1 // This file is part of Eigen, a lightweight C++ template library 2 // for linear algebra. 3 // 4 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud (at) inria.fr> 5 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1 (at) gmail.com> 6 // 7 // This Source Code Form is subject to the terms of the Mozilla 8 // Public License v. 2.0. If a copy of the MPL was not distributed 9 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 10 11 // this hack is needed to make this file compiles with -pedantic (gcc) 12 #ifdef __GNUC__ 13 #define throw(X) 14 #endif 15 // discard stack allocation as that too bypasses malloc 16 #define EIGEN_STACK_ALLOCATION_LIMIT 0 17 // any heap allocation will raise an assert 18 #define EIGEN_NO_MALLOC 19 20 #include "main.h" 21 #include <Eigen/Cholesky> 22 #include <Eigen/Eigenvalues> 23 #include <Eigen/LU> 24 #include <Eigen/QR> 25 #include <Eigen/SVD> 26 27 template<typename MatrixType> void nomalloc(const MatrixType& m) 28 { 29 /* this test check no dynamic memory allocation are issued with fixed-size matrices 30 */ 31 typedef typename MatrixType::Index Index; 32 typedef typename MatrixType::Scalar Scalar; 33 typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; 34 35 Index rows = m.rows(); 36 Index cols = m.cols(); 37 38 MatrixType m1 = MatrixType::Random(rows, cols), 39 m2 = MatrixType::Random(rows, cols), 40 m3(rows, cols); 41 42 Scalar s1 = internal::random<Scalar>(); 43 44 Index r = internal::random<Index>(0, rows-1), 45 c = internal::random<Index>(0, cols-1); 46 47 VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2); 48 VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c))); 49 VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix()); 50 VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2)); 51 52 m2.col(0).noalias() = m1 * m1.col(0); 53 m2.col(0).noalias() -= m1.adjoint() * m1.col(0); 54 m2.col(0).noalias() -= m1 * m1.row(0).adjoint(); 55 m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint(); 56 57 m2.row(0).noalias() = m1.row(0) * m1; 58 m2.row(0).noalias() -= m1.row(0) * m1.adjoint(); 59 m2.row(0).noalias() -= m1.col(0).adjoint() * m1; 60 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint(); 61 VERIFY_IS_APPROX(m2,m2); 62 63 m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0); 64 m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0); 65 m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint(); 66 m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint(); 67 68 m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>(); 69 m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>(); 70 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>(); 71 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>(); 72 VERIFY_IS_APPROX(m2,m2); 73 74 m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0); 75 m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0); 76 m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint(); 77 m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint(); 78 79 m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>(); 80 m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>(); 81 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>(); 82 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>(); 83 VERIFY_IS_APPROX(m2,m2); 84 85 m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1); 86 m2.template selfadjointView<Lower>().rankUpdate(m1.row(0),-1); 87 88 // The following fancy matrix-matrix products are not safe yet regarding static allocation 89 // m1 += m1.template triangularView<Upper>() * m2.col(; 90 // m1.template selfadjointView<Lower>().rankUpdate(m2); 91 // m1 += m1.template triangularView<Upper>() * m2; 92 // m1 += m1.template selfadjointView<Lower>() * m2; 93 // VERIFY_IS_APPROX(m1,m1); 94 } 95 96 template<typename Scalar> 97 void ctms_decompositions() 98 { 99 const int maxSize = 16; 100 const int size = 12; 101 102 typedef Eigen::Matrix<Scalar, 103 Eigen::Dynamic, Eigen::Dynamic, 104 0, 105 maxSize, maxSize> Matrix; 106 107 typedef Eigen::Matrix<Scalar, 108 Eigen::Dynamic, 1, 109 0, 110 maxSize, 1> Vector; 111 112 typedef Eigen::Matrix<std::complex<Scalar>, 113 Eigen::Dynamic, Eigen::Dynamic, 114 0, 115 maxSize, maxSize> ComplexMatrix; 116 117 const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size)); 118 Matrix X(size,size); 119 const ComplexMatrix complexA(ComplexMatrix::Random(size, size)); 120 const Matrix saA = A.adjoint() * A; 121 const Vector b(Vector::Random(size)); 122 Vector x(size); 123 124 // Cholesky module 125 Eigen::LLT<Matrix> LLT; LLT.compute(A); 126 X = LLT.solve(B); 127 x = LLT.solve(b); 128 Eigen::LDLT<Matrix> LDLT; LDLT.compute(A); 129 X = LDLT.solve(B); 130 x = LDLT.solve(b); 131 132 // Eigenvalues module 133 Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp; hessDecomp.compute(complexA); 134 Eigen::ComplexSchur<ComplexMatrix> cSchur(size); cSchur.compute(complexA); 135 Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver; cEigSolver.compute(complexA); 136 Eigen::EigenSolver<Matrix> eigSolver; eigSolver.compute(A); 137 Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size); saEigSolver.compute(saA); 138 Eigen::Tridiagonalization<Matrix> tridiag; tridiag.compute(saA); 139 140 // LU module 141 Eigen::PartialPivLU<Matrix> ppLU; ppLU.compute(A); 142 X = ppLU.solve(B); 143 x = ppLU.solve(b); 144 Eigen::FullPivLU<Matrix> fpLU; fpLU.compute(A); 145 X = fpLU.solve(B); 146 x = fpLU.solve(b); 147 148 // QR module 149 Eigen::HouseholderQR<Matrix> hQR; hQR.compute(A); 150 X = hQR.solve(B); 151 x = hQR.solve(b); 152 Eigen::ColPivHouseholderQR<Matrix> cpQR; cpQR.compute(A); 153 X = cpQR.solve(B); 154 x = cpQR.solve(b); 155 Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A); 156 // FIXME X = fpQR.solve(B); 157 x = fpQR.solve(b); 158 159 // SVD module 160 Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV); 161 } 162 163 void test_nomalloc() 164 { 165 // check that our operator new is indeed called: 166 VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3))); 167 CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) ); 168 CALL_SUBTEST_2(nomalloc(Matrix4d()) ); 169 CALL_SUBTEST_3(nomalloc(Matrix<float,32,32>()) ); 170 171 // Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms) 172 CALL_SUBTEST_4(ctms_decompositions<float>()); 173 174 } 175
