFF)
 if(EIGEN_TEST_NVCC)
   
-find_package(CUDA)
+find_package(CUDA 5.0)
 if(CUDA_FOUND)
   
   set(CUDA_PROPAGATE_HOST_FLAGS OFF)
@@ -331,3 +325,6 @@ endif(CUDA_FOUND)
 
 endif(EIGEN_TEST_NVCC)
 
+
+file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/failtests)    
+add_test(NAME failtests WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/failtests COMMAND ${CMAKE_COMMAND} ${Eigen_SOURCE_DIR} -G "${CMAKE_GENERATOR}" -DEIGEN_FAILTEST=ON)
diff --git a/test/adjoint.cpp b/test/adjoint.cpp
index ea36f7841..3b2a53c91 100644
--- a/test/adjoint.cpp
+++ b/test/adjoint.cpp
@@ -64,6 +64,7 @@ template<typename MatrixType> void adjoint(const MatrixType& m)
   typedef typename NumTraits<Scalar>::Real RealScalar;
   typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
   typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
+  const Index PacketSize = internal::packet_traits<Scalar>::size;
   
   Index rows = m.rows();
   Index cols = m.cols();
@@ -108,6 +109,17 @@ template<typename MatrixType> void adjoint(const MatrixType& m)
   VERIFY_IS_APPROX(m3,m1.transpose());
   m3.transposeInPlace();
   VERIFY_IS_APPROX(m3,m1);
+  
+  if(PacketSize<m3.rows() && PacketSize<m3.cols())
+  {
+    m3 = m1;
+    Index i = internal::random<Index>(0,m3.rows()-PacketSize);
+    Index j = internal::random<Index>(0,m3.cols()-PacketSize);
+    m3.template block<PacketSize,PacketSize>(i,j).transposeInPlace();
+    VERIFY_IS_APPROX( (m3.template block<PacketSize,PacketSize>(i,j)), (m1.template block<PacketSize,PacketSize>(i,j).transpose()) );
+    m3.template block<PacketSize,PacketSize>(i,j).transposeInPlace();
+    VERIFY_IS_APPROX(m3,m1);
+  }
 
   // check inplace adjoint
   m3 = m1;
@@ -129,9 +141,19 @@ void test_adjoint()
     CALL_SUBTEST_1( adjoint(Matrix<float, 1, 1>()) );
     CALL_SUBTEST_2( adjoint(Matrix3d()) );
     CALL_SUBTEST_3( adjoint(Matrix4f()) );
+    
     CALL_SUBTEST_4( adjoint(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
     CALL_SUBTEST_5( adjoint(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
     CALL_SUBTEST_6( adjoint(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
+    
+    // Complement for 128 bits vectorization:
+    CALL_SUBTEST_8( adjoint(Matrix2d()) );
+    CALL_SUBTEST_9( adjoint(Matrix<int,4,4>()) );
+    
+    // 256 bits vectorization:
+    CALL_SUBTEST_10( adjoint(Matrix<float,8,8>()) );
+    CALL_SUBTEST_11( adjoint(Matrix<double,4,4>()) );
+    CALL_SUBTEST_12( adjoint(Matrix<int,8,8>()) );
   }
   // test a large static matrix only once
   CALL_SUBTEST_7( adjoint(Matrix<float, 100, 100>()) );
diff --git a/test/array.cpp b/test/array.cpp
index 010fead2d..ac9be097d 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -81,6 +81,31 @@ template<typename ArrayType> void array(const ArrayType& m)
   VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1);
   m3 = m1;
   VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1);
+  
+  // Conversion from scalar
+  VERIFY_IS_APPROX((m3 = s1), ArrayType::Constant(rows,cols,s1));
+  VERIFY_IS_APPROX((m3 = 1),  ArrayType::Constant(rows,cols,1));
+  VERIFY_IS_APPROX((m3.topLeftCorner(rows,cols) = 1),  ArrayType::Constant(rows,cols,1));
+  typedef Array<Scalar,
+                ArrayType::RowsAtCompileTime==Dynamic?2:ArrayType::RowsAtCompileTime,
+                ArrayType::ColsAtCompileTime==Dynamic?2:ArrayType::ColsAtCompileTime,
+                ArrayType::Options> FixedArrayType;
+  FixedArrayType f1(s1);
+  VERIFY_IS_APPROX(f1, FixedArrayType::Constant(s1));
+  FixedArrayType f2(numext::real(s1));
+  VERIFY_IS_APPROX(f2, FixedArrayType::Constant(numext::real(s1)));
+  FixedArrayType f3((int)100*numext::real(s1));
+  VERIFY_IS_APPROX(f3, FixedArrayType::Constant((int)100*numext::real(s1)));
+  f1.setRandom();
+  FixedArrayType f4(f1.data());
+  VERIFY_IS_APPROX(f4, f1);
+  
+  // Check possible conflicts with 1D ctor
+  typedef Array<Scalar, Dynamic, 1> OneDArrayType;
+  OneDArrayType o1(rows);
+  VERIFY(o1.size()==rows);
+  OneDArrayType o4((int)rows);
+  VERIFY(o4.size()==rows);
 }
 
 template<typename ArrayType> void comparisons(const ArrayType& m)
diff --git a/test/bdcsvd.cpp b/test/bdcsvd.cpp
new file mode 100644
index 000000000..52a02b697
--- /dev/null
+++ b/test/bdcsvd.cpp
@@ -0,0 +1,111 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
+// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
+// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
+// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.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/
+
+// discard stack allocation as that too bypasses malloc
+#define EIGEN_STACK_ALLOCATION_LIMIT 0
+#define EIGEN_RUNTIME_NO_MALLOC
+
+#include "main.h"
+#include <Eigen/SVD>
+#include <iostream>
+#include <Eigen/LU>
+
+
+#define SVD_DEFAULT(M) BDCSVD<M>
+#define SVD_FOR_MIN_NORM(M) BDCSVD<M>
+#include "svd_common.h"
+
+// Check all variants of JacobiSVD
+template<typename MatrixType>
+void bdcsvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
+{
+  MatrixType m = a;
+  if(pickrandom)
+    svd_fill_random(m);
+
+  CALL_SUBTEST(( svd_test_all_computation_options<BDCSVD<MatrixType> >(m, false)  ));
+}
+
+template<typename MatrixType>
+void bdcsvd_method()
+{
+  enum { Size = MatrixType::RowsAtCompileTime };
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Matrix<RealScalar, Size, 1> RealVecType;
+  MatrixType m = MatrixType::Identity();
+  VERIFY_IS_APPROX(m.bdcSvd().singularValues(), RealVecType::Ones());
+  VERIFY_RAISES_ASSERT(m.bdcSvd().matrixU());
+  VERIFY_RAISES_ASSERT(m.bdcSvd().matrixV());
+  VERIFY_IS_APPROX(m.bdcSvd(ComputeFullU|ComputeFullV).solve(m), m);
+}
+
+// compare the Singular values returned with Jacobi and Bdc
+template<typename MatrixType> 
+void compare_bdc_jacobi(const MatrixType& a = MatrixType(), unsigned int computationOptions = 0)
+{
+  MatrixType m = MatrixType::Random(a.rows(), a.cols());
+  BDCSVD<MatrixType> bdc_svd(m);
+  JacobiSVD<MatrixType> jacobi_svd(m);
+  VERIFY_IS_APPROX(bdc_svd.singularValues(), jacobi_svd.singularValues());
+  if(computationOptions & ComputeFullU) VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU());
+  if(computationOptions & ComputeThinU) VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU());
+  if(computationOptions & ComputeFullV) VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV());
+  if(computationOptions & ComputeThinV) VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV());
+}
+
+void test_bdcsvd()
+{
+  CALL_SUBTEST_3(( svd_verify_assert<BDCSVD<Matrix3f>  >(Matrix3f()) ));
+  CALL_SUBTEST_4(( svd_verify_assert<BDCSVD<Matrix4d>  >(Matrix4d()) ));
+  CALL_SUBTEST_7(( svd_verify_assert<BDCSVD<MatrixXf>  >(MatrixXf(10,12)) ));
+  CALL_SUBTEST_8(( svd_verify_assert<BDCSVD<MatrixXcd> >(MatrixXcd(7,5)) ));
+  
+  CALL_SUBTEST_1(( svd_all_trivial_2x2(bdcsvd<Matrix2cd>) ));
+  CALL_SUBTEST_1(( svd_all_trivial_2x2(bdcsvd<Matrix2d>) ));
+
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_3(( bdcsvd<Matrix3f>() ));
+    CALL_SUBTEST_4(( bdcsvd<Matrix4d>() ));
+    CALL_SUBTEST_5(( bdcsvd<Matrix<float,3,5> >() ));
+
+    int r = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2),
+        c = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2);
+    
+    TEST_SET_BUT_UNUSED_VARIABLE(r)
+    TEST_SET_BUT_UNUSED_VARIABLE(c)
+    
+    CALL_SUBTEST_6((  bdcsvd(Matrix<double,Dynamic,2>(r,2)) ));
+    CALL_SUBTEST_7((  bdcsvd(MatrixXf(r,c)) ));
+    CALL_SUBTEST_7((  compare_bdc_jacobi(MatrixXf(r,c)) ));
+    CALL_SUBTEST_10(( bdcsvd(MatrixXd(r,c)) ));
+    CALL_SUBTEST_10(( compare_bdc_jacobi(MatrixXd(r,c)) ));
+    CALL_SUBTEST_8((  bdcsvd(MatrixXcd(r,c)) ));
+    CALL_SUBTEST_8((  compare_bdc_jacobi(MatrixXcd(r,c)) ));
+
+    // Test on inf/nan matrix
+    CALL_SUBTEST_7(  (svd_inf_nan<BDCSVD<MatrixXf>, MatrixXf>()) );
+    CALL_SUBTEST_10( (svd_inf_nan<BDCSVD<MatrixXd>, MatrixXd>()) );
+  }
+
+  // test matrixbase method
+  CALL_SUBTEST_1(( bdcsvd_method<Matrix2cd>() ));
+  CALL_SUBTEST_3(( bdcsvd_method<Matrix3f>() ));
+
+  // Test problem size constructors
+  CALL_SUBTEST_7( BDCSVD<MatrixXf>(10,10) );
+
+  // Check that preallocation avoids subsequent mallocs
+  CALL_SUBTEST_9( svd_preallocate() );
+
+  CALL_SUBTEST_2( svd_underoverflow() );
+}
+
diff --git a/test/block.cpp b/test/block.cpp
index 269acd28e..3b77b704a 100644
--- a/test/block.cpp
+++ b/test/block.cpp
@@ -130,6 +130,14 @@ template<typename MatrixType> void block(const MatrixType& m)
 
   VERIFY(numext::real(ones.col(c1).dot(ones.col(c2))) == RealScalar(rows));
   VERIFY(numext::real(ones.row(r1).dot(ones.row(r2))) == RealScalar(cols));
+  
+  // chekc that linear acccessors works on blocks
+  m1 = m1_copy;
+  if((MatrixType::Flags&RowMajorBit)==0)
+    VERIFY_IS_EQUAL(m1.leftCols(c1).coeff(r1+c1*rows), m1(r1,c1));
+  else
+    VERIFY_IS_EQUAL(m1.topRows(r1).coeff(c1+r1*cols), m1(r1,c1));
+  
 
   // now test some block-inside-of-block.
   
diff --git a/test/cholesky.cpp b/test/cholesky.cpp
index a883192ab..33e32a322 100644
--- a/test/cholesky.cpp
+++ b/test/cholesky.cpp
@@ -316,33 +316,35 @@ template<typename MatrixType> void cholesky_definiteness(const MatrixType& m)
 {
   eigen_assert(m.rows() == 2 && m.cols() == 2);
   MatrixType mat;
+  LDLT<MatrixType> ldlt(2);
+  
   {
     mat << 1, 0, 0, -1;
-    LDLT<MatrixType> ldlt(mat);
+    ldlt.compute(mat);
     VERIFY(!ldlt.isNegative());
     VERIFY(!ldlt.isPositive());
   }
   {
     mat << 1, 2, 2, 1;
-    LDLT<MatrixType> ldlt(mat);
+    ldlt.compute(mat);
     VERIFY(!ldlt.isNegative());
     VERIFY(!ldlt.isPositive());
   }
   {
     mat << 0, 0, 0, 0;
-    LDLT<MatrixType> ldlt(mat);
+    ldlt.compute(mat);
     VERIFY(ldlt.isNegative());
     VERIFY(ldlt.isPositive());
   }
   {
     mat << 0, 0, 0, 1;
-    LDLT<MatrixType> ldlt(mat);
+    ldlt.compute(mat);
     VERIFY(!ldlt.isNegative());
     VERIFY(ldlt.isPositive());
   }
   {
     mat << -1, 0, 0, 0;
-    LDLT<MatrixType> ldlt(mat);
+    ldlt.compute(mat);
     VERIFY(ldlt.isNegative());
     VERIFY(!ldlt.isPositive());
   }
diff --git a/test/cuda_basic.cu b/test/cuda_basic.cu
index 4c7e96c10..300bced02 100644
--- a/test/cuda_basic.cu
+++ b/test/cuda_basic.cu
@@ -65,7 +65,7 @@ struct redux {
 };
 
 template<typename T1, typename T2>
-struct prod {
+struct prod_test {
   EIGEN_DEVICE_FUNC
   void operator()(int i, const typename T1::Scalar* in, typename T1::Scalar* out) const
   {
@@ -125,8 +125,8 @@ void test_cuda_basic()
   CALL_SUBTEST( run_and_compare_to_cuda(redux<Array4f>(), nthreads, in, out) );
   CALL_SUBTEST( run_and_compare_to_cuda(redux<Matrix3f>(), nthreads, in, out) );
   
-  CALL_SUBTEST( run_and_compare_to_cuda(prod<Matrix3f,Matrix3f>(), nthreads, in, out) );
-  CALL_SUBTEST( run_and_compare_to_cuda(prod<Matrix4f,Vector4f>(), nthreads, in, out) );
+  CALL_SUBTEST( run_and_compare_to_cuda(prod_test<Matrix3f,Matrix3f>(), nthreads, in, out) );
+  CALL_SUBTEST( run_and_compare_to_cuda(prod_test<Matrix4f,Vector4f>(), nthreads, in, out) );
   
   CALL_SUBTEST( run_and_compare_to_cuda(diagonal<Matrix3f,Vector3f>(), nthreads, in, out) );
   CALL_SUBTEST( run_and_compare_to_cuda(diagonal<Matrix4f,Vector4f>(), nthreads, in, out) );
diff --git a/test/diagonalmatrices.cpp b/test/diagonalmatrices.cpp
index 149f1db2f..0227ba577 100644
--- a/test/diagonalmatrices.cpp
+++ b/test/diagonalmatrices.cpp
@@ -84,6 +84,13 @@ template<typename MatrixType> void diagonalmatrices(const MatrixType& m)
   
   VERIFY_IS_APPROX(m1 * (rdm1 * s1), (m1 * rdm1) * s1);
   VERIFY_IS_APPROX(m1 * (s1 * rdm1), (m1 * rdm1) * s1);
+  
+  // Diagonal to dense
+  sq_m1.setRandom();
+  sq_m2 = sq_m1;
+  VERIFY_IS_APPROX( (sq_m1 += (s1*v1).asDiagonal()), sq_m2 += (s1*v1).asDiagonal().toDenseMatrix() );
+  VERIFY_IS_APPROX( (sq_m1 -= (s1*v1).asDiagonal()), sq_m2 -= (s1*v1).asDiagonal().toDenseMatrix() );
+  VERIFY_IS_APPROX( (sq_m1 = (s1*v1).asDiagonal()), (s1*v1).asDiagonal().toDenseMatrix() );
 }
 
 void test_diagonalmatrices()
diff --git a/test/eigensolver_selfadjoint.cpp b/test/eigensolver_selfadjoint.cpp
index 3851f9df2..935736328 100644
--- a/test/eigensolver_selfadjoint.cpp
+++ b/test/eigensolver_selfadjoint.cpp
@@ -111,8 +111,17 @@ template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m)
 
   // test Tridiagonalization's methods
   Tridiagonalization<MatrixType> tridiag(symmC);
-  // FIXME tridiag.matrixQ().adjoint() does not work
+  VERIFY_IS_APPROX(tridiag.diagonal(), tridiag.matrixT().diagonal());
+  VERIFY_IS_APPROX(tridiag.subDiagonal(), tridiag.matrixT().template diagonal<-1>());
+  MatrixType T = tridiag.matrixT();
+  if(rows>1 && cols>1) {
+    // FIXME check that upper and lower part are 0:
+    //VERIFY(T.topRightCorner(rows-2, cols-2).template triangularView<Upper>().isZero());
+  }
+  VERIFY_IS_APPROX(tridiag.diagonal(), T.diagonal().real());
+  VERIFY_IS_APPROX(tridiag.subDiagonal(), T.template diagonal<1>().real());
   VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), tridiag.matrixQ() * tridiag.matrixT().eval() * MatrixType(tridiag.matrixQ()).adjoint());
+  VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), tridiag.matrixQ() * tridiag.matrixT() * tridiag.matrixQ().adjoint());
   
