// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #include "main.h" template void product_selfadjoint(const MatrixType& m) { typedef typename MatrixType::Index Index; typedef typename MatrixType::Scalar Scalar; typedef typename NumTraits::Real RealScalar; typedef Matrix VectorType; typedef Matrix RowVectorType; typedef Matrix 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(), s2 = internal::random(), s3 = internal::random(); m1 = (m1.adjoint() + m1).eval(); // rank2 update m2 = m1.template triangularView(); m2.template selfadjointView().rankUpdate(v1,v2); VERIFY_IS_APPROX(m2, (m1 + v1 * v2.adjoint()+ v2 * v1.adjoint()).template triangularView().toDenseMatrix()); m2 = m1.template triangularView(); m2.template selfadjointView().rankUpdate(-v1,s2*v2,s3); VERIFY_IS_APPROX(m2, (m1 + (s3*(-v1)*(s2*v2).adjoint()+internal::conj(s3)*(s2*v2)*(-v1).adjoint())).template triangularView().toDenseMatrix()); m2 = m1.template triangularView(); m2.template selfadjointView().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().toDenseMatrix()); if (rows>1) { m2 = m1.template triangularView(); m2.block(1,1,rows-1,cols-1).template selfadjointView().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().toDenseMatrix()); } } void test_product_selfadjoint() { int s; for(int i = 0; i < g_repeat ; i++) { CALL_SUBTEST_1( product_selfadjoint(Matrix()) ); CALL_SUBTEST_2( product_selfadjoint(Matrix()) ); CALL_SUBTEST_3( product_selfadjoint(Matrix3d()) ); s = internal::random(1,150); CALL_SUBTEST_4( product_selfadjoint(MatrixXcf(s, s)) ); s = internal::random(1,150); CALL_SUBTEST_5( product_selfadjoint(MatrixXcd(s,s)) ); s = internal::random(1,320); CALL_SUBTEST_6( product_selfadjoint(MatrixXd(s,s)) ); s = internal::random(1,320); CALL_SUBTEST_7( product_selfadjoint(Matrix(s,s)) ); } }