Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 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@inria.fr> |
| 5 | // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@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 |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 15 | |
| 16 | #ifdef __INTEL_COMPILER |
| 17 | // disable "warning #76: argument to macro is empty" produced by the above hack |
| 18 | #pragma warning disable 76 |
| 19 | #endif |
| 20 | |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 21 | // discard stack allocation as that too bypasses malloc |
| 22 | #define EIGEN_STACK_ALLOCATION_LIMIT 0 |
| 23 | // any heap allocation will raise an assert |
| 24 | #define EIGEN_NO_MALLOC |
| 25 | |
| 26 | #include "main.h" |
| 27 | #include <Eigen/Cholesky> |
| 28 | #include <Eigen/Eigenvalues> |
| 29 | #include <Eigen/LU> |
| 30 | #include <Eigen/QR> |
| 31 | #include <Eigen/SVD> |
| 32 | |
| 33 | template<typename MatrixType> void nomalloc(const MatrixType& m) |
| 34 | { |
| 35 | /* this test check no dynamic memory allocation are issued with fixed-size matrices |
| 36 | */ |
| 37 | typedef typename MatrixType::Index Index; |
| 38 | typedef typename MatrixType::Scalar Scalar; |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 39 | |
| 40 | Index rows = m.rows(); |
| 41 | Index cols = m.cols(); |
| 42 | |
| 43 | MatrixType m1 = MatrixType::Random(rows, cols), |
| 44 | m2 = MatrixType::Random(rows, cols), |
| 45 | m3(rows, cols); |
| 46 | |
| 47 | Scalar s1 = internal::random<Scalar>(); |
| 48 | |
| 49 | Index r = internal::random<Index>(0, rows-1), |
| 50 | c = internal::random<Index>(0, cols-1); |
| 51 | |
| 52 | VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2); |
| 53 | VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c))); |
| 54 | VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix()); |
| 55 | VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2)); |
| 56 | |
| 57 | m2.col(0).noalias() = m1 * m1.col(0); |
| 58 | m2.col(0).noalias() -= m1.adjoint() * m1.col(0); |
| 59 | m2.col(0).noalias() -= m1 * m1.row(0).adjoint(); |
| 60 | m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint(); |
| 61 | |
| 62 | m2.row(0).noalias() = m1.row(0) * m1; |
| 63 | m2.row(0).noalias() -= m1.row(0) * m1.adjoint(); |
| 64 | m2.row(0).noalias() -= m1.col(0).adjoint() * m1; |
| 65 | m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint(); |
| 66 | VERIFY_IS_APPROX(m2,m2); |
| 67 | |
| 68 | m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0); |
| 69 | m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0); |
| 70 | m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint(); |
| 71 | m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint(); |
| 72 | |
| 73 | m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>(); |
| 74 | m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>(); |
| 75 | m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>(); |
| 76 | m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>(); |
| 77 | VERIFY_IS_APPROX(m2,m2); |
| 78 | |
| 79 | m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0); |
| 80 | m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0); |
| 81 | m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint(); |
| 82 | m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint(); |
| 83 | |
| 84 | m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>(); |
| 85 | m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>(); |
| 86 | m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>(); |
| 87 | m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>(); |
