Add a preliminary GeneralizedEigenSolver computing the eigenvalues of Av=lBv with A and B general real matrices.

Currently only the eigenvalues are reported.

1 files changed, 63 insertions, 0 deletions

diff --git a/test/eigensolver_generalized_real.cpp b/test/eigensolver_generalized_real.cpp
new file mode 100644
index 000000000..e3edbb772
--- /dev/null
+++ b/test/eigensolver_generalized_real.cpp
@@ -0,0 +1,63 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 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"
+#include <limits>
+#include <Eigen/Eigenvalues>
+
+template<typename MatrixType> void generalized_eigensolver_real(const MatrixType& m)
+{
+  typedef typename MatrixType::Index Index;
+  /* this test covers the following files:
+     GeneralizedEigenSolver.h
+  */
+  Index rows = m.rows();
+  Index cols = m.cols();
+
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
+  typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType;
+  typedef typename std::complex<typename NumTraits<typename MatrixType::Scalar>::Real> Complex;
+
+  MatrixType a = MatrixType::Random(rows,cols);
+  MatrixType b = MatrixType::Random(rows,cols);
+  MatrixType a1 = MatrixType::Random(rows,cols);
+  MatrixType b1 = MatrixType::Random(rows,cols);
+  MatrixType spdA =  a.adjoint() * a + a1.adjoint() * a1;
+  MatrixType spdB =  b.adjoint() * b + b1.adjoint() * b1;
+
+  // lets compare to GeneralizedSelfAdjointEigenSolver
+  GeneralizedSelfAdjointEigenSolver<MatrixType> symmEig(spdA, spdB);
+  GeneralizedEigenSolver<MatrixType> eig(spdA, spdB);
+
+  VERIFY_IS_EQUAL(eig.eigenvalues().imag().cwiseAbs().maxCoeff(), 0);
+
+  VectorType realEigenvalues = eig.eigenvalues().real();
+  std::sort(realEigenvalues.data(), realEigenvalues.data()+realEigenvalues.size());
+  VERIFY_IS_APPROX(realEigenvalues, symmEig.eigenvalues());
+}
+
+void test_eigensolver_generalized_real()
+{
+  int s;
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1( generalized_eigensolver_real(Matrix4f()) );
+    s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4);
+    CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(s,s)) );
+
+    // some trivial but implementation-wise tricky cases
+    CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(1,1)) );
+    CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(2,2)) );
+    CALL_SUBTEST_3( generalized_eigensolver_real(Matrix<double,1,1>()) );
+    CALL_SUBTEST_4( generalized_eigensolver_real(Matrix2d()) );
+  }
+  
+  EIGEN_UNUSED_VARIABLE(s)
+}