1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008-2009 Benoit Jacob <jacob.benoit.1@gmail.com> 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#include <Eigen/LU> 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathusing namespace std; 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void lu_non_invertible() 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Index Index; 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::RealScalar RealScalar; 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /* this test covers the following files: 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath LU.h 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rows, cols, cols2; 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(MatrixType::RowsAtCompileTime==Dynamic) 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath rows = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE); 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath rows = MatrixType::RowsAtCompileTime; 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(MatrixType::ColsAtCompileTime==Dynamic) 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath cols = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE); 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath cols2 = internal::random<int>(2,EIGEN_TEST_MAX_SIZE); 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath cols2 = cols = MatrixType::ColsAtCompileTime; 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath RowsAtCompileTime = MatrixType::RowsAtCompileTime, 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ColsAtCompileTime = MatrixType::ColsAtCompileTime 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::kernel_retval_base<FullPivLU<MatrixType> >::ReturnType KernelMatrixType; 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::image_retval_base<FullPivLU<MatrixType> >::ReturnType ImageMatrixType; 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<typename MatrixType::Scalar, ColsAtCompileTime, ColsAtCompileTime> 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CMatrixType; 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<typename MatrixType::Scalar, RowsAtCompileTime, RowsAtCompileTime> 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath RMatrixType; 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1); 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // The image of the zero matrix should consist of a single (zero) column vector 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((MatrixType::Zero(rows,cols).fullPivLu().image(MatrixType::Zero(rows,cols)).cols() == 1)); 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1(rows, cols), m3(rows, cols2); 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CMatrixType m2(cols, cols2); 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath createRandomPIMatrixOfRank(rank, rows, cols, m1); 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath FullPivLU<MatrixType> lu; 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // The special value 0.01 below works well in tests. Keep in mind that we're only computing the rank 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // of singular values are either 0 or 1. 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // So it's not clear at all that the epsilon should play any role there. 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath lu.setThreshold(RealScalar(0.01)); 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath lu.compute(m1); 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType u(rows,cols); 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath u = lu.matrixLU().template triangularView<Upper>(); 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath RMatrixType l = RMatrixType::Identity(rows,rows); 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath l.block(0,0,rows,(std::min)(rows,cols)).template triangularView<StrictlyLower>() 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath = lu.matrixLU().block(0,0,rows,(std::min)(rows,cols)); 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(lu.permutationP() * m1 * lu.permutationQ(), l*u); 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath KernelMatrixType m1kernel = lu.kernel(); 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ImageMatrixType m1image = lu.image(m1); 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1, lu.reconstructedMatrix()); 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(rank == lu.rank()); 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(cols - lu.rank() == lu.dimensionOfKernel()); 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(!lu.isInjective()); 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(!lu.isInvertible()); 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(!lu.isSurjective()); 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY((m1 * m1kernel).isMuchSmallerThan(m1)); 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(m1image.fullPivLu().rank() == rank); 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1 * m1.adjoint() * m1image, m1image); 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2 = CMatrixType::Random(cols,cols2); 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1*m2; 92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2 = CMatrixType::Random(cols,cols2); 93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test that the code, which does resize(), may be applied to an xpr 94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.block(0,0,m2.rows(),m2.cols()) = lu.solve(m3); 95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3, m1*m2); 96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void lu_invertible() 99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /* this test covers the following files: 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath LU.h 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int size = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1(size, size), m2(size, size), m3(size, size); 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath FullPivLU<MatrixType> lu; 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath lu.setThreshold(RealScalar(0.01)); 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath do { 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m1 = MatrixType::Random(size,size); 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath lu.compute(m1); 113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } while(!lu.isInvertible()); 114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1, lu.reconstructedMatrix()); 116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(0 == lu.dimensionOfKernel()); 117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(lu.kernel().cols() == 1); // the kernel() should consist of a single (zero) column vector 118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(size == lu.rank()); 119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(lu.isInjective()); 120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(lu.isSurjective()); 121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(lu.isInvertible()); 122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(lu.image(m1).fullPivLu().isInvertible()); 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = MatrixType::Random(size,size); 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2 = lu.solve(m3); 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3, m1*m2); 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2, lu.inverse()*m3); 127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void lu_partial_piv() 130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /* this test covers the following files: 132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath PartialPivLU.h 133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Index Index; 135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; 137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rows = internal::random<Index>(1,4); 138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index cols = rows; 139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1(cols, rows); 141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m1.setRandom(); 142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath PartialPivLU<MatrixType> plu(m1); 143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1, plu.reconstructedMatrix()); 145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void lu_verify_assert() 148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 149c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType tmp; 150c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 151c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath FullPivLU<MatrixType> lu; 152c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.matrixLU()) 153c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.permutationP()) 154c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.permutationQ()) 155c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.kernel()) 156c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.image(tmp)) 157c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.solve(tmp)) 158c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.determinant()) 159c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.rank()) 160c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.dimensionOfKernel()) 161c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.isInjective()) 162c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.isSurjective()) 163c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.isInvertible()) 164c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(lu.inverse()) 165c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 166c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath PartialPivLU<MatrixType> plu; 167c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(plu.matrixLU()) 168c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(plu.permutationP()) 169c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(plu.solve(tmp)) 170c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(plu.determinant()) 171c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(plu.inverse()) 172c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 173c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 174c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_lu() 175c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 176c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 177c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( lu_non_invertible<Matrix3f>() ); 178c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( lu_verify_assert<Matrix3f>() ); 179c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 180c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( (lu_non_invertible<Matrix<double, 4, 6> >()) ); 181c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( (lu_verify_assert<Matrix<double, 4, 6> >()) ); 182c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 183c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( lu_non_invertible<MatrixXf>() ); 184c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( lu_invertible<MatrixXf>() ); 185c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( lu_verify_assert<MatrixXf>() ); 186c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 187c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( lu_non_invertible<MatrixXd>() ); 188c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( lu_invertible<MatrixXd>() ); 189c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( lu_partial_piv<MatrixXd>() ); 190c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( lu_verify_assert<MatrixXd>() ); 191c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 192c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( lu_non_invertible<MatrixXcf>() ); 193c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( lu_invertible<MatrixXcf>() ); 194c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( lu_verify_assert<MatrixXcf>() ); 195c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 196c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6( lu_non_invertible<MatrixXcd>() ); 197c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6( lu_invertible<MatrixXcd>() ); 198c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6( lu_partial_piv<MatrixXcd>() ); 199c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6( lu_verify_assert<MatrixXcd>() ); 200c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 201c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_7(( lu_non_invertible<Matrix<float,Dynamic,16> >() )); 202c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 203c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Test problem size constructors 204c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_9( PartialPivLU<MatrixXf>(10) ); 205c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_9( FullPivLU<MatrixXf>(10, 20); ); 206c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 207c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 208