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97 lines
4.1 KiB
C++
97 lines
4.1 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) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.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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template<typename MatrixType> void product_selfadjoint(const MatrixType& m)
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{
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typedef typename MatrixType::Index Index;
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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<Scalar, 1, MatrixType::RowsAtCompileTime> RowVectorType;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, Dynamic, RowMajor> RhsMatrixType;
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Index rows = m.rows();
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Index cols = m.cols();
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MatrixType m1 = MatrixType::Random(rows, cols),
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m2 = MatrixType::Random(rows, cols),
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m3;
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VectorType v1 = VectorType::Random(rows),
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v2 = VectorType::Random(rows),
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v3(rows);
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RowVectorType r1 = RowVectorType::Random(rows),
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r2 = RowVectorType::Random(rows);
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RhsMatrixType m4 = RhsMatrixType::Random(rows,10);
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Scalar s1 = internal::random<Scalar>(),
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s2 = internal::random<Scalar>(),
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s3 = internal::random<Scalar>();
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m1 = (m1.adjoint() + m1).eval();
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// rank2 update
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m2 = m1.template triangularView<Lower>();
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m2.template selfadjointView<Lower>().rankUpdate(v1,v2);
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VERIFY_IS_APPROX(m2, (m1 + v1 * v2.adjoint()+ v2 * v1.adjoint()).template triangularView<Lower>().toDenseMatrix());
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m2 = m1.template triangularView<Upper>();
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m2.template selfadjointView<Upper>().rankUpdate(-v1,s2*v2,s3);
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VERIFY_IS_APPROX(m2, (m1 + (s3*(-v1)*(s2*v2).adjoint()+internal::conj(s3)*(s2*v2)*(-v1).adjoint())).template triangularView<Upper>().toDenseMatrix());
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m2 = m1.template triangularView<Upper>();
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m2.template selfadjointView<Upper>().rankUpdate(-s2*r1.adjoint(),r2.adjoint()*s3,s1);
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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());
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if (rows>1)
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{
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m2 = m1.template triangularView<Lower>();
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m2.block(1,1,rows-1,cols-1).template selfadjointView<Lower>().rankUpdate(v1.tail(rows-1),v2.head(cols-1));
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m3 = m1;
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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();
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VERIFY_IS_APPROX(m2, m3.template triangularView<Lower>().toDenseMatrix());
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}
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}
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void test_product_selfadjoint()
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{
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int s;
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for(int i = 0; i < g_repeat ; i++) {
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CALL_SUBTEST_1( product_selfadjoint(Matrix<float, 1, 1>()) );
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CALL_SUBTEST_2( product_selfadjoint(Matrix<float, 2, 2>()) );
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CALL_SUBTEST_3( product_selfadjoint(Matrix3d()) );
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s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
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CALL_SUBTEST_4( product_selfadjoint(MatrixXcf(s, s)) );
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s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
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CALL_SUBTEST_5( product_selfadjoint(MatrixXcd(s,s)) );
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s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
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CALL_SUBTEST_6( product_selfadjoint(MatrixXd(s,s)) );
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s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
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CALL_SUBTEST_7( product_selfadjoint(Matrix<float,Dynamic,Dynamic,RowMajor>(s,s)) );
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}
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EIGEN_UNUSED_VARIABLE(s)
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}
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