1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is triangularView of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla 7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed 8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "main.h" 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void trmv(const MatrixType& m) 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Index Index; 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename NumTraits<Scalar>::Real RealScalar; 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath RealScalar largerEps = 10*test_precision<RealScalar>(); 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rows = m.rows(); 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index cols = m.cols(); 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1 = MatrixType::Random(rows, cols), 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3(rows, cols); 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VectorType v1 = VectorType::Random(rows); 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s1 = internal::random<Scalar>(); 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m1 = MatrixType::Random(rows, cols); 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // check with a column-major matrix 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::Lower>(); 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::Lower>() * v1, largerEps)); 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::Upper>(); 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::Upper>() * v1, largerEps)); 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::UnitLower>(); 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::UnitLower>() * v1, largerEps)); 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::UnitUpper>(); 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::UnitUpper>() * v1, largerEps)); 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // check conjugated and scalar multiple expressions (col-major) 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::Lower>(); 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(((s1*m3).conjugate() * v1).isApprox((s1*m1).conjugate().template triangularView<Eigen::Lower>() * v1, largerEps)); 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::Upper>(); 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m3.conjugate() * v1.conjugate()).isApprox(m1.conjugate().template triangularView<Eigen::Upper>() * v1.conjugate(), largerEps)); 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // check with a row-major matrix 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::Upper>(); 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::Lower>() * v1, largerEps)); 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::Lower>(); 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::Upper>() * v1, largerEps)); 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::UnitUpper>(); 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::UnitLower>() * v1, largerEps)); 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::UnitLower>(); 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::UnitUpper>() * v1, largerEps)); 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // check conjugated and scalar multiple expressions (row-major) 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::Upper>(); 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m3.adjoint() * v1).isApprox(m1.adjoint().template triangularView<Eigen::Lower>() * v1, largerEps)); 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::Lower>(); 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m3.adjoint() * (s1*v1.conjugate())).isApprox(m1.adjoint().template triangularView<Eigen::Upper>() * (s1*v1.conjugate()), largerEps)); 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::UnitUpper>(); 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // check transposed cases: 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1.template triangularView<Eigen::Lower>(); 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((v1.transpose() * m3).isApprox(v1.transpose() * m1.template triangularView<Eigen::Lower>(), largerEps)); 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((v1.adjoint() * m3).isApprox(v1.adjoint() * m1.template triangularView<Eigen::Lower>(), largerEps)); 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((v1.adjoint() * m3.adjoint()).isApprox(v1.adjoint() * m1.template triangularView<Eigen::Lower>().adjoint(), largerEps)); 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // TODO check with sub-matrices 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_product_trmv() 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 767faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez int s = 0; 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat ; i++) { 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( trmv(Matrix<float, 1, 1>()) ); 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( trmv(Matrix<float, 2, 2>()) ); 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( trmv(Matrix3d()) ); 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2); 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( trmv(MatrixXcf(s,s)) ); 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2); 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( trmv(MatrixXcd(s,s)) ); 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6( trmv(Matrix<float,Dynamic,Dynamic,RowMajor>(s, s)) ); 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 887faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez TEST_SET_BUT_UNUSED_VARIABLE(s); 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 90