1 files changed, 81 insertions, 0 deletions
diff --git a/test/product_selfadjoint.cpp b/test/product_selfadjoint.cpp
new file mode 100644
index 000000000..95693b155
--- /dev/null
+++ b/test/product_selfadjoint.cpp
@@ -0,0 +1,81 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+template<typename MatrixType> void product_selfadjoint(const MatrixType& m)
+{
+  typedef typename MatrixType::Index Index;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
+  typedef Matrix<Scalar, 1, MatrixType::RowsAtCompileTime> RowVectorType;
+
+  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, Dynamic, RowMajor> RhsMatrixType;
+
+  Index rows = m.rows();
+  Index cols = m.cols();
+
+  MatrixType m1 = MatrixType::Random(rows, cols),
+             m2 = MatrixType::Random(rows, cols),
+             m3;
+  VectorType v1 = VectorType::Random(rows),
+             v2 = VectorType::Random(rows),
+             v3(rows);
+  RowVectorType r1 = RowVectorType::Random(rows),
+                r2 = RowVectorType::Random(rows);
+  RhsMatrixType m4 = RhsMatrixType::Random(rows,10);
+
+  Scalar s1 = internal::random<Scalar>(),
+         s2 = internal::random<Scalar>(),
+         s3 = internal::random<Scalar>();
+
+  m1 = (m1.adjoint() + m1).eval();
+
+  // rank2 update
+  m2 = m1.template triangularView<Lower>();
+  m2.template selfadjointView<Lower>().rankUpdate(v1,v2);
+  VERIFY_IS_APPROX(m2, (m1 + v1 * v2.adjoint()+ v2 * v1.adjoint()).template triangularView<Lower>().toDenseMatrix());
+
+  m2 = m1.template triangularView<Upper>();
+  m2.template selfadjointView<Upper>().rankUpdate(-v1,s2*v2,s3);
+  VERIFY_IS_APPROX(m2, (m1 + (s3*(-v1)*(s2*v2).adjoint()+internal::conj(s3)*(s2*v2)*(-v1).adjoint())).template triangularView<Upper>().toDenseMatrix());
+
+  m2 = m1.template triangularView<Upper>();
+  m2.template selfadjointView<Upper>().rankUpdate(-s2*r1.adjoint(),r2.adjoint()*s3,s1);
+  VERIFY_IS_APPROX(m2, (m1 + s1*(-s2*r1.adjoint())*(r2.adjoint()*s3).adjoint() + internal::conj(s1)*(r2.adjoint()*s3) * (-s2*r1.adjoint()).adjoint()).template triangularView<Upper>().toDenseMatrix());
+
+  if (rows>1)
+  {
+    m2 = m1.template triangularView<Lower>();
+    m2.block(1,1,rows-1,cols-1).template selfadjointView<Lower>().rankUpdate(v1.tail(rows-1),v2.head(cols-1));
+    m3 = m1;
+    m3.block(1,1,rows-1,cols-1) += v1.tail(rows-1) * v2.head(cols-1).adjoint()+ v2.head(cols-1) * v1.tail(rows-1).adjoint();
+    VERIFY_IS_APPROX(m2, m3.template triangularView<Lower>().toDenseMatrix());
+  }
+}
+
+void test_product_selfadjoint()
+{
+  int s;
+  for(int i = 0; i < g_repeat ; i++) {
+    CALL_SUBTEST_1( product_selfadjoint(Matrix<float, 1, 1>()) );
+    CALL_SUBTEST_2( product_selfadjoint(Matrix<float, 2, 2>()) );
+    CALL_SUBTEST_3( product_selfadjoint(Matrix3d()) );
+    s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
+    CALL_SUBTEST_4( product_selfadjoint(MatrixXcf(s, s)) );
+    s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
+    CALL_SUBTEST_5( product_selfadjoint(MatrixXcd(s,s)) );
+    s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
+    CALL_SUBTEST_6( product_selfadjoint(MatrixXd(s,s)) );
+    s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
+    CALL_SUBTEST_7( product_selfadjoint(Matrix<float,Dynamic,Dynamic,RowMajor>(s,s)) );
+  }
+  EIGEN_UNUSED_VARIABLE(s)
+}