   // Test computation of eigenvalues from tridiagonal matrix
   if(rows > 1)
@@ -136,11 +145,14 @@ void test_eigensolver_selfadjoint()
 {
   int s = 0;
   for(int i = 0; i < g_repeat; i++) {
+    // trivial test for 1x1 matrices:
+    CALL_SUBTEST_1( selfadjointeigensolver(Matrix<float, 1, 1>()));
+    CALL_SUBTEST_1( selfadjointeigensolver(Matrix<double, 1, 1>()));
     // very important to test 3x3 and 2x2 matrices since we provide special paths for them
-    CALL_SUBTEST_1( selfadjointeigensolver(Matrix2f()) );
-    CALL_SUBTEST_1( selfadjointeigensolver(Matrix2d()) );
-    CALL_SUBTEST_1( selfadjointeigensolver(Matrix3f()) );
-    CALL_SUBTEST_1( selfadjointeigensolver(Matrix3d()) );
+    CALL_SUBTEST_12( selfadjointeigensolver(Matrix2f()) );
+    CALL_SUBTEST_12( selfadjointeigensolver(Matrix2d()) );
+    CALL_SUBTEST_13( selfadjointeigensolver(Matrix3f()) );
+    CALL_SUBTEST_13( selfadjointeigensolver(Matrix3d()) );
     CALL_SUBTEST_2( selfadjointeigensolver(Matrix4d()) );
     s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4);
     CALL_SUBTEST_3( selfadjointeigensolver(MatrixXf(s,s)) );
diff --git a/test/evaluators.cpp b/test/evaluators.cpp
index e3922c1be..f41968da8 100644
--- a/test/evaluators.cpp
+++ b/test/evaluators.cpp
@@ -1,7 +1,78 @@
-#define EIGEN_ENABLE_EVALUATORS
+
 #include "main.h"
 
-using internal::copy_using_evaluator;
+namespace Eigen {
+  
+  template<typename DstXprType, typename SrcXprType>
+  EIGEN_STRONG_INLINE
+  DstXprType& copy_using_evaluator(const EigenBase<DstXprType> &dst, const SrcXprType &src)
+  {
+    call_assignment(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar>());
+    return dst.const_cast_derived();
+  }
+  
+  template<typename DstXprType, template <typename> class StorageBase, typename SrcXprType>
+  EIGEN_STRONG_INLINE
+  const DstXprType& copy_using_evaluator(const NoAlias<DstXprType, StorageBase>& dst, const SrcXprType &src)
+  {
+    call_assignment(dst, src.derived(), internal::assign_op<typename DstXprType::Scalar>());
+    return dst.expression();
+  }
+  
+  template<typename DstXprType, typename SrcXprType>
+  EIGEN_STRONG_INLINE
+  DstXprType& copy_using_evaluator(const PlainObjectBase<DstXprType> &dst, const SrcXprType &src)
+  {
+    #ifdef EIGEN_NO_AUTOMATIC_RESIZING
+    eigen_assert((dst.size()==0 || (IsVectorAtCompileTime ? (dst.size() == src.size())
+                                                          : (dst.rows() == src.rows() && dst.cols() == src.cols())))
+                && "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
+  #else
+    dst.const_cast_derived().resizeLike(src.derived());
+  #endif
+    
+    call_assignment(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar>());
+    return dst.const_cast_derived();
+  }
+
+  template<typename DstXprType, typename SrcXprType>
+  void add_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
+  {
+    typedef typename DstXprType::Scalar Scalar;
+    call_assignment(const_cast<DstXprType&>(dst), src.derived(), internal::add_assign_op<Scalar>());
+  }
+
+  template<typename DstXprType, typename SrcXprType>
+  void subtract_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
+  {
+    typedef typename DstXprType::Scalar Scalar;
+    call_assignment(const_cast<DstXprType&>(dst), src.derived(), internal::sub_assign_op<Scalar>());
+  }
+
+  template<typename DstXprType, typename SrcXprType>
+  void multiply_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
+  {
+    typedef typename DstXprType::Scalar Scalar;
+    call_assignment(dst.const_cast_derived(), src.derived(), internal::mul_assign_op<Scalar>());
+  }
+
+  template<typename DstXprType, typename SrcXprType>
+  void divide_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
+  {
+    typedef typename DstXprType::Scalar Scalar;
+    call_assignment(dst.const_cast_derived(), src.derived(), internal::div_assign_op<Scalar>());
+  }
+  
+  template<typename DstXprType, typename SrcXprType>
+  void swap_using_evaluator(const DstXprType& dst, const SrcXprType& src)
+  {
+    typedef typename DstXprType::Scalar Scalar;
+    call_assignment(dst.const_cast_derived(), src.const_cast_derived(), internal::swap_assign_op<Scalar>());
+  }
+  
+}
+
+
 using namespace std;
 
 #define VERIFY_IS_APPROX_EVALUATOR(DEST,EXPR) VERIFY_IS_APPROX(copy_using_evaluator(DEST,(EXPR)), (EXPR).eval());
@@ -72,8 +143,19 @@ void test_evaluators()
     c = a*a;
     copy_using_evaluator(a, prod(a,a));
     VERIFY_IS_APPROX(a,c);
+
+    // check compound assignment of products
+    d = c;
+    add_assign_using_evaluator(c.noalias(), prod(a,b));
+    d.noalias() += a*b;
+    VERIFY_IS_APPROX(c, d);
+
+    d = c;
+    subtract_assign_using_evaluator(c.noalias(), prod(a,b));
+    d.noalias() -= a*b;
+    VERIFY_IS_APPROX(c, d);
   }
-  
+
   {
     // test product with all possible sizes
     int s = internal::random<int>(1,100);
@@ -124,7 +206,7 @@ void test_evaluators()
     
     // this does not work because Random is eval-before-nested: 
     // copy_using_evaluator(w, Vector2d::Random().transpose());
-    
+
     // test CwiseUnaryOp
     VERIFY_IS_APPROX_EVALUATOR(v2, 3 * v);
     VERIFY_IS_APPROX_EVALUATOR(w, (3 * v).transpose());
@@ -327,4 +409,56 @@ void test_evaluators()
     arr_ref.row(1) /= (arr_ref.row(2) + 1);
     VERIFY_IS_APPROX(arr, arr_ref);
   }
+  
+  {
+    // test triangular shapes
+    MatrixXd A = MatrixXd::Random(6,6), B(6,6), C(6,6), D(6,6);
+    A.setRandom();B.setRandom();
+    VERIFY_IS_APPROX_EVALUATOR2(B, A.triangularView<Upper>(), MatrixXd(A.triangularView<Upper>()));
+    
+    A.setRandom();B.setRandom();
+    VERIFY_IS_APPROX_EVALUATOR2(B, A.triangularView<UnitLower>(), MatrixXd(A.triangularView<UnitLower>()));
+    
+    A.setRandom();B.setRandom();
+    VERIFY_IS_APPROX_EVALUATOR2(B, A.triangularView<UnitUpper>(), MatrixXd(A.triangularView<UnitUpper>()));
+    
+    A.setRandom();B.setRandom();
+    C = B; C.triangularView<Upper>() = A;
+    copy_using_evaluator(B.triangularView<Upper>(), A);
+    VERIFY(B.isApprox(C) && "copy_using_evaluator(B.triangularView<Upper>(), A)");
+    
+    A.setRandom();B.setRandom();
+    C = B; C.triangularView<Lower>() = A.triangularView<Lower>();
+    copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Lower>());
+    VERIFY(B.isApprox(C) && "copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Lower>())");
+    
+    
+    A.setRandom();B.setRandom();
+    C = B; C.triangularView<Lower>() = A.triangularView<Upper>().transpose();
+    copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Upper>().transpose());
+    VERIFY(B.isApprox(C) && "copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Lower>().transpose())");
+    
+    
+    A.setRandom();B.setRandom(); C = B; D = A;
+    C.triangularView<Upper>().swap(D.triangularView<Upper>());
+    swap_using_evaluator(B.triangularView<Upper>(), A.triangularView<Upper>());
+    VERIFY(B.isApprox(C) && "swap_using_evaluator(B.triangularView<Upper>(), A.triangularView<Upper>())");
+    
+    
+    VERIFY_IS_APPROX_EVALUATOR2(B, prod(A.triangularView<Upper>(),A), MatrixXd(A.triangularView<Upper>()*A));
+    
+    VERIFY_IS_APPROX_EVALUATOR2(B, prod(A.selfadjointView<Upper>(),A), MatrixXd(A.selfadjointView<Upper>()*A));
+    
+  }
+
+  {
+    // test diagonal shapes
+    VectorXd d = VectorXd::Random(6);
+    MatrixXd A = MatrixXd::Random(6,6), B(6,6);
+    A.setRandom();B.setRandom();
+    
+    VERIFY_IS_APPROX_EVALUATOR2(B, lazyprod(d.asDiagonal(),A), MatrixXd(d.asDiagonal()*A));
+    VERIFY_IS_APPROX_EVALUATOR2(B, lazyprod(A,d.asDiagonal()), MatrixXd(A*d.asDiagonal()));
+    
+  }
 }
diff --git a/test/geo_homogeneous.cpp b/test/geo_homogeneous.cpp
index c91bde819..2f9d18c0f 100644
--- a/test/geo_homogeneous.cpp
+++ b/test/geo_homogeneous.cpp
@@ -38,6 +38,10 @@ template<typename Scalar,int Size> void homogeneous(void)
   hv0 << v0, 1;
   VERIFY_IS_APPROX(v0.homogeneous(), hv0);
   VERIFY_IS_APPROX(v0, hv0.hnormalized());
+  
+  VERIFY_IS_APPROX(v0.homogeneous().sum(), hv0.sum());
+  VERIFY_IS_APPROX(v0.homogeneous().minCoeff(), hv0.minCoeff());
+  VERIFY_IS_APPROX(v0.homogeneous().maxCoeff(), hv0.maxCoeff());
 
   hm0 << m0, ones.transpose();
   VERIFY_IS_APPROX(m0.colwise().homogeneous(), hm0);
@@ -57,7 +61,6 @@ template<typename Scalar,int Size> void homogeneous(void)
 
   VERIFY_IS_APPROX((v0.transpose().rowwise().homogeneous().eval()) * t2,
                     v0.transpose().rowwise().homogeneous() * t2);
-                    m0.transpose().rowwise().homogeneous().eval();
   VERIFY_IS_APPROX((m0.transpose().rowwise().homogeneous().eval()) * t2,
                     m0.transpose().rowwise().homogeneous() * t2);
 
@@ -82,7 +85,7 @@ template<typename Scalar,int Size> void homogeneous(void)
   VERIFY_IS_APPROX(aff  * pts.colwise().homogeneous(), (aff  * pts1).colwise().hnormalized());
   VERIFY_IS_APPROX(caff * pts.colwise().homogeneous(), (caff * pts1).colwise().hnormalized());
   VERIFY_IS_APPROX(proj * pts.colwise().homogeneous(), (proj * pts1));
-  
+
   VERIFY_IS_APPROX((aff  * pts1).colwise().hnormalized(),  aff  * pts);
   VERIFY_IS_APPROX((caff * pts1).colwise().hnormalized(), caff * pts);
   
diff --git a/test/geo_hyperplane.cpp b/test/geo_hyperplane.cpp
index ed5928f10..aa744a3ea 100644
--- a/test/geo_hyperplane.cpp
+++ b/test/geo_hyperplane.cpp
@@ -124,6 +124,33 @@ template<typename Scalar> void lines()
   }
 }
 
+template<typename Scalar> void planes()
+{
+  using std::abs;
+  typedef Hyperplane<Scalar, 3> Plane;
+  typedef Matrix<Scalar,3,1> Vector;
+  typedef Matrix<Scalar,4,1> CoeffsType;
+
+  for(int i = 0; i < 10; i++)
+  {
+    Vector v0 = Vector::Random();
+    Vector v1(v0), v2(v0);
+    if(internal::random<double>(0,1)>0.25)
+      v1 += Vector::Random();
+    if(internal::random<double>(0,1)>0.25)
+      v2 += v1 * std::pow(internal::random<Scalar>(0,1),internal::random<int>(1,16));
+    if(internal::random<double>(0,1)>0.25)
+      v2 += Vector::Random() * std::pow(internal::random<Scalar>(0,1),internal::random<int>(1,16));
+
+    Plane p0 = Plane::Through(v0, v1, v2);
+
+    VERIFY_IS_APPROX(p0.normal().norm(), Scalar(1));
+    VERIFY_IS_MUCH_SMALLER_THAN(p0.absDistance(v0), Scalar(1));
+    VERIFY_IS_MUCH_SMALLER_THAN(p0.absDistance(v1), Scalar(1));
+    VERIFY_IS_MUCH_SMALLER_THAN(p0.absDistance(v2), Scalar(1));
+  }
+}
+
 template<typename Scalar> void hyperplane_alignment()
 {
   typedef Hyperplane<Scalar,3,AutoAlign> Plane3a;
@@ -163,5 +190,7 @@ void test_geo_hyperplane()
     CALL_SUBTEST_4( hyperplane(Hyperplane<std::complex<double>,5>()) );
     CALL_SUBTEST_1( lines<float>() );
     CALL_SUBTEST_3( lines<double>() );
+    CALL_SUBTEST_2( planes<float>() );
+    CALL_SUBTEST_5( planes<double>() );
   }
 }
diff --git a/test/geo_orthomethods.cpp b/test/geo_orthomethods.cpp
index c836dae40..e178df257 100644
--- a/test/geo_orthomethods.cpp
+++ b/test/geo_orthomethods.cpp
@@ -33,12 +33,16 @@ template<typename Scalar> void orthomethods_3()
   VERIFY_IS_MUCH_SMALLER_THAN(v1.dot(v1.cross(v2)), Scalar(1));
   VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(v2).dot(v2), Scalar(1));
   VERIFY_IS_MUCH_SMALLER_THAN(v2.dot(v1.cross(v2)), Scalar(1));
+  VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(Vector3::Random()).dot(v1), Scalar(1));
   Matrix3 mat3;
   mat3 << v0.normalized(),
          (v0.cross(v1)).normalized(),
          (v0.cross(v1).cross(v0)).normalized();
   VERIFY(mat3.isUnitary());
-
+  
+  mat3.setRandom();
+  VERIFY_IS_APPROX(v0.cross(mat3*v1), -(mat3*v1).cross(v0));
+  VERIFY_IS_APPROX(v0.cross(mat3.lazyProduct(v1)), -(mat3.lazyProduct(v1)).cross(v0));
 
   // colwise/rowwise cross product
   mat3.setRandom();
@@ -47,6 +51,13 @@ template<typename Scalar> void orthomethods_3()
   int i = internal::random<int>(0,2);
   mcross = mat3.colwise().cross(vec3);
   VERIFY_IS_APPROX(mcross.col(i), mat3.col(i).cross(vec3));
+  
+  VERIFY_IS_MUCH_SMALLER_THAN((mat3.adjoint() * mat3.colwise().cross(vec3)).diagonal().cwiseAbs().sum(), Scalar(1));
+  VERIFY_IS_MUCH_SMALLER_THAN((mat3.adjoint() * mat3.colwise().cross(Vector3::Random())).diagonal().cwiseAbs().sum(), Scalar(1));
+  
+  VERIFY_IS_MUCH_SMALLER_THAN((vec3.adjoint() * mat3.colwise().cross(vec3)).cwiseAbs().sum(), Scalar(1));
+  VERIFY_IS_MUCH_SMALLER_THAN((vec3.adjoint() * Matrix3::Random().colwise().cross(vec3)).cwiseAbs().sum(), Scalar(1));
+  
   mcross = mat3.rowwise().cross(vec3);
   VERIFY_IS_APPROX(mcross.row(i), mat3.row(i).cross(vec3));
 
@@ -57,6 +68,7 @@ template<typename Scalar> void orthomethods_3()
   v40.w() = v41.w() = v42.w() = 0;
   v42.template head<3>() = v40.template head<3>().cross(v41.template head<3>());
   VERIFY_IS_APPROX(v40.cross3(v41), v42);
+  VERIFY_IS_MUCH_SMALLER_THAN(v40.cross3(Vector4::Random()).dot(v40), Scalar(1));
   
   // check mixed product
   typedef Matrix<RealScalar, 3, 1> RealVector3;
diff --git a/test/geo_transformations.cpp b/test/geo_transformations.cpp
index 7d9080333..042dd0329 100644
--- a/test/geo_transformations.cpp
+++ b/test/geo_transformations.cpp
@@ -98,11 +98,17 @@ template<typename Scalar, int Mode, int Options> void transformations()
   Matrix3 matrot1, m;
 
   Scalar a = internal::random<Scalar>(-Scalar(M_PI), Scalar(M_PI));
-  Scalar s0 = internal::random<Scalar>();
+  Scalar s0 = internal::random<Scalar>(), s1 = internal::random<Scalar>();
+  
+  while(v0.norm() < test_precision<Scalar>()) v0 = Vector3::Random();
+  while(v1.norm() < test_precision<Scalar>()) v1 = Vector3::Random();
 
