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e7d809d434
* use SelfAdjointView instead of Eigen2's SelfAdjoint flag. * add tests and documentation. * allow eigenvalues() for non-selfadjoint matrices. * they no longer depend only on SelfAdjointEigenSolver, so move them to a separate file
99 lines
3.7 KiB
C++
99 lines
3.7 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#include "main.h"
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#include <Eigen/Eigenvalues>
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#ifdef HAS_GSL
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#include "gsl_helper.h"
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#endif
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template<typename MatrixType> void eigensolver(const MatrixType& m)
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{
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/* this test covers the following files:
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EigenSolver.h
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*/
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int rows = m.rows();
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int cols = m.cols();
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typedef typename MatrixType::Scalar Scalar;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
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typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType;
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typedef typename std::complex<typename NumTraits<typename MatrixType::Scalar>::Real> Complex;
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// RealScalar largerEps = 10*test_precision<RealScalar>();
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MatrixType a = MatrixType::Random(rows,cols);
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MatrixType a1 = MatrixType::Random(rows,cols);
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MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
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EigenSolver<MatrixType> ei0(symmA);
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VERIFY_IS_APPROX(symmA * ei0.pseudoEigenvectors(), ei0.pseudoEigenvectors() * ei0.pseudoEigenvalueMatrix());
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VERIFY_IS_APPROX((symmA.template cast<Complex>()) * (ei0.pseudoEigenvectors().template cast<Complex>()),
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(ei0.pseudoEigenvectors().template cast<Complex>()) * (ei0.eigenvalues().asDiagonal()));
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EigenSolver<MatrixType> ei1(a);
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VERIFY_IS_APPROX(a * ei1.pseudoEigenvectors(), ei1.pseudoEigenvectors() * ei1.pseudoEigenvalueMatrix());
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VERIFY_IS_APPROX(a.template cast<Complex>() * ei1.eigenvectors(),
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ei1.eigenvectors() * ei1.eigenvalues().asDiagonal());
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VERIFY_IS_APPROX(a.eigenvalues(), ei1.eigenvalues());
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MatrixType id = MatrixType::Identity(rows, cols);
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VERIFY_IS_APPROX(id.operatorNorm(), RealScalar(1));
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}
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template<typename MatrixType> void eigensolver_verify_assert()
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{
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MatrixType tmp;
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EigenSolver<MatrixType> eig;
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VERIFY_RAISES_ASSERT(eig.eigenvectors())
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VERIFY_RAISES_ASSERT(eig.pseudoEigenvectors())
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VERIFY_RAISES_ASSERT(eig.pseudoEigenvalueMatrix())
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VERIFY_RAISES_ASSERT(eig.eigenvalues())
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}
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void test_eigensolver_generic()
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_1( eigensolver(Matrix4f()) );
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CALL_SUBTEST_2( eigensolver(MatrixXd(17,17)) );
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// some trivial but implementation-wise tricky cases
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CALL_SUBTEST_2( eigensolver(MatrixXd(1,1)) );
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CALL_SUBTEST_2( eigensolver(MatrixXd(2,2)) );
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CALL_SUBTEST_3( eigensolver(Matrix<double,1,1>()) );
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CALL_SUBTEST_4( eigensolver(Matrix2d()) );
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}
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CALL_SUBTEST_1( eigensolver_verify_assert<Matrix4f>() );
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CALL_SUBTEST_2( eigensolver_verify_assert<MatrixXd>() );
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CALL_SUBTEST_4( eigensolver_verify_assert<Matrix2d>() );
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CALL_SUBTEST_5( eigensolver_verify_assert<MatrixXf>() );
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// Test problem size constructors
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CALL_SUBTEST_6(EigenSolver<MatrixXf>(10));
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}
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