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