   VERIFY_IS_APPROX(v0, AngleAxisx(a, v0.normalized()) * v0);
   VERIFY_IS_APPROX(-v0, AngleAxisx(Scalar(M_PI), v0.unitOrthogonal()) * v0);
-  VERIFY_IS_APPROX(cos(a)*v0.squaredNorm(), v0.dot(AngleAxisx(a, v0.unitOrthogonal()) * v0));
+  if(abs(cos(a)) > test_precision<Scalar>())
+  {
+    VERIFY_IS_APPROX(cos(a)*v0.squaredNorm(), v0.dot(AngleAxisx(a, v0.unitOrthogonal()) * v0));
+  }
   m = AngleAxisx(a, v0.normalized()).toRotationMatrix().adjoint();
   VERIFY_IS_APPROX(Matrix3::Identity(), m * AngleAxisx(a, v0.normalized()));
   VERIFY_IS_APPROX(Matrix3::Identity(), AngleAxisx(a, v0.normalized()) * m);
@@ -123,11 +129,18 @@ template<typename Scalar, int Mode, int Options> void transformations()
   // angle-axis conversion
   AngleAxisx aa = AngleAxisx(q1);
   VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1);
-  VERIFY_IS_NOT_APPROX(q1 * v1, Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1);
+  
+  if(abs(aa.angle()) > NumTraits<Scalar>::dummy_precision())
+  {
+    VERIFY( !(q1 * v1).isApprox(Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1) );
+  }
 
   aa.fromRotationMatrix(aa.toRotationMatrix());
   VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1);
-  VERIFY_IS_NOT_APPROX(q1 * v1, Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1);
+  if(abs(aa.angle()) > NumTraits<Scalar>::dummy_precision())
+  {
+    VERIFY( !(q1 * v1).isApprox(Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1) );
+  }
 
   // AngleAxis
   VERIFY_IS_APPROX(AngleAxisx(a,v1.normalized()).toRotationMatrix(),
@@ -347,7 +360,9 @@ template<typename Scalar, int Mode, int Options> void transformations()
   // test transform inversion
   t0.setIdentity();
   t0.translate(v0);
-  t0.linear().setRandom();
+  do {
+    t0.linear().setRandom();
+  } while(t0.linear().jacobiSvd().singularValues()(2)<test_precision<Scalar>());
   Matrix4 t044 = Matrix4::Zero();
   t044(3,3) = 1;
   t044.block(0,0,t0.matrix().rows(),4) = t0.matrix();
@@ -394,9 +409,29 @@ template<typename Scalar, int Mode, int Options> void transformations()
   Rotation2D<double> r2d1d = r2d1.template cast<double>();
   VERIFY_IS_APPROX(r2d1d.template cast<Scalar>(),r2d1);
 
-  t20 = Translation2(v20) * (Rotation2D<Scalar>(s0) * Eigen::Scaling(s0));
-  t21 = Translation2(v20) * Rotation2D<Scalar>(s0) * Eigen::Scaling(s0);
+  Rotation2D<Scalar> R0(s0), R1(s1);
+  
+  t20 = Translation2(v20) * (R0 * Eigen::Scaling(s0));
+  t21 = Translation2(v20) * R0 * Eigen::Scaling(s0);
   VERIFY_IS_APPROX(t20,t21);
+  
+  t20 = Translation2(v20) * (R0 * R0.inverse() * Eigen::Scaling(s0));
+  t21 = Translation2(v20) * Eigen::Scaling(s0);
+  VERIFY_IS_APPROX(t20,t21);
+  
+  VERIFY_IS_APPROX(s0, (R0.slerp(0, R1)).angle());
+  VERIFY_IS_APPROX(s1, (R0.slerp(1, R1)).angle());
+  VERIFY_IS_APPROX(s0, (R0.slerp(0.5, R0)).angle());
+  VERIFY_IS_APPROX(Scalar(0), (R0.slerp(0.5, R0.inverse())).angle());
+  
+  // check basic features
+  {
+    Rotation2D<Scalar> r1;           // default ctor
+    r1 = Rotation2D<Scalar>(s0);     // copy assignment
+    VERIFY_IS_APPROX(r1.angle(),s0);
+    Rotation2D<Scalar> r2(r1);       // copy ctor
+    VERIFY_IS_APPROX(r2.angle(),s0);
+  }
 }
 
 template<typename Scalar> void transform_alignment()
diff --git a/test/inverse.cpp b/test/inverse.cpp
index 8187b088d..1e7b20958 100644
--- a/test/inverse.cpp
+++ b/test/inverse.cpp
@@ -68,6 +68,15 @@ template<typename MatrixType> void inverse(const MatrixType& m)
   VERIFY_IS_MUCH_SMALLER_THAN(abs(det-m3.determinant()), RealScalar(1));
   m3.computeInverseWithCheck(m4, invertible);
   VERIFY( rows==1 ? invertible : !invertible );
+  
+  // check with submatrices
+  {
+    Matrix<Scalar, MatrixType::RowsAtCompileTime+1, MatrixType::RowsAtCompileTime+1, MatrixType::Options> m3;
+    m3.setRandom();
+    m3.topLeftCorner(rows,rows) = m1;
+    m2 = m3.template topLeftCorner<MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime>().inverse();
+    VERIFY_IS_APPROX( (m3.template topLeftCorner<MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime>()), m2.inverse() );
+  }
 #endif
 
   // check in-place inversion
diff --git a/test/jacobisvd.cpp b/test/jacobisvd.cpp
index cd04db5be..f9de6b708 100644
--- a/test/jacobisvd.cpp
+++ b/test/jacobisvd.cpp
@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // This Source Code Form is subject to the terms of the Mozilla
@@ -14,273 +14,47 @@
 #include "main.h"
 #include <Eigen/SVD>
 
-template<typename MatrixType, int QRPreconditioner>
-void jacobisvd_check_full(const MatrixType& m, const JacobiSVD<MatrixType, QRPreconditioner>& svd)
-{
-  typedef typename MatrixType::Index Index;
-  Index rows = m.rows();
-  Index cols = m.cols();
-
-  enum {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime
-  };
-
-  typedef typename MatrixType::Scalar Scalar;
-  typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime> MatrixUType;
-  typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime> MatrixVType;
-
-  MatrixType sigma = MatrixType::Zero(rows,cols);
-  sigma.diagonal() = svd.singularValues().template cast<Scalar>();
-  MatrixUType u = svd.matrixU();
-  MatrixVType v = svd.matrixV();
-
-  VERIFY_IS_APPROX(m, u * sigma * v.adjoint());
-  VERIFY_IS_UNITARY(u);
-  VERIFY_IS_UNITARY(v);
-}
-
-template<typename MatrixType, int QRPreconditioner>
-void jacobisvd_compare_to_full(const MatrixType& m,
-                               unsigned int computationOptions,
-                               const JacobiSVD<MatrixType, QRPreconditioner>& referenceSvd)
-{
-  typedef typename MatrixType::Index Index;
-  Index rows = m.rows();
-  Index cols = m.cols();
-  Index diagSize = (std::min)(rows, cols);
-
-  JacobiSVD<MatrixType, QRPreconditioner> svd(m, computationOptions);
-
-  VERIFY_IS_APPROX(svd.singularValues(), referenceSvd.singularValues());
-  if(computationOptions & ComputeFullU)
-    VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU());
-  if(computationOptions & ComputeThinU)
-    VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU().leftCols(diagSize));
-  if(computationOptions & ComputeFullV)
-    VERIFY_IS_APPROX(svd.matrixV(), referenceSvd.matrixV());
-  if(computationOptions & ComputeThinV)
-    VERIFY_IS_APPROX(svd.matrixV(), referenceSvd.matrixV().leftCols(diagSize));
-}
-
-template<typename MatrixType, int QRPreconditioner>
-void jacobisvd_solve(const MatrixType& m, unsigned int computationOptions)
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef typename MatrixType::Index Index;
-  Index rows = m.rows();
-  Index cols = m.cols();
-
-  enum {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime
-  };
-
-  typedef Matrix<Scalar, RowsAtCompileTime, Dynamic> RhsType;
-  typedef Matrix<Scalar, ColsAtCompileTime, Dynamic> SolutionType;
-
-  RhsType rhs = RhsType::Random(rows, internal::random<Index>(1, cols));
-  JacobiSVD<MatrixType, QRPreconditioner> svd(m, computationOptions);
-
-       if(internal::is_same<RealScalar,double>::value) svd.setThreshold(1e-8);
-  else if(internal::is_same<RealScalar,float>::value)  svd.setThreshold(1e-4);
-  
-  SolutionType x = svd.solve(rhs);
-  
-  RealScalar residual = (m*x-rhs).norm();
-  // Check that there is no significantly better solution in the neighborhood of x
-  if(!test_isMuchSmallerThan(residual,rhs.norm()))
-  {
-    // If the residual is very small, then we have an exact solution, so we are already good.
-    for(int k=0;k<x.rows();++k)
-    {
-      SolutionType y(x);
-      y.row(k).array() += 2*NumTraits<RealScalar>::epsilon();
-      RealScalar residual_y = (m*y-rhs).norm();
-      VERIFY( test_isApprox(residual_y,residual) || residual < residual_y );
-      
-      y.row(k) = x.row(k).array() - 2*NumTraits<RealScalar>::epsilon();
-      residual_y = (m*y-rhs).norm();
-      VERIFY( test_isApprox(residual_y,residual) || residual < residual_y );
-    }
-  }
-  
-  // evaluate normal equation which works also for least-squares solutions
-  if(internal::is_same<RealScalar,double>::value)
-  {
-    // This test is not stable with single precision.
-    // This is probably because squaring m signicantly affects the precision.
-    VERIFY_IS_APPROX(m.adjoint()*m*x,m.adjoint()*rhs);
-  }
-  
-  // check minimal norm solutions
-  {
-    // generate a full-rank m x n problem with m<n
-    enum {
-      RankAtCompileTime2 = ColsAtCompileTime==Dynamic ? Dynamic : (ColsAtCompileTime)/2+1,
-      RowsAtCompileTime3 = ColsAtCompileTime==Dynamic ? Dynamic : ColsAtCompileTime+1
-    };
-    typedef Matrix<Scalar, RankAtCompileTime2, ColsAtCompileTime> MatrixType2;
-    typedef Matrix<Scalar, RankAtCompileTime2, 1> RhsType2;
-    typedef Matrix<Scalar, ColsAtCompileTime, RankAtCompileTime2> MatrixType2T;
-    Index rank = RankAtCompileTime2==Dynamic ? internal::random<Index>(1,cols) : Index(RankAtCompileTime2);
-    MatrixType2 m2(rank,cols);
-    int guard = 0;
-    do {
-      m2.setRandom();
-    } while(m2.jacobiSvd().setThreshold(test_precision<Scalar>()).rank()!=rank && (++guard)<10);
-    VERIFY(guard<10);
-    RhsType2 rhs2 = RhsType2::Random(rank);
-    // use QR to find a reference minimal norm solution
-    HouseholderQR<MatrixType2T> qr(m2.adjoint());
-    Matrix<Scalar,Dynamic,1> tmp = qr.matrixQR().topLeftCorner(rank,rank).template triangularView<Upper>().adjoint().solve(rhs2);
-    tmp.conservativeResize(cols);
-    tmp.tail(cols-rank).setZero();
-    SolutionType x21 = qr.householderQ() * tmp;
-    // now check with SVD
-    JacobiSVD<MatrixType2, ColPivHouseholderQRPreconditioner> svd2(m2, computationOptions);
-    SolutionType x22 = svd2.solve(rhs2);
-    VERIFY_IS_APPROX(m2*x21, rhs2);
-    VERIFY_IS_APPROX(m2*x22, rhs2);
-    VERIFY_IS_APPROX(x21, x22);
-    
-    // Now check with a rank deficient matrix
-    typedef Matrix<Scalar, RowsAtCompileTime3, ColsAtCompileTime> MatrixType3;
-    typedef Matrix<Scalar, RowsAtCompileTime3, 1> RhsType3;
-    Index rows3 = RowsAtCompileTime3==Dynamic ? internal::random<Index>(rank+1,2*cols) : Index(RowsAtCompileTime3);
-    Matrix<Scalar,RowsAtCompileTime3,Dynamic> C = Matrix<Scalar,RowsAtCompileTime3,Dynamic>::Random(rows3,rank);
-    MatrixType3 m3 = C * m2;
-    RhsType3 rhs3 = C * rhs2;
-    JacobiSVD<MatrixType3, ColPivHouseholderQRPreconditioner> svd3(m3, computationOptions);
-    SolutionType x3 = svd3.solve(rhs3);
-    VERIFY_IS_APPROX(m3*x3, rhs3);
-    VERIFY_IS_APPROX(m3*x21, rhs3);
-    VERIFY_IS_APPROX(m2*x3, rhs2);
-    
-    VERIFY_IS_APPROX(x21, x3);
-  }
-}
-
-template<typename MatrixType, int QRPreconditioner>
-void jacobisvd_test_all_computation_options(const MatrixType& m)
-{
-  if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols())
-    return;
-  JacobiSVD<MatrixType, QRPreconditioner> fullSvd(m, ComputeFullU|ComputeFullV);
-  CALL_SUBTEST(( jacobisvd_check_full(m, fullSvd) ));
-  CALL_SUBTEST(( jacobisvd_solve<MatrixType, QRPreconditioner>(m, ComputeFullU | ComputeFullV) ));
-  
-  #if defined __INTEL_COMPILER
-  // remark #111: statement is unreachable
-  #pragma warning disable 111
-  #endif
-  if(QRPreconditioner == FullPivHouseholderQRPreconditioner)
-    return;
-
-  CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeFullU, fullSvd) ));
-  CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeFullV, fullSvd) ));
-  CALL_SUBTEST(( jacobisvd_compare_to_full(m, 0, fullSvd) ));
-
-  if (MatrixType::ColsAtCompileTime == Dynamic) {
-    // thin U/V are only available with dynamic number of columns
-    CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeFullU|ComputeThinV, fullSvd) ));
-    CALL_SUBTEST(( jacobisvd_compare_to_full(m,              ComputeThinV, fullSvd) ));
-    CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeThinU|ComputeFullV, fullSvd) ));
-    CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeThinU             , fullSvd) ));
-    CALL_SUBTEST(( jacobisvd_compare_to_full(m, ComputeThinU|ComputeThinV, fullSvd) ));
-    CALL_SUBTEST(( jacobisvd_solve<MatrixType, QRPreconditioner>(m, ComputeFullU | ComputeThinV) ));
-    CALL_SUBTEST(( jacobisvd_solve<MatrixType, QRPreconditioner>(m, ComputeThinU | ComputeFullV) ));
-    CALL_SUBTEST(( jacobisvd_solve<MatrixType, QRPreconditioner>(m, ComputeThinU | ComputeThinV) ));
-
-    // test reconstruction
-    typedef typename MatrixType::Index Index;
-    Index diagSize = (std::min)(m.rows(), m.cols());
-    JacobiSVD<MatrixType, QRPreconditioner> svd(m, ComputeThinU | ComputeThinV);
-    VERIFY_IS_APPROX(m, svd.matrixU().leftCols(diagSize) * svd.singularValues().asDiagonal() * svd.matrixV().leftCols(diagSize).adjoint());
-  }
-}
+#define SVD_DEFAULT(M) JacobiSVD<M>
+#define SVD_FOR_MIN_NORM(M) JacobiSVD<M,ColPivHouseholderQRPreconditioner>
+#include "svd_common.h"
 
+// Check all variants of JacobiSVD
 template<typename MatrixType>
 void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
 {
   MatrixType m = a;
   if(pickrandom)
-  {
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::Index Index;
-    Index diagSize = (std::min)(a.rows(), a.cols());
-    RealScalar s = std::numeric_limits<RealScalar>::max_exponent10/4;
-    s = internal::random<RealScalar>(1,s);
-    Matrix<RealScalar,Dynamic,1> d =  Matrix<RealScalar,Dynamic,1>::Random(diagSize);
-    for(Index k=0; k<diagSize; ++k)
-      d(k) = d(k)*std::pow(RealScalar(10),internal::random<RealScalar>(-s,s));
-    m = Matrix<Scalar,Dynamic,Dynamic>::Random(a.rows(),diagSize) * d.asDiagonal() * Matrix<Scalar,Dynamic,Dynamic>::Random(diagSize,a.cols());
-    // cancel some coeffs
-    Index n  = internal::random<Index>(0,m.size()-1);
-    for(Index i=0; i<n; ++i)
-      m(internal::random<Index>(0,m.rows()-1), internal::random<Index>(0,m.cols()-1)) = Scalar(0);
-  }
+    svd_fill_random(m);
 
-  CALL_SUBTEST(( jacobisvd_test_all_computation_options<MatrixType, FullPivHouseholderQRPreconditioner>(m) ));
-  CALL_SUBTEST(( jacobisvd_test_all_computation_options<MatrixType, ColPivHouseholderQRPreconditioner>(m) ));
-  CALL_SUBTEST(( jacobisvd_test_all_computation_options<MatrixType, HouseholderQRPreconditioner>(m) ));
-  CALL_SUBTEST(( jacobisvd_test_all_computation_options<MatrixType, NoQRPreconditioner>(m) ));
+  CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner> >(m, true)  )); // check full only
+  CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>  >(m, false) ));
+  CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, HouseholderQRPreconditioner>        >(m, false) ));
+  if(m.rows()==m.cols())
+    CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, NoQRPreconditioner>               >(m, false) ));
 }
 
 template<typename MatrixType> void jacobisvd_verify_assert(const MatrixType& m)
 {
-  typedef typename MatrixType::Scalar Scalar;
+  svd_verify_assert<JacobiSVD<MatrixType> >(m);
   typedef typename MatrixType::Index Index;
   Index rows = m.rows();
   Index cols = m.cols();
 
   enum {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
     ColsAtCompileTime = MatrixType::ColsAtCompileTime
   };
 