| 88 | VERIFY_IS_APPROX(m2,m2); |
| 89 | |
| 90 | m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1); |
| 91 | m2.template selfadjointView<Lower>().rankUpdate(m1.row(0),-1); |
| 92 | |
| 93 | // The following fancy matrix-matrix products are not safe yet regarding static allocation |
| 94 | // m1 += m1.template triangularView<Upper>() * m2.col(; |
| 95 | // m1.template selfadjointView<Lower>().rankUpdate(m2); |
| 96 | // m1 += m1.template triangularView<Upper>() * m2; |
| 97 | // m1 += m1.template selfadjointView<Lower>() * m2; |
| 98 | // VERIFY_IS_APPROX(m1,m1); |
| 99 | } |
| 100 | |
| 101 | template<typename Scalar> |
| 102 | void ctms_decompositions() |
| 103 | { |
| 104 | const int maxSize = 16; |
| 105 | const int size = 12; |
| 106 | |
| 107 | typedef Eigen::Matrix<Scalar, |
| 108 | Eigen::Dynamic, Eigen::Dynamic, |
| 109 | 0, |
| 110 | maxSize, maxSize> Matrix; |
| 111 | |
| 112 | typedef Eigen::Matrix<Scalar, |
| 113 | Eigen::Dynamic, 1, |
| 114 | 0, |
| 115 | maxSize, 1> Vector; |
| 116 | |
| 117 | typedef Eigen::Matrix<std::complex<Scalar>, |
| 118 | Eigen::Dynamic, Eigen::Dynamic, |
| 119 | 0, |
| 120 | maxSize, maxSize> ComplexMatrix; |
| 121 | |
| 122 | const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size)); |
| 123 | Matrix X(size,size); |
| 124 | const ComplexMatrix complexA(ComplexMatrix::Random(size, size)); |
| 125 | const Matrix saA = A.adjoint() * A; |
| 126 | const Vector b(Vector::Random(size)); |
| 127 | Vector x(size); |
| 128 | |
| 129 | // Cholesky module |
| 130 | Eigen::LLT<Matrix> LLT; LLT.compute(A); |
| 131 | X = LLT.solve(B); |
| 132 | x = LLT.solve(b); |
| 133 | Eigen::LDLT<Matrix> LDLT; LDLT.compute(A); |
| 134 | X = LDLT.solve(B); |
| 135 | x = LDLT.solve(b); |
| 136 | |
| 137 | // Eigenvalues module |
| 138 | Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp; hessDecomp.compute(complexA); |
| 139 | Eigen::ComplexSchur<ComplexMatrix> cSchur(size); cSchur.compute(complexA); |
| 140 | Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver; cEigSolver.compute(complexA); |
| 141 | Eigen::EigenSolver<Matrix> eigSolver; eigSolver.compute(A); |
| 142 | Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size); saEigSolver.compute(saA); |
| 143 | Eigen::Tridiagonalization<Matrix> tridiag; tridiag.compute(saA); |
| 144 | |
| 145 | // LU module |
| 146 | Eigen::PartialPivLU<Matrix> ppLU; ppLU.compute(A); |
| 147 | X = ppLU.solve(B); |
| 148 | x = ppLU.solve(b); |
| 149 | Eigen::FullPivLU<Matrix> fpLU; fpLU.compute(A); |
| 150 | X = fpLU.solve(B); |
| 151 | x = fpLU.solve(b); |
| 152 | |
| 153 | // QR module |
| 154 | Eigen::HouseholderQR<Matrix> hQR; hQR.compute(A); |
| 155 | X = hQR.solve(B); |
| 156 | x = hQR.solve(b); |
| 157 | Eigen::ColPivHouseholderQR<Matrix> cpQR; cpQR.compute(A); |
| 158 | X = cpQR.solve(B); |
| 159 | x = cpQR.solve(b); |
| 160 | Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A); |
| 161 | // FIXME X = fpQR.solve(B); |
| 162 | x = fpQR.solve(b); |
| 163 | |
| 164 | // SVD module |
| 165 | Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV); |
| 166 | } |
| 167 | |
| 168 | void test_nomalloc() |
| 169 | { |
| 170 | // check that our operator new is indeed called: |
| 171 | VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3))); |
| 172 | CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) ); |
| 173 | CALL_SUBTEST_2(nomalloc(Matrix4d()) ); |
| 174 | CALL_SUBTEST_3(nomalloc(Matrix<float,32,32>()) ); |
| 175 | |
| 176 | // Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms) |
| 177 | CALL_SUBTEST_4(ctms_decompositions<float>()); |
| 178 | |
| 179 | } |