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87 lines
3.5 KiB
87 lines
3.5 KiB
// 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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// This Source Code Form is subject to the terms of the Mozilla |
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// Public License v. 2.0. If a copy of the MPL was not distributed |
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
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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 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()+numext::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() + numext::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 = 0; |
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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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TEST_SET_BUT_UNUSED_VARIABLE(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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TEST_SET_BUT_UNUSED_VARIABLE(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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TEST_SET_BUT_UNUSED_VARIABLE(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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TEST_SET_BUT_UNUSED_VARIABLE(s) |
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} |
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}
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