-  typedef Matrix<Scalar, RowsAtCompileTime, 1> RhsType;
-
-  RhsType rhs(rows);
-
-  JacobiSVD<MatrixType> svd;
-  VERIFY_RAISES_ASSERT(svd.matrixU())
-  VERIFY_RAISES_ASSERT(svd.singularValues())
-  VERIFY_RAISES_ASSERT(svd.matrixV())
-  VERIFY_RAISES_ASSERT(svd.solve(rhs))
 
   MatrixType a = MatrixType::Zero(rows, cols);
   a.setZero();
-  svd.compute(a, 0);
-  VERIFY_RAISES_ASSERT(svd.matrixU())
-  VERIFY_RAISES_ASSERT(svd.matrixV())
-  svd.singularValues();
-  VERIFY_RAISES_ASSERT(svd.solve(rhs))
 
   if (ColsAtCompileTime == Dynamic)
   {
-    svd.compute(a, ComputeThinU);
-    svd.matrixU();
-    VERIFY_RAISES_ASSERT(svd.matrixV())
-    VERIFY_RAISES_ASSERT(svd.solve(rhs))
-
-    svd.compute(a, ComputeThinV);
-    svd.matrixV();
-    VERIFY_RAISES_ASSERT(svd.matrixU())
-    VERIFY_RAISES_ASSERT(svd.solve(rhs))
-
     JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner> svd_fullqr;
     VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeFullU|ComputeThinV))
     VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeThinV))
     VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeFullV))
   }
-  else
-  {
-    VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinU))
-    VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinV))
-  }
 }
 
 template<typename MatrixType>
@@ -296,165 +70,17 @@ void jacobisvd_method()
   VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m);
 }
 
-// work around stupid msvc error when constructing at compile time an expression that involves
-// a division by zero, even if the numeric type has floating point
-template<typename Scalar>
-EIGEN_DONT_INLINE Scalar zero() { return Scalar(0); }
-
-// workaround aggressive optimization in ICC
-template<typename T> EIGEN_DONT_INLINE  T sub(T a, T b) { return a - b; }
-
-template<typename MatrixType>
-void jacobisvd_inf_nan()
-{
-  // all this function does is verify we don't iterate infinitely on nan/inf values
-
-  JacobiSVD<MatrixType> svd;
-  typedef typename MatrixType::Scalar Scalar;
-  Scalar some_inf = Scalar(1) / zero<Scalar>();
-  VERIFY(sub(some_inf, some_inf) != sub(some_inf, some_inf));
-  svd.compute(MatrixType::Constant(10,10,some_inf), ComputeFullU | ComputeFullV);
-
-  Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
-  VERIFY(nan != nan);
-  svd.compute(MatrixType::Constant(10,10,nan), ComputeFullU | ComputeFullV);
-
-  MatrixType m = MatrixType::Zero(10,10);
-  m(internal::random<int>(0,9), internal::random<int>(0,9)) = some_inf;
-  svd.compute(m, ComputeFullU | ComputeFullV);
-
-  m = MatrixType::Zero(10,10);
-  m(internal::random<int>(0,9), internal::random<int>(0,9)) = nan;
-  svd.compute(m, ComputeFullU | ComputeFullV);
-  
-  // regression test for bug 791
-  m.resize(3,3);
-  m << 0,    2*NumTraits<Scalar>::epsilon(),  0.5,
-       0,   -0.5,                             0,
-       nan,  0,                               0;
-  svd.compute(m, ComputeFullU | ComputeFullV);
-  
-  m.resize(4,4);
-  m <<  1, 0, 0, 0,
-        0, 3, 1, 2e-308,
-        1, 0, 1, nan,
-        0, nan, nan, 0;
-  svd.compute(m, ComputeFullU | ComputeFullV);
-}
-
-// Regression test for bug 286: JacobiSVD loops indefinitely with some
-// matrices containing denormal numbers.
-void jacobisvd_underoverflow()
-{
-#if defined __INTEL_COMPILER
-// shut up warning #239: floating point underflow
-#pragma warning push
-#pragma warning disable 239
-#endif
-  Matrix2d M;
-  M << -7.90884e-313, -4.94e-324,
-                 0, 5.60844e-313;
-  JacobiSVD<Matrix2d> svd;
-  svd.compute(M,ComputeFullU|ComputeFullV);
-  jacobisvd_check_full(M,svd);
-  
-  VectorXd value_set(9);
-  value_set << 0, 1, -1, 5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324, -4.94e-223, 4.94e-223;
-  Array4i id(0,0,0,0);
-  int k = 0;
-  do
-  {
-    M << value_set(id(0)), value_set(id(1)), value_set(id(2)), value_set(id(3));
-    svd.compute(M,ComputeFullU|ComputeFullV);
-    jacobisvd_check_full(M,svd);
-    
-    id(k)++;
-    if(id(k)>=value_set.size())
-    {
-      while(k<3 && id(k)>=value_set.size()) id(++k)++;
-      id.head(k).setZero();
-      k=0;
-    }
-    
-  } while((id<int(value_set.size())).all());
-  
-#if defined __INTEL_COMPILER
-#pragma warning pop
-#endif
-  
-  // Check for overflow:
-  Matrix3d M3;
-  M3 << 4.4331978442502944e+307, -5.8585363752028680e+307,  6.4527017443412964e+307,
-        3.7841695601406358e+307,  2.4331702789740617e+306, -3.5235707140272905e+307,
-       -8.7190887618028355e+307, -7.3453213709232193e+307, -2.4367363684472105e+307;
-
-  JacobiSVD<Matrix3d> svd3;
-  svd3.compute(M3,ComputeFullU|ComputeFullV); // just check we don't loop indefinitely
-  jacobisvd_check_full(M3,svd3);
-}
-
-void jacobisvd_preallocate()
-{
-  Vector3f v(3.f, 2.f, 1.f);
-  MatrixXf m = v.asDiagonal();
-
-  internal::set_is_malloc_allowed(false);
-  VERIFY_RAISES_ASSERT(VectorXf tmp(10);)
-  JacobiSVD<MatrixXf> svd;
-  internal::set_is_malloc_allowed(true);
-  svd.compute(m);
-  VERIFY_IS_APPROX(svd.singularValues(), v);
-
-  JacobiSVD<MatrixXf> svd2(3,3);
-  internal::set_is_malloc_allowed(false);
-  svd2.compute(m);
-  internal::set_is_malloc_allowed(true);
-  VERIFY_IS_APPROX(svd2.singularValues(), v);
-  VERIFY_RAISES_ASSERT(svd2.matrixU());
-  VERIFY_RAISES_ASSERT(svd2.matrixV());
-  svd2.compute(m, ComputeFullU | ComputeFullV);
-  VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity());
-  VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity());
-  internal::set_is_malloc_allowed(false);
-  svd2.compute(m);
-  internal::set_is_malloc_allowed(true);
-
-  JacobiSVD<MatrixXf> svd3(3,3,ComputeFullU|ComputeFullV);
-  internal::set_is_malloc_allowed(false);
-  svd2.compute(m);
-  internal::set_is_malloc_allowed(true);
-  VERIFY_IS_APPROX(svd2.singularValues(), v);
-  VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity());
-  VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity());
-  internal::set_is_malloc_allowed(false);
-  svd2.compute(m, ComputeFullU|ComputeFullV);
-  internal::set_is_malloc_allowed(true);
-}
-
 void test_jacobisvd()
 {
   CALL_SUBTEST_3(( jacobisvd_verify_assert(Matrix3f()) ));
   CALL_SUBTEST_4(( jacobisvd_verify_assert(Matrix4d()) ));
   CALL_SUBTEST_7(( jacobisvd_verify_assert(MatrixXf(10,12)) ));
   CALL_SUBTEST_8(( jacobisvd_verify_assert(MatrixXcd(7,5)) ));
+  
+  CALL_SUBTEST_11(svd_all_trivial_2x2(jacobisvd<Matrix2cd>));
+  CALL_SUBTEST_12(svd_all_trivial_2x2(jacobisvd<Matrix2d>));
 
   for(int i = 0; i < g_repeat; i++) {
-    Matrix2cd m;
-    m << 0, 1,
-         0, 1;
-    CALL_SUBTEST_1(( jacobisvd(m, false) ));
-    m << 1, 0,
-         1, 0;
-    CALL_SUBTEST_1(( jacobisvd(m, false) ));
-
-    Matrix2d n;
-    n << 0, 0,
-         0, 0;
-    CALL_SUBTEST_2(( jacobisvd(n, false) ));
-    n << 0, 0,
-         0, 1;
-    CALL_SUBTEST_2(( jacobisvd(n, false) ));
-    
     CALL_SUBTEST_3(( jacobisvd<Matrix3f>() ));
     CALL_SUBTEST_4(( jacobisvd<Matrix4d>() ));
     CALL_SUBTEST_5(( jacobisvd<Matrix<float,3,5> >() ));
@@ -473,8 +99,8 @@ void test_jacobisvd()
     (void) c;
 
     // Test on inf/nan matrix
-    CALL_SUBTEST_7( jacobisvd_inf_nan<MatrixXf>() );
-    CALL_SUBTEST_10( jacobisvd_inf_nan<MatrixXd>() );
+    CALL_SUBTEST_7(  (svd_inf_nan<JacobiSVD<MatrixXf>, MatrixXf>()) );
+    CALL_SUBTEST_10( (svd_inf_nan<JacobiSVD<MatrixXd>, MatrixXd>()) );
   }
 
   CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) ));
@@ -488,8 +114,7 @@ void test_jacobisvd()
   CALL_SUBTEST_7( JacobiSVD<MatrixXf>(10,10) );
 
   // Check that preallocation avoids subsequent mallocs
-  CALL_SUBTEST_9( jacobisvd_preallocate() );
+  CALL_SUBTEST_9( svd_preallocate() );
 
-  // Regression check for bug 286
-  CALL_SUBTEST_2( jacobisvd_underoverflow() );
+  CALL_SUBTEST_2( svd_underoverflow() );
 }
diff --git a/test/linearstructure.cpp b/test/linearstructure.cpp
index b627915ce..8e3cc9a86 100644
--- a/test/linearstructure.cpp
+++ b/test/linearstructure.cpp
@@ -9,7 +9,6 @@
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 
 static bool g_called;
-
 #define EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN { g_called = true; }
 
 #include "main.h"
@@ -93,6 +92,8 @@ template<typename MatrixType> void real_complex(DenseIndex rows = MatrixType::Ro
 
 void test_linearstructure()
 {
+  g_called = true;
+  VERIFY(g_called); // avoid `unneeded-internal-declaration` warning.
   for(int i = 0; i < g_repeat; i++) {
     CALL_SUBTEST_1( linearStructure(Matrix<float, 1, 1>()) );
     CALL_SUBTEST_2( linearStructure(Matrix2f()) );
@@ -107,4 +108,19 @@ void test_linearstructure()
     CALL_SUBTEST_10( real_complex<Matrix4cd>() );
     CALL_SUBTEST_10( real_complex<MatrixXcf>(10,10) );
   }
+  
+#ifdef EIGEN_TEST_PART_4
+  {
+    // make sure that /=scalar and /scalar do not overflow
+    // rational: 1.0/4.94e-320 overflow, but m/4.94e-320 should not
+    Matrix4d m2, m3;
+    m3 = m2 =  Matrix4d::Random()*1e-20;
+    m2 = m2 / 4.9e-320;
+    VERIFY_IS_APPROX(m2.cwiseQuotient(m2), Matrix4d::Ones());
+    m3 /= 4.9e-320;
+    VERIFY_IS_APPROX(m3.cwiseQuotient(m3), Matrix4d::Ones());
+    
+    
+  }
+#endif
 }
diff --git a/test/main.h b/test/main.h
index 9cb41c828..579cd2131 100644
--- a/test/main.h
+++ b/test/main.h
@@ -61,7 +61,7 @@
 #endif
 
 // shuts down ICC's remark #593: variable "XXX" was set but never used
-#define TEST_SET_BUT_UNUSED_VARIABLE(X) X = X + 0;
+#define TEST_SET_BUT_UNUSED_VARIABLE(X) EIGEN_UNUSED_VARIABLE(X)
 
 // the following file is automatically generated by cmake
 #include "split_test_helper.h"
@@ -76,7 +76,7 @@
 #endif
 
 // bounds integer values for AltiVec
-#ifdef __ALTIVEC__
+#if defined(__ALTIVEC__) || defined(__VSX__)
 #define EIGEN_MAKING_DOCS
 #endif
 
@@ -94,6 +94,9 @@ namespace Eigen
   static bool g_has_set_repeat, g_has_set_seed;
 }
 
+#define TRACK std::cerr << __FILE__ << " " << __LINE__ << std::endl
+// #define TRACK while()
+
 #define EI_PP_MAKE_STRING2(S) #S
 #define EI_PP_MAKE_STRING(S) EI_PP_MAKE_STRING2(S)
 
@@ -312,13 +315,7 @@ inline bool test_isApproxOrLessThan(const long double& a, const long double& b)
 template<typename Type1, typename Type2>
 inline bool test_isApprox(const Type1& a, const Type2& b)
 {
-#ifdef EIGEN_TEST_EVALUATORS
-  typename internal::eval<Type1>::type a_eval(a);
-  typename internal::eval<Type2>::type b_eval(b);
-  return a_eval.isApprox(b_eval, test_precision<typename Type1::Scalar>());
-#else
   return a.isApprox(b, test_precision<typename Type1::Scalar>());
-#endif
 }
 
 // The idea behind this function is to compare the two scalars a and b where
@@ -436,6 +433,26 @@ void randomPermutationVector(PermutationVectorType& v, typename PermutationVecto
   }
 }
 
+template<typename T> bool isNotNaN(const T& x)
+{
+  return x==x;
+}
+
+template<typename T> bool isNaN(const T& x)
+{
+  return x!=x;
+}
+
+template<typename T> bool isInf(const T& x)
+{
+  return x > NumTraits<T>::highest();
+}
+
+template<typename T> bool isMinusInf(const T& x)
+{
+  return x < NumTraits<T>::lowest();
+}
+
 } // end namespace Eigen
 
 template<typename T> struct GetDifferentType;
diff --git a/test/mixingtypes.cpp b/test/mixingtypes.cpp
index 1e0e2d4c1..048f7255a 100644
--- a/test/mixingtypes.cpp
+++ b/test/mixingtypes.cpp
@@ -53,10 +53,11 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType)
   mf+mf;
   VERIFY_RAISES_ASSERT(mf+md);
   VERIFY_RAISES_ASSERT(mf+mcf);
-  VERIFY_RAISES_ASSERT(vf=vd);
-  VERIFY_RAISES_ASSERT(vf+=vd);
-  VERIFY_RAISES_ASSERT(mcd=md);
-
+  // the following do not even compile since the introduction of evaluators
+//   VERIFY_RAISES_ASSERT(vf=vd);
+//   VERIFY_RAISES_ASSERT(vf+=vd);
+//   VERIFY_RAISES_ASSERT(mcd=md);
+  
   // check scalar products
   VERIFY_IS_APPROX(vcf * sf , vcf * complex<float>(sf));
   VERIFY_IS_APPROX(sd * vcd, complex<double>(sd) * vcd);
diff --git a/test/nesting_ops.cpp b/test/nesting_ops.cpp
index 1e8523283..6e772c70f 100644
--- a/test/nesting_ops.cpp
+++ b/test/nesting_ops.cpp
@@ -11,7 +11,7 @@
 
 template <typename MatrixType> void run_nesting_ops(const MatrixType& _m)
 {
-  typename MatrixType::Nested m(_m);
+  typename internal::nested_eval<MatrixType,2>::type m(_m);
 
   // Make really sure that we are in debug mode!
   VERIFY_RAISES_ASSERT(eigen_assert(false));
diff --git a/test/nomalloc.cpp b/test/nomalloc.cpp
index cbd02dd21..306664210 100644
--- a/test/nomalloc.cpp
+++ b/test/nomalloc.cpp
@@ -21,7 +21,7 @@
 // discard stack allocation as that too bypasses malloc
 #define EIGEN_STACK_ALLOCATION_LIMIT 0
 // any heap allocation will raise an assert
-#define EIGEN_NO_MALLOC
+#define EIGEN_RUNTIME_NO_MALLOC
 
 #include "main.h"
 #include <Eigen/Cholesky>
@@ -165,8 +165,62 @@ void ctms_decompositions()
   Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV);
 }
 
+void test_zerosized() {
+  // default constructors:
+  Eigen::MatrixXd A;
+  Eigen::VectorXd v;
+  // explicit zero-sized:
+  Eigen::ArrayXXd A0(0,0);
+  Eigen::ArrayXd v0(0);
+
+  // assigning empty objects to each other:
+  A=A0;
+  v=v0;
+}
+
+template<typename MatrixType> void test_reference(const MatrixType& m) {
+  typedef typename MatrixType::Scalar Scalar;
+  enum { Flag          =  MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
+  enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
+  typename MatrixType::Index rows = m.rows(), cols=m.cols();
+  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag         > MatrixX;
+  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> MatrixXT;
+  // Dynamic reference:
+  typedef Eigen::Ref<const MatrixX  > Ref;
+  typedef Eigen::Ref<const MatrixXT > RefT;
+
+  Ref r1(m);
+  Ref r2(m.block(rows/3, cols/4, rows/2, cols/2));
+  RefT r3(m.transpose());
+  RefT r4(m.topLeftCorner(rows/2, cols/2).transpose());
+
+  VERIFY_RAISES_ASSERT(RefT r5(m));
+  VERIFY_RAISES_ASSERT(Ref r6(m.transpose()));
+  VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m));
+
+  // Copy constructors shall also never malloc
+  Ref r8 = r1;
+  RefT r9 = r3;
+
+  // Initializing from a compatible Ref shall also never malloc
+  Eigen::Ref<const MatrixX, Unaligned, Stride<Dynamic, Dynamic> > r10=r8, r11=m;
+
+  // Initializing from an incompatible Ref will malloc:
+  typedef Eigen::Ref<const MatrixX, Aligned> RefAligned;
+  VERIFY_RAISES_ASSERT(RefAligned r12=r10);
+  VERIFY_RAISES_ASSERT(Ref r13=r10); // r10 has more dynamic strides
+
+}
+
 void test_nomalloc()
 {
+  // create some dynamic objects
+  Eigen::MatrixXd M1 = MatrixXd::Random(3,3);
+  Ref<const MatrixXd> R1 = 2.0*M1; // Ref requires temporary
+
+  // from here on prohibit malloc:
+  Eigen::internal::set_is_malloc_allowed(false);
+
   // check that our operator new is indeed called:
   VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3)));
   CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) );
@@ -176,4 +230,9 @@ void test_nomalloc()
   // Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms)
   CALL_SUBTEST_4(ctms_decompositions<float>());
 
+  CALL_SUBTEST_5(test_zerosized());
+
+  CALL_SUBTEST_6(test_reference(Matrix<float,32,32>()));
+  CALL_SUBTEST_7(test_reference(R1));
+  CALL_SUBTEST_8(Ref<MatrixXd> R2 = M1.topRows<2>(); test_reference(R2));
 }
diff --git a/test/nullary.cpp b/test/nullary.cpp
index 5408d88b2..fbc721a1a 100644
--- a/test/nullary.cpp
+++ b/test/nullary.cpp
@@ -80,7 +80,9 @@ void testVectorType(const VectorType& base)
   Matrix<Scalar,1,Dynamic> col_vector(size);
   row_vector.setLinSpaced(size,low,high);
   col_vector.setLinSpaced(size,low,high);
-  VERIFY( row_vector.isApprox(col_vector.transpose(), NumTraits<Scalar>::epsilon()));
+  // when using the extended precision (e.g., FPU) the relative error might exceed 1 bit
+  // when computing the squared sum in isApprox, thus the 2x factor.
+  VERIFY( row_vector.isApprox(col_vector.transpose(), Scalar(2)*NumTraits<Scalar>::epsilon()));
 
   Matrix<Scalar,Dynamic,1> size_changer(size+50);
   size_changer.setLinSpaced(size,low,high);
diff --git a/test/packetmath.cpp b/test/packetmath.cpp
index af9be89ca..49f601907 100644
--- a/test/packetmath.cpp
+++ b/test/packetmath.cpp
@@ -156,7 +156,7 @@ template<typename Scalar> void packetmath()
   CHECK_CWISE2(REF_ADD,  internal::padd);
   CHECK_CWISE2(REF_SUB,  internal::psub);
   CHECK_CWISE2(REF_MUL,  internal::pmul);
-  #ifndef EIGEN_VECTORIZE_ALTIVEC
+  #if !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX)
   if (!internal::is_same<Scalar,int>::value)
     CHECK_CWISE2(REF_DIV,  internal::pdiv);
   #endif
@@ -313,6 +313,12 @@ template<typename Scalar> void packetmath_real()
     data2[i] = internal::random<Scalar>(-87,88);
   }
   CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasExp, std::exp, internal::pexp);
+  {
+    data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
+    packet_helper<internal::packet_traits<Scalar>::HasExp,Packet> h;
+    h.store(data2, internal::pexp(h.load(data1))); 
+    VERIFY(isNaN(data2[0]));
+  }
 
   for (int i=0; i<size; ++i)
   {
@@ -321,8 +327,22 @@ template<typename Scalar> void packetmath_real()
   }
   if(internal::random<float>(0,1)<0.1)
     data1[internal::random<int>(0, PacketSize)] = 0;
-  CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasLog, std::log, internal::plog);
   CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasSqrt, std::sqrt, internal::psqrt);
+  CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasLog, std::log, internal::plog);
+  {
+    data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
+    packet_helper<internal::packet_traits<Scalar>::HasLog,Packet> h;
+    h.store(data2, internal::plog(h.load(data1)));
+    VERIFY(isNaN(data2[0]));
+    data1[0] = -1.0f;
+    h.store(data2, internal::plog(h.load(data1)));
+    VERIFY(isNaN(data2[0]));
+#if !EIGEN_FAST_MATH
+    h.store(data2, internal::psqrt(h.load(data1)));
+    VERIFY(isNaN(data2[0]));
+    VERIFY(isNaN(data2[1]));
+#endif
+  }
 }
 
 template<typename Scalar> void packetmath_notcomplex()
diff --git a/test/product_mmtr.cpp b/test/product_mmtr.cpp
index 7d6746800..92e6b668f 100644
--- a/test/product_mmtr.cpp
+++ b/test/product_mmtr.cpp
@@ -13,7 +13,8 @@
     ref2 = ref1 = DEST;                               \
     DEST.template triangularView<TRI>() OP;           \
     ref1 OP;                                          \
-    ref2.template triangularView<TRI>() = ref1;       \
+    ref2.template triangularView<TRI>()               \
+      = ref1.template triangularView<TRI>();          \
     VERIFY_IS_APPROX(DEST,ref2);                      \
   }
 
diff --git a/test/product_notemporary.cpp b/test/product_notemporary.cpp
index 3a9df618b..805cc8939 100644
--- a/test/product_notemporary.cpp
+++ b/test/product_notemporary.cpp
@@ -113,8 +113,7 @@ template<typename MatrixType> void product_notemporary(const MatrixType& m)
   VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) /  (m3.transpose() * m3).diagonal().array().abs().sum(), 0 );
 
   // Zero temporaries for ... CoeffBasedProductMode
-  // - does not work with GCC because of the <..>, we'ld need variadic macros ...
-  //VERIFY_EVALUATION_COUNT( m3.col(0).head<5>() * m3.col(0).transpose() + m3.col(0).head<5>() * m3.col(0).transpose(), 0 );
+  VERIFY_EVALUATION_COUNT( m3.col(0).template head<5>() * m3.col(0).transpose() + m3.col(0).template head<5>() * m3.col(0).transpose(), 0 );
 
   // Check matrix * vectors
   VERIFY_EVALUATION_COUNT( cvres.noalias() = m1 * cv1, 0 );
diff --git a/test/product_small.cpp b/test/product_small.cpp
index 8b132abb6..091955a0f 100644
--- a/test/product_small.cpp
+++ b/test/product_small.cpp
@@ -9,6 +9,7 @@
 
 #define EIGEN_NO_STATIC_ASSERT
 #include "product.h"
+#include <Eigen/LU>
 
 // regression test for bug 447
 void product1x1()
@@ -46,5 +47,14 @@ void test_product_small()
     Vector3f v = Vector3f::Random();
     VERIFY_IS_APPROX( (v * v.transpose()) * v, (v * v.transpose()).eval() * v);
   }
+  
+  {
+    // regression test for pull-request #93
+    Eigen::Matrix<double, 1, 1> A;  A.setRandom();
+    Eigen::Matrix<double, 18, 1> B; B.setRandom();
+    Eigen::Matrix<double, 1, 18> C; C.setRandom();
+    VERIFY_IS_APPROX(B * A.inverse(), B * A.inverse()[0]);
+    VERIFY_IS_APPROX(A.inverse() * C, A.inverse()[0] * C);
+  }
 #endif
 }
diff --git a/test/qr_fullpivoting.cpp b/test/qr_fullpivoting.cpp
index 511f2473f..601773404 100644
--- a/test/qr_fullpivoting.cpp
+++ b/test/qr_fullpivoting.cpp
@@ -40,7 +40,11 @@ template<typename MatrixType> void qr()
   MatrixType c = qr.matrixQ() * r * qr.colsPermutation().inverse();
 
   VERIFY_IS_APPROX(m1, c);
-
+  
+  // stress the ReturnByValue mechanism
+  MatrixType tmp;
+  VERIFY_IS_APPROX(tmp.noalias() = qr.matrixQ() * r, (qr.matrixQ() * r).eval());
+  
   MatrixType m2 = MatrixType::Random(cols,cols2);
   MatrixType m3 = m1*m2;
   m2 = MatrixType::Random(cols,cols2);
diff --git a/test/ref.cpp b/test/ref.cpp
index d91e3b54c..b9470213c 100644
--- a/test/ref.cpp
+++ b/test/ref.cpp
@@ -182,15 +182,15 @@ void call_ref()
 
   VERIFY_EVALUATION_COUNT( call_ref_1(a,a), 0);
   VERIFY_EVALUATION_COUNT( call_ref_1(b,b.transpose()), 0);
-//   call_ref_1(ac);           // does not compile because ac is const
+//   call_ref_1(ac,a<c);           // does not compile because ac is const
   VERIFY_EVALUATION_COUNT( call_ref_1(ab,ab), 0);
   VERIFY_EVALUATION_COUNT( call_ref_1(a.head(4),a.head(4)), 0);
   VERIFY_EVALUATION_COUNT( call_ref_1(abc,abc), 0);
   VERIFY_EVALUATION_COUNT( call_ref_1(A.col(3),A.col(3)), 0);
-//   call_ref_1(A.row(3));    // does not compile because innerstride!=1
+//   call_ref_1(A.row(3),A.row(3));    // does not compile because innerstride!=1
   VERIFY_EVALUATION_COUNT( call_ref_3(A.row(3),A.row(3).transpose()), 0);
   VERIFY_EVALUATION_COUNT( call_ref_4(A.row(3),A.row(3).transpose()), 0);
-//   call_ref_1(a+a);          // does not compile for obvious reason
+//   call_ref_1(a+a, a+a);          // does not compile for obvious reason
 
   MatrixXf tmp = A*A.col(1);
   VERIFY_EVALUATION_COUNT( call_ref_2(A*A.col(1), tmp), 1);     // evaluated into a temp
@@ -211,7 +211,7 @@ void call_ref()
   VERIFY_EVALUATION_COUNT( call_ref_5(a,a), 0);
   VERIFY_EVALUATION_COUNT( call_ref_5(a.head(3),a.head(3)), 0);
   VERIFY_EVALUATION_COUNT( call_ref_5(A,A), 0);
-//   call_ref_5(A.transpose());   // does not compile
+//   call_ref_5(A.transpose(),A.transpose());   // does not compile because storage order does not match
   VERIFY_EVALUATION_COUNT( call_ref_5(A.block(1,1,2,2),A.block(1,1,2,2)), 0);
   VERIFY_EVALUATION_COUNT( call_ref_5(b,b), 0);             // storage order do not match, but this is a degenerate case that should work
   VERIFY_EVALUATION_COUNT( call_ref_5(a.row(3),a.row(3)), 0);
diff --git a/test/sparse_basic.cpp b/test/sparse_basic.cpp
index 4c9b9111e..097959c84 100644
--- a/test/sparse_basic.cpp
+++ b/test/sparse_basic.cpp
@@ -18,6 +18,9 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
   
   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;
   enum { Flags = SparseMatrixType::Flags };
 
@@ -36,23 +39,22 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     std::vector<Vector2> nonzeroCoords;
     initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
 
-    if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
-      return;
-
     // test coeff and coeffRef
-    for (int i=0; i<(int)zeroCoords.size(); ++i)
+    for (std::size_t i=0; i<zeroCoords.size(); ++i)
     {
       VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
       if(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value)
-        VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 );
+        VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[i].x(),zeroCoords[i].y()) = 5 );
     }
     VERIFY_IS_APPROX(m, refMat);
 
-    m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
-    refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+    if(!nonzeroCoords.empty()) {
+      m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+      refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+    }
 
     VERIFY_IS_APPROX(m, refMat);
-      /*
+
       // test InnerIterators and Block expressions
       for (int t=0; t<10; ++t)
       {
@@ -61,23 +63,25 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
         int w = internal::random<int>(1,cols-j-1);
         int h = internal::random<int>(1,rows-i-1);
 
-    //     VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
+         VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
         for(int 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(int 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));
+            // FIXME col().coeff() not implemented yet
+//             VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
           }
         }
-    //     for(int 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(int 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));
-    //       }
-    //     }
+         for(int 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(int c=0; c<w; c++)
+           {
+             // FIXME row().coeff() not implemented yet
+//             VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
+           }
+         }
       }
 
       for(int c=0; c<cols; c++)
@@ -91,8 +95,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
         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 assertion
       VERIFY_RAISES_ASSERT( m.coeffRef(-1,1) = 0 );
       VERIFY_RAISES_ASSERT( m.coeffRef(0,m.cols()) = 0 );
@@ -165,11 +169,11 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
 
   // test innerVector()
   {
-    DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
-    SparseMatrixType m2(rows, rows);
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+    SparseMatrixType m2(rows, cols);
     initSparse<Scalar>(density, refMat2, m2);
-    Index j0 = internal::random<Index>(0,rows-1);
-    Index j1 = internal::random<Index>(0,rows-1);
+    Index j0 = internal::random<Index>(0,outer-1);
+    Index j1 = internal::random<Index>(0,outer-1);
     if(SparseMatrixType::IsRowMajor)
       VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.row(j0));
     else
@@ -180,42 +184,41 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     else
       VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
 
-    SparseMatrixType m3(rows,rows);
-    m3.reserve(VectorXi::Constant(rows,int(rows/2)));
-    for(Index j=0; j<rows; ++j)
-      for(Index k=0; k<j; ++k)
+    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) = k+1;
-    for(Index j=0; j<rows; ++j)
+    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<rows; ++j)
+    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);
+//     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, rows);
-    SparseMatrixType m2(rows, rows);
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+    SparseMatrixType m2(rows, cols);
     initSparse<Scalar>(density, refMat2, m2);
     if(internal::random<float>(0,1)>0.5) m2.makeCompressed();
-    
-    Index j0 = internal::random<Index>(0,rows-2);
-    Index j1 = internal::random<Index>(0,rows-2);
-    Index n0 = internal::random<Index>(1,rows-(std::max)(j0,j1));
+    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
@@ -239,22 +242,23 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     
     VERIFY_IS_APPROX(m2, refMat2);
   }
-  
+
   // test basic computations
   {
-    DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
-    DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
-    DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
-    DenseMatrix refM4 = DenseMatrix::Zero(rows, rows);
-    SparseMatrixType m1(rows, rows);
-    SparseMatrixType m2(rows, rows);
-    SparseMatrixType m3(rows, rows);
-    SparseMatrixType m4(rows, rows);
+    DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
+    DenseMatrix refM2 = DenseMatrix::Zero(rows, cols);
+    DenseMatrix refM3 = DenseMatrix::Zero(rows, cols);
+    DenseMatrix refM4 = DenseMatrix::Zero(rows, cols);
+    SparseMatrixType m1(rows, cols);
+    SparseMatrixType m2(rows, cols);
+    SparseMatrixType m3(rows, cols);
+    SparseMatrixType m4(rows, cols);
     initSparse<Scalar>(density, refM1, m1);
     initSparse<Scalar>(density, refM2, m2);
     initSparse<Scalar>(density, refM3, m3);
     initSparse<Scalar>(density, refM4, m4);
 
+    VERIFY_IS_APPROX(m1*s1, refM1*s1);
     VERIFY_IS_APPROX(m1+m2, refM1+refM2);
     VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3);
     VERIFY_IS_APPROX(m3.cwiseProduct(m1+m2), refM3.cwiseProduct(refM1+refM2));
@@ -269,7 +273,7 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     if(SparseMatrixType::IsRowMajor)
       VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.row(0).dot(refM2.row(0)));
     else
-      VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.col(0).dot(refM2.row(0)));
+      VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.col(0)), refM1.col(0).dot(refM2.col(0)));
     
     DenseVector rv = DenseVector::Random(m1.cols());
     DenseVector cv = DenseVector::Random(m1.rows());
@@ -296,25 +300,29 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
 
   // test transpose
   {
-    DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
-    SparseMatrixType m2(rows, rows);
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+    SparseMatrixType m2(rows, cols);
     initSparse<Scalar>(density, refMat2, m2);
     VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
     VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
 
     VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint());
+    
+    // check isApprox handles opposite storage order
+    typename Transpose<SparseMatrixType>::PlainObject m3(m2);
+    VERIFY(m2.isApprox(m3));
   }
 
   
   
   // test generic blocks
   {
-    DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
-    SparseMatrixType m2(rows, rows);
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+    SparseMatrixType m2(rows, cols);
     initSparse<Scalar>(density, refMat2, m2);
-    Index j0 = internal::random<Index>(0,rows-2);
-    Index j1 = internal::random<Index>(0,rows-2);
-    Index n0 = internal::random<Index>(1,rows-(std::max)(j0,j1));
+    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
@@ -341,8 +349,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
 
   // test prune
   {
-    SparseMatrixType m2(rows, rows);
-    DenseMatrix refM2(rows, rows);
+    SparseMatrixType m2(rows, cols);
+    DenseMatrix refM2(rows, cols);
     refM2.setZero();
     int countFalseNonZero = 0;
     int countTrueNonZero = 0;
@@ -403,8 +411,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
 
   // test triangularView
   {
-    DenseMatrix refMat2(rows, rows), refMat3(rows, rows);
-    SparseMatrixType m2(rows, rows), m3(rows, rows);
+    DenseMatrix refMat2(rows, cols), refMat3(rows, cols);
+    SparseMatrixType m2(rows, cols), m3(rows, cols);
     initSparse<Scalar>(density, refMat2, m2);
     refMat3 = refMat2.template triangularView<Lower>();
     m3 = m2.template triangularView<Lower>();
@@ -414,13 +422,16 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     m3 = m2.template triangularView<Upper>();
     VERIFY_IS_APPROX(m3, refMat3);
 
-    refMat3 = refMat2.template triangularView<UnitUpper>();
-    m3 = m2.template triangularView<UnitUpper>();
-    VERIFY_IS_APPROX(m3, refMat3);
+    if(inner>=outer) // FIXME this should be implemented for outer>inner as well
+    {
+      refMat3 = refMat2.template triangularView<UnitUpper>();
+      m3 = m2.template triangularView<UnitUpper>();
+      VERIFY_IS_APPROX(m3, refMat3);
 
-    refMat3 = refMat2.template triangularView<UnitLower>();
-    m3 = m2.template triangularView<UnitLower>();
-    VERIFY_IS_APPROX(m3, refMat3);
+      refMat3 = refMat2.template triangularView<UnitLower>();
+      m3 = m2.template triangularView<UnitLower>();
+      VERIFY_IS_APPROX(m3, refMat3);
+    }
 
     refMat3 = refMat2.template triangularView<StrictlyUpper>();
     m3 = m2.template triangularView<StrictlyUpper>();
@@ -440,6 +451,11 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     refMat3 = refMat2.template selfadjointView<Lower>();
     m3 = m2.template selfadjointView<Lower>();
     VERIFY_IS_APPROX(m3, refMat3);
+
+    // selfadjointView only works for square matrices:
+    SparseMatrixType m4(rows, rows+1);
+    VERIFY_RAISES_ASSERT(m4.template selfadjointView<Lower>());
+    VERIFY_RAISES_ASSERT(m4.template selfadjointView<Upper>());
   }
   
   // test sparseView
@@ -452,16 +468,23 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
 
   // test diagonal
   {
-    DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
-    SparseMatrixType m2(rows, rows);
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+    SparseMatrixType m2(rows, cols);
     initSparse<Scalar>(density, refMat2, m2);
     VERIFY_IS_APPROX(m2.diagonal(), refMat2.diagonal().eval());
+    VERIFY_IS_APPROX(const_cast<const SparseMatrixType&>(m2).diagonal(), refMat2.diagonal().eval());
+    
+    initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag);
+    m2.diagonal()      += refMat2.diagonal();
+    refMat2.diagonal() += refMat2.diagonal();
+    VERIFY_IS_APPROX(m2, refMat2);
   }
   
   // test conservative resize
   {
       std::vector< std::pair<Index,Index> > inc;
-      inc.push_back(std::pair<Index,Index>(-3,-2));
+      if(rows > 3 && cols > 2)
+        inc.push_back(std::pair<Index,Index>(-3,-2));
       inc.push_back(std::pair<Index,Index>(0,0));
       inc.push_back(std::pair<Index,Index>(3,2));
       inc.push_back(std::pair<Index,Index>(3,0));
@@ -502,19 +525,54 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
   }
 }
 
+
+template<typename SparseMatrixType>
+void big_sparse_triplet(typename SparseMatrixType::Index rows, typename SparseMatrixType::Index cols, double density) {
+typedef typename SparseMatrixType::Index Index;
+typedef typename SparseMatrixType::Scalar Scalar;
+typedef Triplet<Scalar,Index> TripletType;
+std::vector<TripletType> triplets;
+double nelements = density * rows*cols;
+VERIFY(nelements>=0 && nelements <  NumTraits<Index>::highest());
+Index ntriplets = Index(nelements);
+triplets.reserve(ntriplets);
+Scalar sum = Scalar(0);
+for(Index i=0;i<ntriplets;++i)
+{
+  Index r = internal::random<Index>(0,rows-1);
+  Index c = internal::random<Index>(0,cols-1);
+  Scalar v = internal::random<Scalar>();
+  triplets.push_back(TripletType(r,c,v));
+  sum += v;
+}
+SparseMatrixType m(rows,cols);
+m.setFromTriplets(triplets.begin(), triplets.end());
+VERIFY(m.nonZeros() <= ntriplets);
+VERIFY_IS_APPROX(sum, m.sum());
+}
+
+
 void test_sparse_basic()
 {
   for(int i = 0; i < g_repeat; i++) {
-    int s = Eigen::internal::random<int>(1,50);
-    EIGEN_UNUSED_VARIABLE(s);
+    int 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
+    }
+    EIGEN_UNUSED_VARIABLE(r+c);
+    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(1, 1)) ));
     CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(8, 8)) ));
-    CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(s, s)) ));
-    CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(s, s)) ));
-    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(s, s)) ));
-    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,long int>(s, s)) ));
-    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,long int>(s, s)) ));
+    CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(r, c)) ));
+    CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(r, c)) ));
+    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(r, c)) ));
+    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,long int>(r, c)) ));
+    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,long int>(r, c)) ));
     
-    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(s), short(s))) ));
-    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(s), short(s))) ));
+    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) ));
+    CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) ));
   }
+
+  // Regression test for bug 900: (manually insert higher values here, if you have enough RAM):
+  CALL_SUBTEST_3((big_sparse_triplet<SparseMatrix<float, RowMajor, int> >(10000, 10000, 0.125)));
+  CALL_SUBTEST_4((big_sparse_triplet<SparseMatrix<double, ColMajor, long int> >(10000, 10000, 0.125)));
 }
diff --git a/test/sparse_product.cpp b/test/sparse_product.cpp
index 0f52164c8..366e27274 100644
--- a/test/sparse_product.cpp
+++ b/test/sparse_product.cpp
@@ -19,7 +19,7 @@ template<typename SparseMatrixType> void sparse_product()
   typedef typename SparseMatrixType::Scalar Scalar;
   enum { Flags = SparseMatrixType::Flags };
 
-  double density = (std::max)(8./(rows*cols), 0.1);
+  double density = (std::max)(8./(rows*cols), 0.2);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
   typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
@@ -77,17 +77,27 @@ template<typename SparseMatrixType> void sparse_product()
     m4 = m2; refMat4 = refMat2;
     VERIFY_IS_APPROX(m4=m4*m3, refMat4=refMat4*refMat3);
 
-    // sparse * dense
+    // sparse * dense matrix
     VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3);
     VERIFY_IS_APPROX(dm4=m2*refMat3t.transpose(), refMat4=refMat2*refMat3t.transpose());
     VERIFY_IS_APPROX(dm4=m2t.transpose()*refMat3, refMat4=refMat2t.transpose()*refMat3);
     VERIFY_IS_APPROX(dm4=m2t.transpose()*refMat3t.transpose(), refMat4=refMat2t.transpose()*refMat3t.transpose());
 
+    VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3);
+    VERIFY_IS_APPROX(dm4=dm4+m2*refMat3, refMat4=refMat4+refMat2*refMat3);
     VERIFY_IS_APPROX(dm4=m2*(refMat3+refMat3), refMat4=refMat2*(refMat3+refMat3));
     VERIFY_IS_APPROX(dm4=m2t.transpose()*(refMat3+refMat5)*0.5, refMat4=refMat2t.transpose()*(refMat3+refMat5)*0.5);
+    
+    // sparse * dense vector
+    VERIFY_IS_APPROX(dm4.col(0)=m2*refMat3.col(0), refMat4.col(0)=refMat2*refMat3.col(0));
+    VERIFY_IS_APPROX(dm4.col(0)=m2*refMat3t.transpose().col(0), refMat4.col(0)=refMat2*refMat3t.transpose().col(0));
+    VERIFY_IS_APPROX(dm4.col(0)=m2t.transpose()*refMat3.col(0), refMat4.col(0)=refMat2t.transpose()*refMat3.col(0));
+    VERIFY_IS_APPROX(dm4.col(0)=m2t.transpose()*refMat3t.transpose().col(0), refMat4.col(0)=refMat2t.transpose()*refMat3t.transpose().col(0));
 
     // dense * sparse
     VERIFY_IS_APPROX(dm4=refMat2*m3, refMat4=refMat2*refMat3);
+    VERIFY_IS_APPROX(dm4=dm4+refMat2*m3, refMat4=refMat4+refMat2*refMat3);
+    VERIFY_IS_APPROX(dm4+=refMat2*m3, refMat4+=refMat2*refMat3);
     VERIFY_IS_APPROX(dm4=refMat2*m3t.transpose(), refMat4=refMat2*refMat3t.transpose());
     VERIFY_IS_APPROX(dm4=refMat2t.transpose()*m3, refMat4=refMat2t.transpose()*refMat3);
     VERIFY_IS_APPROX(dm4=refMat2t.transpose()*m3t.transpose(), refMat4=refMat2t.transpose()*refMat3t.transpose());
@@ -99,7 +109,7 @@ template<typename SparseMatrixType> void sparse_product()
       Index c1 = internal::random<Index>(0,cols-1);
       Index r1 = internal::random<Index>(0,depth-1);
       DenseMatrix dm5  = DenseMatrix::Random(depth, cols);
-      
+
       VERIFY_IS_APPROX( m4=m2.col(c)*dm5.col(c1).transpose(), refMat4=refMat2.col(c)*dm5.col(c1).transpose());
       VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
       VERIFY_IS_APPROX( m4=m2.middleCols(c,1)*dm5.col(c1).transpose(), refMat4=refMat2.col(c)*dm5.col(c1).transpose());
@@ -143,11 +153,11 @@ template<typename SparseMatrixType> void sparse_product()
     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(cv1=m3*cv0, dcv1=refMat3*dcv0);    
     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(cv1=rv0*m3, dcv1=drv0*refMat3);
     VERIFY_IS_APPROX(rv1=m3*cv0, drv1=refMat3*dcv0);
   }
   
@@ -184,7 +194,7 @@ template<typename SparseMatrixType> void sparse_product()
     VERIFY_IS_APPROX(d3=d1*m2.transpose(), refM3=d1*refM2.transpose());
   }
 
-  // test self adjoint products
+  // test self-adjoint and traingular-view products
   {
     DenseMatrix b = DenseMatrix::Random(rows, rows);
     DenseMatrix x = DenseMatrix::Random(rows, rows);
@@ -192,9 +202,12 @@ template<typename SparseMatrixType> void sparse_product()
     DenseMatrix refUp = DenseMatrix::Zero(rows, rows);
     DenseMatrix refLo = DenseMatrix::Zero(rows, rows);
     DenseMatrix refS = DenseMatrix::Zero(rows, rows);
+    DenseMatrix refA = DenseMatrix::Zero(rows, rows);
     SparseMatrixType mUp(rows, rows);
     SparseMatrixType mLo(rows, rows);
     SparseMatrixType mS(rows, rows);
+    SparseMatrixType mA(rows, rows);
+    initSparse<Scalar>(density, refA, mA);
     do {
       initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular);
     } while (refUp.isZero());
@@ -214,19 +227,30 @@ template<typename SparseMatrixType> void sparse_product()
     VERIFY_IS_APPROX(mS, refS);
     VERIFY_IS_APPROX(x=mS*b, refX=refS*b);
 
+    // sparse selfadjointView with dense matrices
     VERIFY_IS_APPROX(x=mUp.template selfadjointView<Upper>()*b, refX=refS*b);
     VERIFY_IS_APPROX(x=mLo.template selfadjointView<Lower>()*b, refX=refS*b);
     VERIFY_IS_APPROX(x=mS.template selfadjointView<Upper|Lower>()*b, refX=refS*b);
     
-    // sparse selfadjointView * sparse 
+    // sparse selfadjointView with sparse matrices
     SparseMatrixType mSres(rows,rows);
     VERIFY_IS_APPROX(mSres = mLo.template selfadjointView<Lower>()*mS,
                      refX = refLo.template selfadjointView<Lower>()*refS);
-    // sparse * sparse selfadjointview
     VERIFY_IS_APPROX(mSres = mS * mLo.template selfadjointView<Lower>(),
                      refX = refS * refLo.template selfadjointView<Lower>());
+    
+    // sparse triangularView with dense matrices
+    VERIFY_IS_APPROX(x=mA.template triangularView<Upper>()*b, refX=refA.template triangularView<Upper>()*b);
+    VERIFY_IS_APPROX(x=mA.template triangularView<Lower>()*b, refX=refA.template triangularView<Lower>()*b);
+    VERIFY_IS_APPROX(x=b*mA.template triangularView<Upper>(), refX=b*refA.template triangularView<Upper>());
+    VERIFY_IS_APPROX(x=b*mA.template triangularView<Lower>(), refX=b*refA.template triangularView<Lower>());
+    
+    // sparse triangularView with sparse matrices
+    VERIFY_IS_APPROX(mSres = mA.template triangularView<Lower>()*mS,   refX = refA.template triangularView<Lower>()*refS);
+    VERIFY_IS_APPROX(mSres = mS * mA.template triangularView<Lower>(), refX = refS * refA.template triangularView<Lower>());
+    VERIFY_IS_APPROX(mSres = mA.template triangularView<Upper>()*mS,   refX = refA.template triangularView<Upper>()*refS);
+    VERIFY_IS_APPROX(mSres = mS * mA.template triangularView<Upper>(), refX = refS * refA.template triangularView<Upper>());
   }
-  
 }
 
 // New test for Bug in SparseTimeDenseProduct
diff --git a/test/sparse_solver.h b/test/sparse_solver.h
index d84aff070..ee350d561 100644
--- a/test/sparse_solver.h
+++ b/test/sparse_solver.h
@@ -15,6 +15,7 @@ void check_sparse_solving(Solver& solver, const typename Solver::MatrixType& A,
 {
   typedef typename Solver::MatrixType Mat;
   typedef typename Mat::Scalar Scalar;
+  typedef typename Mat::Index Index;
 
   DenseRhs refX = dA.lu().solve(db);
   {
@@ -35,8 +36,8 @@ void check_sparse_solving(Solver& solver, const typename Solver::MatrixType& A,
       return;
     }
     VERIFY(oldb.isApprox(b) && "sparse solver testing: the rhs should not be modified!");
-
     VERIFY(x.isApprox(refX,test_precision<Scalar>()));
+    
     x.setZero();
     // test the analyze/factorize API
     solver.analyzePattern(A);
@@ -54,8 +55,31 @@ void check_sparse_solving(Solver& solver, const typename Solver::MatrixType& A,
       return;
     }
     VERIFY(oldb.isApprox(b) && "sparse solver testing: the rhs should not be modified!");
-
     VERIFY(x.isApprox(refX,test_precision<Scalar>()));
+    
+    
+    x.setZero();
+    // test with Map
+    MappedSparseMatrix<Scalar,Mat::Options,Index> Am(A.rows(), A.cols(), A.nonZeros(), const_cast<Index*>(A.outerIndexPtr()), const_cast<Index*>(A.innerIndexPtr()), const_cast<Scalar*>(A.valuePtr()));
+    solver.compute(Am);
+    if (solver.info() != Success)
+    {
+      std::cerr << "sparse solver testing: factorization failed (check_sparse_solving)\n";
+      exit(0);
+      return;
+    }
+    DenseRhs dx(refX);
+    dx.setZero();
+    Map<DenseRhs> xm(dx.data(), dx.rows(), dx.cols());
+    Map<const DenseRhs> bm(db.data(), db.rows(), db.cols());
+    xm = solver.solve(bm);
+    if (solver.info() != Success)
+    {
+      std::cerr << "sparse solver testing: solving failed\n";
+      return;
+    }
+    VERIFY(oldb.isApprox(bm) && "sparse solver testing: the rhs should not be modified!");
+    VERIFY(xm.isApprox(refX,test_precision<Scalar>()));
   }
   
   // test dense Block as the result and rhs:
@@ -67,6 +91,15 @@ void check_sparse_solving(Solver& solver, const typename Solver::MatrixType& A,
     VERIFY(oldb.isApprox(db) && "sparse solver testing: the rhs should not be modified!");
     VERIFY(x.isApprox(refX,test_precision<Scalar>()));
   }
+  
+  // test uncompressed inputs
+  {
+    Mat A2 = A;
+    A2.reserve((ArrayXf::Random(A.outerSize())+2).template cast<typename Mat::Index>().eval());
+    solver.compute(A2);
+    Rhs x = solver.solve(b);
+    VERIFY(x.isApprox(refX,test_precision<Scalar>()));
+  }
 }
 
 template<typename Solver, typename Rhs>
@@ -124,7 +157,23 @@ void check_sparse_determinant(Solver& solver, const typename Solver::MatrixType&
   Scalar refDet = dA.determinant();
   VERIFY_IS_APPROX(refDet,solver.determinant());
 }
+template<typename Solver, typename DenseMat>
+void check_sparse_abs_determinant(Solver& solver, const typename Solver::MatrixType& A, const DenseMat& dA)
+{
+  using std::abs;
+  typedef typename Solver::MatrixType Mat;
+  typedef typename Mat::Scalar Scalar;
+  
+  solver.compute(A);
+  if (solver.info() != Success)
+  {
+    std::cerr << "sparse solver testing: factorization failed (check_sparse_abs_determinant)\n";
+    return;
+  }
 
+  Scalar refDet = abs(dA.determinant());
+  VERIFY_IS_APPROX(refDet,solver.absDeterminant());
+}
 
 template<typename Solver, typename DenseMat>
 int generate_sparse_spd_problem(Solver& , typename Solver::MatrixType& A, typename Solver::MatrixType& halfA, DenseMat& dA, int maxSize = 300)
@@ -324,3 +373,20 @@ template<typename Solver> void check_sparse_square_determinant(Solver& solver)
     check_sparse_determinant(solver, A, dA);
   }
 }
+
+template<typename Solver> void check_sparse_square_abs_determinant(Solver& solver)
+{
+  typedef typename Solver::MatrixType Mat;
+  typedef typename Mat::Scalar Scalar;
+  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
+
+  // generate the problem
+  Mat A;
+  DenseMatrix dA;
+  generate_sparse_square_problem(solver, A, dA, 30);
+  A.makeCompressed();
+  for (int i = 0; i < g_repeat; i++) {
+    check_sparse_abs_determinant(solver, A, dA);
+  }
+}
+
diff --git a/test/sparse_vector.cpp b/test/sparse_vector.cpp
index 0c9476803..5dc421976 100644
--- a/test/sparse_vector.cpp
+++ b/test/sparse_vector.cpp
@@ -23,8 +23,8 @@ template<typename Scalar,typename Index> void sparse_vector(int rows, int cols)
   SparseVectorType v1(rows), v2(rows), v3(rows);
   DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
   DenseVector refV1 = DenseVector::Random(rows),
-    refV2 = DenseVector::Random(rows),
-    refV3 = DenseVector::Random(rows);
+              refV2 = DenseVector::Random(rows),
+              refV3 = DenseVector::Random(rows);
 
   std::vector<int> zerocoords, nonzerocoords;
   initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords);
@@ -52,6 +52,20 @@ template<typename Scalar,typename Index> void sparse_vector(int rows, int cols)
     }
   }
   VERIFY_IS_APPROX(v1, refV1);
+  
+  // test coeffRef with reallocation
+  {
+    SparseVectorType v1(rows);
+    DenseVector v2 = DenseVector::Zero(rows);
+    for(int k=0; k<rows; ++k)
+    {
+      int i = internal::random<int>(0,rows-1);
+      Scalar v = internal::random<Scalar>();
+      v1.coeffRef(i) += v;
+      v2.coeffRef(i) += v;
+    }
+    VERIFY_IS_APPROX(v1,v2);
+  }
 
   v1.coeffRef(nonzerocoords[0]) = Scalar(5);
   refV1.coeffRef(nonzerocoords[0]) = Scalar(5);
@@ -71,6 +85,7 @@ template<typename Scalar,typename Index> void sparse_vector(int rows, int cols)
   VERIFY_IS_APPROX(v1.dot(v2), refV1.dot(refV2));
   VERIFY_IS_APPROX(v1.dot(refV2), refV1.dot(refV2));
 
+  VERIFY_IS_APPROX(m1*v2, refM1*refV2);
   VERIFY_IS_APPROX(v1.dot(m1*v2), refV1.dot(refM1*refV2));
   int i = internal::random<int>(0,rows-1);
   VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i)));
diff --git a/test/sparselu.cpp b/test/sparselu.cpp
index 37980defc..52371cb12 100644
--- a/test/sparselu.cpp
+++ b/test/sparselu.cpp
@@ -44,6 +44,9 @@ template<typename T> void test_sparselu_T()
   check_sparse_square_solving(sparselu_colamd); 
   check_sparse_square_solving(sparselu_amd);
   check_sparse_square_solving(sparselu_natural);
+  
+  check_sparse_square_abs_determinant(sparselu_colamd);
+  check_sparse_square_abs_determinant(sparselu_amd);
 }
 
 void test_sparselu()
diff --git a/test/stable_norm.cpp b/test/stable_norm.cpp
index f76919af4..650f62a8a 100644
--- a/test/stable_norm.cpp
+++ b/test/stable_norm.cpp
@@ -9,26 +9,6 @@
 
 #include "main.h"
 
-template<typename T> bool isNotNaN(const T& x)
-{
-  return x==x;
-}
-
-template<typename T> bool isNaN(const T& x)
-{
-  return x!=x;
-}
-
-template<typename T> bool isInf(const T& x)
-{
-  return x > NumTraits<T>::highest();
-}
-
-template<typename T> bool isMinusInf(const T& x)
-{
-  return x < NumTraits<T>::lowest();
-}
-
 // workaround aggressive optimization in ICC
 template<typename T> EIGEN_DONT_INLINE  T sub(T a, T b) { return a - b; }
 
@@ -130,7 +110,7 @@ template<typename MatrixType> void stable_norm(const MatrixType& m)
   // NaN
   {
     v = vrand;
-    v(i,j) = RealScalar(0)/RealScalar(0);
+    v(i,j) = std::numeric_limits<RealScalar>::quiet_NaN();
     VERIFY(!isFinite(v.squaredNorm()));   VERIFY(isNaN(v.squaredNorm()));
     VERIFY(!isFinite(v.norm()));          VERIFY(isNaN(v.norm()));
     VERIFY(!isFinite(v.stableNorm()));    VERIFY(isNaN(v.stableNorm()));
@@ -141,7 +121,7 @@ template<typename MatrixType> void stable_norm(const MatrixType& m)
   // +inf
   {
     v = vrand;
-    v(i,j) = RealScalar(1)/RealScalar(0);
+    v(i,j) = std::numeric_limits<RealScalar>::infinity();
     VERIFY(!isFinite(v.squaredNorm()));   VERIFY(isInf(v.squaredNorm()));
     VERIFY(!isFinite(v.norm()));          VERIFY(isInf(v.norm()));
     VERIFY(!isFinite(v.stableNorm()));    VERIFY(isInf(v.stableNorm()));
@@ -152,7 +132,7 @@ template<typename MatrixType> void stable_norm(const MatrixType& m)
   // -inf
   {
     v = vrand;
-    v(i,j) = RealScalar(-1)/RealScalar(0);
+    v(i,j) = -std::numeric_limits<RealScalar>::infinity();
     VERIFY(!isFinite(v.squaredNorm()));   VERIFY(isInf(v.squaredNorm()));
     VERIFY(!isFinite(v.norm()));          VERIFY(isInf(v.norm()));
     VERIFY(!isFinite(v.stableNorm()));    VERIFY(isInf(v.stableNorm()));
@@ -165,8 +145,8 @@ template<typename MatrixType> void stable_norm(const MatrixType& m)
     Index i2 = internal::random<Index>(0,rows-1);
     Index j2 = internal::random<Index>(0,cols-1);
     v = vrand;
-    v(i,j) = RealScalar(-1)/RealScalar(0);
-    v(i2,j2) = RealScalar(0)/RealScalar(0);
+    v(i,j) = -std::numeric_limits<RealScalar>::infinity();
+    v(i2,j2) = std::numeric_limits<RealScalar>::quiet_NaN();
     VERIFY(!isFinite(v.squaredNorm()));   VERIFY(isNaN(v.squaredNorm()));
     VERIFY(!isFinite(v.norm()));          VERIFY(isNaN(v.norm()));
     VERIFY(!isFinite(v.stableNorm()));    VERIFY(isNaN(v.stableNorm()));
diff --git a/test/svd_common.h b/test/svd_common.h
new file mode 100644
index 000000000..4c172cf9d
--- /dev/null
+++ b/test/svd_common.h
@@ -0,0 +1,493 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// 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/.
+
+#ifndef SVD_DEFAULT
+#error a macro SVD_DEFAULT(MatrixType) must be defined prior to including svd_common.h
+#endif
+
+#ifndef SVD_FOR_MIN_NORM
+#error a macro SVD_FOR_MIN_NORM(MatrixType) must be defined prior to including svd_common.h
+#endif
+
+// Check that the matrix m is properly reconstructed and that the U and V factors are unitary
+// The SVD must have already been computed.
+template<typename SvdType, typename MatrixType>
+void svd_check_full(const MatrixType& m, const SvdType& svd)
+{
+  typedef typename MatrixType::Index Index;
+  Index rows = m.rows();
+  Index cols = m.cols();
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime
+  };
+
+  typedef typename MatrixType::Scalar Scalar;
+  typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime> MatrixUType;
+  typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime> MatrixVType;
+
+  MatrixType sigma = MatrixType::Zero(rows,cols);
+  sigma.diagonal() = svd.singularValues().template cast<Scalar>();
+  MatrixUType u = svd.matrixU();
+  MatrixVType v = svd.matrixV();
+  VERIFY_IS_APPROX(m, u * sigma * v.adjoint());
+  VERIFY_IS_UNITARY(u);
+  VERIFY_IS_UNITARY(v);
+}
+
+// Compare partial SVD defined by computationOptions to a full SVD referenceSvd
+template<typename SvdType, typename MatrixType>
+void svd_compare_to_full(const MatrixType& m,
+                         unsigned int computationOptions,
+                         const SvdType& referenceSvd)
+{
+  typedef typename MatrixType::Index Index;
+  Index rows = m.rows();
+  Index cols = m.cols();
+  Index diagSize = (std::min)(rows, cols);
+
+  SvdType svd(m, computationOptions);
+
+  VERIFY_IS_APPROX(svd.singularValues(), referenceSvd.singularValues());
+  if(computationOptions & ComputeFullU)  VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU());
+  if(computationOptions & ComputeThinU)  VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU().leftCols(diagSize));
+  if(computationOptions & ComputeFullV)  VERIFY_IS_APPROX(svd.matrixV(), referenceSvd.matrixV());
+  if(computationOptions & ComputeThinV)  VERIFY_IS_APPROX(svd.matrixV(), referenceSvd.matrixV().leftCols(diagSize));
+}
+
+//
+template<typename SvdType, typename MatrixType>
+void svd_least_square(const MatrixType& m, unsigned int computationOptions)
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::Index Index;
+  Index rows = m.rows();
+  Index cols = m.cols();
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, RowsAtCompileTime, Dynamic> RhsType;
+  typedef Matrix<Scalar, ColsAtCompileTime, Dynamic> SolutionType;
+
+  RhsType rhs = RhsType::Random(rows, internal::random<Index>(1, cols));
+  SvdType svd(m, computationOptions);
+
+       if(internal::is_same<RealScalar,double>::value) svd.setThreshold(1e-8);
+  else if(internal::is_same<RealScalar,float>::value)  svd.setThreshold(1e-4);
+  
+  SolutionType x = svd.solve(rhs);
+  
+  // evaluate normal equation which works also for least-squares solutions
+  if(internal::is_same<RealScalar,double>::value || svd.rank()==m.diagonal().size())
+  {
+    // This test is not stable with single precision.
+    // This is probably because squaring m signicantly affects the precision.
+    VERIFY_IS_APPROX(m.adjoint()*(m*x),m.adjoint()*rhs);
+  }
+  
+  RealScalar residual = (m*x-rhs).norm();
+  // Check that there is no significantly better solution in the neighborhood of x
+  if(!test_isMuchSmallerThan(residual,rhs.norm()))
+  {
+    // ^^^ If the residual is very small, then we have an exact solution, so we are already good.
+    for(Index k=0;k<x.rows();++k)
+    {
+      SolutionType y(x);
+      y.row(k) = (1.+2*NumTraits<RealScalar>::epsilon())*x.row(k);
+      RealScalar residual_y = (m*y-rhs).norm();
+      VERIFY( test_isApprox(residual_y,residual) || residual < residual_y );
+      
+      y.row(k) = (1.-2*NumTraits<RealScalar>::epsilon())*x.row(k);
+      residual_y = (m*y-rhs).norm();
+      VERIFY( test_isApprox(residual_y,residual) || residual < residual_y );
+    }
+  }
+}
+
+// check minimal norm solutions, the inoput matrix m is only used to recover problem size
+template<typename MatrixType>
+void svd_min_norm(const MatrixType& m, unsigned int computationOptions)
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::Index Index;
+  Index cols = m.cols();
+
+  enum {
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, ColsAtCompileTime, Dynamic> SolutionType;
+
+  // generate a full-rank m x n problem with m<n
+  enum {
+    RankAtCompileTime2 = ColsAtCompileTime==Dynamic ? Dynamic : (ColsAtCompileTime)/2+1,
+    RowsAtCompileTime3 = ColsAtCompileTime==Dynamic ? Dynamic : ColsAtCompileTime+1
+  };
+  typedef Matrix<Scalar, RankAtCompileTime2, ColsAtCompileTime> MatrixType2;
+  typedef Matrix<Scalar, RankAtCompileTime2, 1> RhsType2;
+  typedef Matrix<Scalar, ColsAtCompileTime, RankAtCompileTime2> MatrixType2T;
+  Index rank = RankAtCompileTime2==Dynamic ? internal::random<Index>(1,cols) : Index(RankAtCompileTime2);
+  MatrixType2 m2(rank,cols);
+  int guard = 0;
+  do {
+    m2.setRandom();
+  } while(SVD_FOR_MIN_NORM(MatrixType2)(m2).setThreshold(test_precision<Scalar>()).rank()!=rank && (++guard)<10);
+  VERIFY(guard<10);
+
+  RhsType2 rhs2 = RhsType2::Random(rank);
+  // use QR to find a reference minimal norm solution
+  HouseholderQR<MatrixType2T> qr(m2.adjoint());
+  Matrix<Scalar,Dynamic,1> tmp = qr.matrixQR().topLeftCorner(rank,rank).template triangularView<Upper>().adjoint().solve(rhs2);
+  tmp.conservativeResize(cols);
+  tmp.tail(cols-rank).setZero();
+  SolutionType x21 = qr.householderQ() * tmp;
+  // now check with SVD
+  SVD_FOR_MIN_NORM(MatrixType2) svd2(m2, computationOptions);
+  SolutionType x22 = svd2.solve(rhs2);
+  VERIFY_IS_APPROX(m2*x21, rhs2);
+  VERIFY_IS_APPROX(m2*x22, rhs2);
+  VERIFY_IS_APPROX(x21, x22);
+
+  // Now check with a rank deficient matrix
+  typedef Matrix<Scalar, RowsAtCompileTime3, ColsAtCompileTime> MatrixType3;
+  typedef Matrix<Scalar, RowsAtCompileTime3, 1> RhsType3;
+  Index rows3 = RowsAtCompileTime3==Dynamic ? internal::random<Index>(rank+1,2*cols) : Index(RowsAtCompileTime3);
+  Matrix<Scalar,RowsAtCompileTime3,Dynamic> C = Matrix<Scalar,RowsAtCompileTime3,Dynamic>::Random(rows3,rank);
+  MatrixType3 m3 = C * m2;
+  RhsType3 rhs3 = C * rhs2;
+  SVD_FOR_MIN_NORM(MatrixType3) svd3(m3, computationOptions);
+  SolutionType x3 = svd3.solve(rhs3);
+  VERIFY_IS_APPROX(m3*x3, rhs3);
+  VERIFY_IS_APPROX(m3*x21, rhs3);
+  VERIFY_IS_APPROX(m2*x3, rhs2);
+  VERIFY_IS_APPROX(x21, x3);
+}
+
+// Check full, compare_to_full, least_square, and min_norm for all possible compute-options
+template<typename SvdType, typename MatrixType>
+void svd_test_all_computation_options(const MatrixType& m, bool full_only)
+{
+//   if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols())
+//     return;
+  SvdType fullSvd(m, ComputeFullU|ComputeFullV);
+  CALL_SUBTEST(( svd_check_full(m, fullSvd) ));
+  CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeFullU | ComputeFullV) ));
+  CALL_SUBTEST(( svd_min_norm(m, ComputeFullU | ComputeFullV) ));
+  
+  #if defined __INTEL_COMPILER
+  // remark #111: statement is unreachable
+  #pragma warning disable 111
+  #endif
+  if(full_only)
+    return;
+
+  CALL_SUBTEST(( svd_compare_to_full(m, ComputeFullU, fullSvd) ));
+  CALL_SUBTEST(( svd_compare_to_full(m, ComputeFullV, fullSvd) ));
+  CALL_SUBTEST(( svd_compare_to_full(m, 0, fullSvd) ));
+
+  if (MatrixType::ColsAtCompileTime == Dynamic) {
+    // thin U/V are only available with dynamic number of columns
+    CALL_SUBTEST(( svd_compare_to_full(m, ComputeFullU|ComputeThinV, fullSvd) ));
+    CALL_SUBTEST(( svd_compare_to_full(m,              ComputeThinV, fullSvd) ));
+    CALL_SUBTEST(( svd_compare_to_full(m, ComputeThinU|ComputeFullV, fullSvd) ));
+    CALL_SUBTEST(( svd_compare_to_full(m, ComputeThinU             , fullSvd) ));
+    CALL_SUBTEST(( svd_compare_to_full(m, ComputeThinU|ComputeThinV, fullSvd) ));
+    
+    CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeFullU | ComputeThinV) ));
+    CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeThinU | ComputeFullV) ));
+    CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeThinU | ComputeThinV) ));
+
+    CALL_SUBTEST(( svd_min_norm(m, ComputeFullU | ComputeThinV) ));
+    CALL_SUBTEST(( svd_min_norm(m, ComputeThinU | ComputeFullV) ));
+    CALL_SUBTEST(( svd_min_norm(m, ComputeThinU | ComputeThinV) ));
+
+    // test reconstruction
+    typedef typename MatrixType::Index Index;
+    Index diagSize = (std::min)(m.rows(), m.cols());
+    SvdType svd(m, ComputeThinU | ComputeThinV);
+    VERIFY_IS_APPROX(m, svd.matrixU().leftCols(diagSize) * svd.singularValues().asDiagonal() * svd.matrixV().leftCols(diagSize).adjoint());
+  }
+}
+
+template<typename MatrixType>
+void svd_fill_random(MatrixType &m)
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::Index Index;
+  Index diagSize = (std::min)(m.rows(), m.cols());
+  RealScalar s = std::numeric_limits<RealScalar>::max_exponent10/4;
+  s = internal::random<RealScalar>(1,s);
+  Matrix<RealScalar,Dynamic,1> d =  Matrix<RealScalar,Dynamic,1>::Random(diagSize);
+  for(Index k=0; k<diagSize; ++k)
+    d(k) = d(k)*std::pow(RealScalar(10),internal::random<RealScalar>(-s,s));
+
+  bool dup     = internal::random<int>(0,10) < 3;
+  bool unit_uv = internal::random<int>(0,10) < (dup?7:3); // if we duplicate some diagonal entries, then increase the chance to preserve them using unitary U and V factors
+  
+  // duplicate some singular values
+  if(dup)
+  {
+    Index n = internal::random<Index>(0,d.size()-1);
+    for(Index i=0; i<n; ++i)
+      d(internal::random<Index>(0,d.size()-1)) = d(internal::random<Index>(0,d.size()-1));
+  }
+  
+  Matrix<Scalar,Dynamic,Dynamic> U(m.rows(),diagSize);
+  Matrix<Scalar,Dynamic,Dynamic> VT(diagSize,m.cols());
+  if(unit_uv)
+  {
+    // in very rare cases let's try with a pure diagonal matrix
+    if(internal::random<int>(0,10) < 1)
+    {
+      U.setIdentity();
+      VT.setIdentity();
+    }
+    else
+    {
+      createRandomPIMatrixOfRank(diagSize,U.rows(), U.cols(), U);
+      createRandomPIMatrixOfRank(diagSize,VT.rows(), VT.cols(), VT);
+    }
+  }
+  else
+  {
+    U.setRandom();
+    VT.setRandom();
+  }
+  
+  m = U * d.asDiagonal() * VT;
+  
+  // (partly) cancel some coeffs
+  if(!(dup && unit_uv))
+  {
+    Matrix<Scalar,Dynamic,1> samples(7);
+    samples << 0, 5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324, -1./NumTraits<RealScalar>::highest(), 1./NumTraits<RealScalar>::highest();
+    Index n = internal::random<Index>(0,m.size()-1);
+    for(Index i=0; i<n; ++i)
+      m(internal::random<Index>(0,m.rows()-1), internal::random<Index>(0,m.cols()-1)) = samples(internal::random<Index>(0,6));
+  }
+}
+
+
+// work around stupid msvc error when constructing at compile time an expression that involves
+// a division by zero, even if the numeric type has floating point
+template<typename Scalar>
+EIGEN_DONT_INLINE Scalar zero() { return Scalar(0); }
+
+// workaround aggressive optimization in ICC
+template<typename T> EIGEN_DONT_INLINE  T sub(T a, T b) { return a - b; }
+
+// all this function does is verify we don't iterate infinitely on nan/inf values
+template<typename SvdType, typename MatrixType>
+void svd_inf_nan()
+{
+  SvdType svd;
+  typedef typename MatrixType::Scalar Scalar;
+  Scalar some_inf = Scalar(1) / zero<Scalar>();
+  VERIFY(sub(some_inf, some_inf) != sub(some_inf, some_inf));
+  svd.compute(MatrixType::Constant(10,10,some_inf), ComputeFullU | ComputeFullV);
+
+  Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
+  VERIFY(nan != nan);
+  svd.compute(MatrixType::Constant(10,10,nan), ComputeFullU | ComputeFullV);
+
+  MatrixType m = MatrixType::Zero(10,10);
+  m(internal::random<int>(0,9), internal::random<int>(0,9)) = some_inf;
+  svd.compute(m, ComputeFullU | ComputeFullV);
+
+  m = MatrixType::Zero(10,10);
+  m(internal::random<int>(0,9), internal::random<int>(0,9)) = nan;
+  svd.compute(m, ComputeFullU | ComputeFullV);
+  
+  // regression test for bug 791
+  m.resize(3,3);
+  m << 0,    2*NumTraits<Scalar>::epsilon(),  0.5,
+       0,   -0.5,                             0,
+       nan,  0,                               0;
+  svd.compute(m, ComputeFullU | ComputeFullV);
+  
+  m.resize(4,4);
+  m <<  1, 0, 0, 0,
+        0, 3, 1, 2e-308,
+        1, 0, 1, nan,
+        0, nan, nan, 0;
+  svd.compute(m, ComputeFullU | ComputeFullV);
+}
+
+// Regression test for bug 286: JacobiSVD loops indefinitely with some
+// matrices containing denormal numbers.
+void svd_underoverflow()
+{
+#if defined __INTEL_COMPILER
+// shut up warning #239: floating point underflow
+#pragma warning push
+#pragma warning disable 239
+#endif
+  Matrix2d M;
+  M << -7.90884e-313, -4.94e-324,
+                 0, 5.60844e-313;
+  SVD_DEFAULT(Matrix2d) svd;
+  svd.compute(M,ComputeFullU|ComputeFullV);
+  CALL_SUBTEST( svd_check_full(M,svd) );
+  
+  // Check all 2x2 matrices made with the following coefficients:
+  VectorXd value_set(9);
+  value_set << 0, 1, -1, 5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324, -4.94e-223, 4.94e-223;
+  Array4i id(0,0,0,0);
+  int k = 0;
+  do
+  {
+    M << value_set(id(0)), value_set(id(1)), value_set(id(2)), value_set(id(3));
+    svd.compute(M,ComputeFullU|ComputeFullV);
+    CALL_SUBTEST( svd_check_full(M,svd) );
+    
+    id(k)++;
+    if(id(k)>=value_set.size())
+    {
+      while(k<3 && id(k)>=value_set.size()) id(++k)++;
+      id.head(k).setZero();
+      k=0;
+    }
+    
+  } while((id<int(value_set.size())).all());
+  
+#if defined __INTEL_COMPILER
+#pragma warning pop
+#endif
+  
+  // Check for overflow:
+  Matrix3d M3;
+  M3 << 4.4331978442502944e+307, -5.8585363752028680e+307,  6.4527017443412964e+307,
+        3.7841695601406358e+307,  2.4331702789740617e+306, -3.5235707140272905e+307,
+       -8.7190887618028355e+307, -7.3453213709232193e+307, -2.4367363684472105e+307;
+
+  SVD_DEFAULT(Matrix3d) svd3;
+  svd3.compute(M3,ComputeFullU|ComputeFullV); // just check we don't loop indefinitely
+  CALL_SUBTEST( svd_check_full(M3,svd3) );
+}
+
+// void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
+
+template<typename MatrixType>
+void svd_all_trivial_2x2( void (*cb)(const MatrixType&,bool) )
+{
+  MatrixType M;
+  VectorXd value_set(3);
+  value_set << 0, 1, -1;
+  Array4i id(0,0,0,0);
+  int k = 0;
+  do
+  {
+    M << value_set(id(0)), value_set(id(1)), value_set(id(2)), value_set(id(3));
+    
+    cb(M,false);
+    
+    id(k)++;
+    if(id(k)>=value_set.size())
+    {
+      while(k<3 && id(k)>=value_set.size()) id(++k)++;
+      id.head(k).setZero();
+      k=0;
+    }
+    
+  } while((id<int(value_set.size())).all());
+}
+
+void svd_preallocate()
+{
+  Vector3f v(3.f, 2.f, 1.f);
+  MatrixXf m = v.asDiagonal();
+
+  internal::set_is_malloc_allowed(false);
+  VERIFY_RAISES_ASSERT(VectorXf tmp(10);)
+  SVD_DEFAULT(MatrixXf) svd;
+  internal::set_is_malloc_allowed(true);
+  svd.compute(m);
+  VERIFY_IS_APPROX(svd.singularValues(), v);
+
+  SVD_DEFAULT(MatrixXf) svd2(3,3);
+  internal::set_is_malloc_allowed(false);
+  svd2.compute(m);
+  internal::set_is_malloc_allowed(true);
+  VERIFY_IS_APPROX(svd2.singularValues(), v);
+  VERIFY_RAISES_ASSERT(svd2.matrixU());
+  VERIFY_RAISES_ASSERT(svd2.matrixV());
+  svd2.compute(m, ComputeFullU | ComputeFullV);
+  VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity());
+  VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity());
+  internal::set_is_malloc_allowed(false);
+  svd2.compute(m);
+  internal::set_is_malloc_allowed(true);
+
+  SVD_DEFAULT(MatrixXf) svd3(3,3,ComputeFullU|ComputeFullV);
+  internal::set_is_malloc_allowed(false);
+  svd2.compute(m);
+  internal::set_is_malloc_allowed(true);
+  VERIFY_IS_APPROX(svd2.singularValues(), v);
+  VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity());
+  VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity());
+  internal::set_is_malloc_allowed(false);
+  svd2.compute(m, ComputeFullU|ComputeFullV);
+  internal::set_is_malloc_allowed(true);
+}
+
+template<typename SvdType,typename MatrixType> 
+void svd_verify_assert(const MatrixType& m)
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::Index Index;
+  Index rows = m.rows();
+  Index cols = m.cols();
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, RowsAtCompileTime, 1> RhsType;
+  RhsType rhs(rows);
+  SvdType svd;
+  VERIFY_RAISES_ASSERT(svd.matrixU())
+  VERIFY_RAISES_ASSERT(svd.singularValues())
+  VERIFY_RAISES_ASSERT(svd.matrixV())
+  VERIFY_RAISES_ASSERT(svd.solve(rhs))
+  MatrixType a = MatrixType::Zero(rows, cols);
+  a.setZero();
+  svd.compute(a, 0);
+  VERIFY_RAISES_ASSERT(svd.matrixU())
+  VERIFY_RAISES_ASSERT(svd.matrixV())
+  svd.singularValues();
+  VERIFY_RAISES_ASSERT(svd.solve(rhs))
+    
+  if (ColsAtCompileTime == Dynamic)
+  {
+    svd.compute(a, ComputeThinU);
+    svd.matrixU();
+    VERIFY_RAISES_ASSERT(svd.matrixV())
+    VERIFY_RAISES_ASSERT(svd.solve(rhs))
+    svd.compute(a, ComputeThinV);
+    svd.matrixV();
+    VERIFY_RAISES_ASSERT(svd.matrixU())
+    VERIFY_RAISES_ASSERT(svd.solve(rhs))
+  }
+  else
+  {
+    VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinU))
+    VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinV))
+  }
+}
+
+#undef SVD_DEFAULT
+#undef SVD_FOR_MIN_NORM
diff --git a/test/swap.cpp b/test/swap.cpp
index 36b353148..dc3610085 100644
--- a/test/swap.cpp
+++ b/test/swap.cpp
@@ -41,9 +41,15 @@ template<typename MatrixType> void swap(const MatrixType& m)
   OtherMatrixType m3_copy = m3;
   
   // test swapping 2 matrices of same type
+  Scalar *d1=m1.data(), *d2=m2.data();
   m1.swap(m2);
   VERIFY_IS_APPROX(m1,m2_copy);
   VERIFY_IS_APPROX(m2,m1_copy);
+  if(MatrixType::SizeAtCompileTime==Dynamic)
+  {
+    VERIFY(m1.data()==d2);
+    VERIFY(m2.data()==d1);
+  }
   m1 = m1_copy;
   m2 = m2_copy;
   
diff --git a/test/vectorization_logic.cpp b/test/vectorization_logic.cpp
index b069f0771..2f839cf51 100644
--- a/test/vectorization_logic.cpp
+++ b/test/vectorization_logic.cpp
@@ -27,19 +27,37 @@ std::string demangle_unrolling(int t)
   if(t==CompleteUnrolling) return "CompleteUnrolling";
   return "?";
 }
+std::string demangle_flags(int f)
+{
+  std::string res;
+  if(f&RowMajorBit)                 res += " | RowMajor";
+  if(f&PacketAccessBit)             res += " | Packet";
+  if(f&LinearAccessBit)             res += " | Linear";
+  if(f&LvalueBit)                   res += " | Lvalue";
+  if(f&DirectAccessBit)             res += " | Direct";
+  if(f&AlignedBit)                  res += " | Aligned";
+  if(f&NestByRefBit)                res += " | NestByRef";
+  if(f&NoPreferredStorageOrderBit)  res += " | NoPreferredStorageOrderBit";
+  
+  return res;
+}
 
 template<typename Dst, typename Src>
 bool test_assign(const Dst&, const Src&, int traversal, int unrolling)
 {
-  internal::assign_traits<Dst,Src>::debug();
-  bool res = internal::assign_traits<Dst,Src>::Traversal==traversal
-          && internal::assign_traits<Dst,Src>::Unrolling==unrolling;
+  typedef internal::copy_using_evaluator_traits<internal::evaluator<Dst>,internal::evaluator<Src>, internal::assign_op<typename Dst::Scalar> > traits;
+  bool res = traits::Traversal==traversal && traits::Unrolling==unrolling;
   if(!res)
   {
+    std::cerr << "Src: " << demangle_flags(Src::Flags) << std::endl;
+    std::cerr << "     " << demangle_flags(internal::evaluator<Src>::Flags) << std::endl;
+    std::cerr << "Dst: " << demangle_flags(Dst::Flags) << std::endl;
+    std::cerr << "     " << demangle_flags(internal::evaluator<Dst>::Flags) << std::endl;
+    traits::debug();
     std::cerr << " Expected Traversal == " << demangle_traversal(traversal)
-              << " got " << demangle_traversal(internal::assign_traits<Dst,Src>::Traversal) << "\n";
+              << " got " << demangle_traversal(traits::Traversal) << "\n";
     std::cerr << " Expected Unrolling == " << demangle_unrolling(unrolling)
-              << " got " << demangle_unrolling(internal::assign_traits<Dst,Src>::Unrolling) << "\n";
+              << " got " << demangle_unrolling(traits::Unrolling) << "\n";
   }
   return res;
 }
@@ -47,15 +65,19 @@ bool test_assign(const Dst&, const Src&, int traversal, int unrolling)
 template<typename Dst, typename Src>
 bool test_assign(int traversal, int unrolling)
 {
-  internal::assign_traits<Dst,Src>::debug();
-  bool res = internal::assign_traits<Dst,Src>::Traversal==traversal
-          && internal::assign_traits<Dst,Src>::Unrolling==unrolling;
+  typedef internal::copy_using_evaluator_traits<internal::evaluator<Dst>,internal::evaluator<Src>, internal::assign_op<typename Dst::Scalar> > traits;
+  bool res = traits::Traversal==traversal && traits::Unrolling==unrolling;
   if(!res)
   {
+    std::cerr << "Src: " << demangle_flags(Src::Flags) << std::endl;
+    std::cerr << "     " << demangle_flags(internal::evaluator<Src>::Flags) << std::endl;
+    std::cerr << "Dst: " << demangle_flags(Dst::Flags) << std::endl;
+    std::cerr << "     " << demangle_flags(internal::evaluator<Dst>::Flags) << std::endl;
+    traits::debug();
     std::cerr << " Expected Traversal == " << demangle_traversal(traversal)
-              << " got " << demangle_traversal(internal::assign_traits<Dst,Src>::Traversal) << "\n";
+              << " got " << demangle_traversal(traits::Traversal) << "\n";
     std::cerr << " Expected Unrolling == " << demangle_unrolling(unrolling)
-              << " got " << demangle_unrolling(internal::assign_traits<Dst,Src>::Unrolling) << "\n";
+              << " got " << demangle_unrolling(traits::Unrolling) << "\n";
   }
   return res;
 }
@@ -63,10 +85,15 @@ bool test_assign(int traversal, int unrolling)
 template<typename Xpr>
 bool test_redux(const Xpr&, int traversal, int unrolling)
 {
-  typedef internal::redux_traits<internal::scalar_sum_op<typename Xpr::Scalar>,Xpr> traits;
+  typedef internal::redux_traits<internal::scalar_sum_op<typename Xpr::Scalar>,internal::redux_evaluator<Xpr> > traits;
+  
   bool res = traits::Traversal==traversal && traits::Unrolling==unrolling;
   if(!res)
   {
+    std::cerr << demangle_flags(Xpr::Flags) << std::endl;
+    std::cerr << demangle_flags(internal::evaluator<Xpr>::Flags) << std::endl;
+    traits::debug();
+    
     std::cerr << " Expected Traversal == " << demangle_traversal(traversal)
               << " got " << demangle_traversal(traits::Traversal) << "\n";
     std::cerr << " Expected Unrolling == " << demangle_unrolling(unrolling)
diff --git a/test/vectorwiseop.cpp b/test/vectorwiseop.cpp
index 6cd1acdda..1631d54c4 100644
--- a/test/vectorwiseop.cpp
+++ b/test/vectorwiseop.cpp
@@ -104,8 +104,8 @@ template<typename ArrayType> void vectorwiseop_array(const ArrayType& m)
   
   m2 = m1;
   // yes, there might be an aliasing issue there but ".rowwise() /="
-  // is suppposed to evaluate " m2.colwise().sum()" into to temporary to avoid
-  // evaluating the reducions multiple times
+  // is supposed to evaluate " m2.colwise().sum()" into a temporary to avoid
+  // evaluating the reduction multiple times
   if(ArrayType::RowsAtCompileTime>2 || ArrayType::RowsAtCompileTime==Dynamic)
   {
     m2.rowwise() /= m2.colwise().sum();