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 Gael Guennebaud <gael.guennebaud@inria.fr> 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> 6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla 8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed 9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// this hack is needed to make this file compiles with -pedantic (gcc) 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#ifdef __GNUC__ 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define throw(X) 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif 157faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 167faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez#ifdef __INTEL_COMPILER 177faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez // disable "warning #76: argument to macro is empty" produced by the above hack 187faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez #pragma warning disable 76 197faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez#endif 207faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// discard stack allocation as that too bypasses malloc 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_STACK_ALLOCATION_LIMIT 0 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// any heap allocation will raise an assert 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_NO_MALLOC 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "main.h" 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/Cholesky> 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/Eigenvalues> 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/LU> 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/QR> 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/SVD> 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void nomalloc(const MatrixType& m) 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /* this test check no dynamic memory allocation are issued with fixed-size matrices 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Index Index; 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rows = m.rows(); 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index cols = m.cols(); 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1 = MatrixType::Random(rows, cols), 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2 = MatrixType::Random(rows, cols), 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3(rows, cols); 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s1 = internal::random<Scalar>(); 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index r = internal::random<Index>(0, rows-1), 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath c = internal::random<Index>(0, cols-1); 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2); 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c))); 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix()); 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2)); 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() = m1 * m1.col(0); 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint() * m1.col(0); 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1 * m1.row(0).adjoint(); 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint(); 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() = m1.row(0) * m1; 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.row(0) * m1.adjoint(); 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1; 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint(); 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2,m2); 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0); 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0); 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint(); 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint(); 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>(); 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>(); 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>(); 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>(); 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2,m2); 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0); 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0); 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint(); 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint(); 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>(); 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>(); 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>(); 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>(); 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2,m2); 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1); 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.template selfadjointView<Lower>().rankUpdate(m1.row(0),-1); 92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // The following fancy matrix-matrix products are not safe yet regarding static allocation 94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// m1 += m1.template triangularView<Upper>() * m2.col(; 95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// m1.template selfadjointView<Lower>().rankUpdate(m2); 96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// m1 += m1.template triangularView<Upper>() * m2; 97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// m1 += m1.template selfadjointView<Lower>() * m2; 98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_APPROX(m1,m1); 99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar> 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid ctms_decompositions() 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const int maxSize = 16; 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const int size = 12; 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Eigen::Matrix<Scalar, 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::Dynamic, Eigen::Dynamic, 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 0, 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath maxSize, maxSize> Matrix; 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Eigen::Matrix<Scalar, 113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::Dynamic, 1, 114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 0, 115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath maxSize, 1> Vector; 116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Eigen::Matrix<std::complex<Scalar>, 118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::Dynamic, Eigen::Dynamic, 119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 0, 120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath maxSize, maxSize> ComplexMatrix; 121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size)); 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Matrix X(size,size); 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ComplexMatrix complexA(ComplexMatrix::Random(size, size)); 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Matrix saA = A.adjoint() * A; 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Vector b(Vector::Random(size)); 127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Vector x(size); 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Cholesky module 130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::LLT<Matrix> LLT; LLT.compute(A); 131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = LLT.solve(B); 132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = LLT.solve(b); 133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::LDLT<Matrix> LDLT; LDLT.compute(A); 134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = LDLT.solve(B); 135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = LDLT.solve(b); 136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Eigenvalues module 138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp; hessDecomp.compute(complexA); 139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::ComplexSchur<ComplexMatrix> cSchur(size); cSchur.compute(complexA); 140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver; cEigSolver.compute(complexA); 141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::EigenSolver<Matrix> eigSolver; eigSolver.compute(A); 142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size); saEigSolver.compute(saA); 143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::Tridiagonalization<Matrix> tridiag; tridiag.compute(saA); 144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // LU module 146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::PartialPivLU<Matrix> ppLU; ppLU.compute(A); 147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = ppLU.solve(B); 148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = ppLU.solve(b); 149c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::FullPivLU<Matrix> fpLU; fpLU.compute(A); 150c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = fpLU.solve(B); 151c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = fpLU.solve(b); 152c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 153c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // QR module 154c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::HouseholderQR<Matrix> hQR; hQR.compute(A); 155c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = hQR.solve(B); 156c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = hQR.solve(b); 157c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::ColPivHouseholderQR<Matrix> cpQR; cpQR.compute(A); 158c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = cpQR.solve(B); 159c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = cpQR.solve(b); 160c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A); 161c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // FIXME X = fpQR.solve(B); 162c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = fpQR.solve(b); 163c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 164c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // SVD module 165c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV); 166c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 167c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 168615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murrayvoid test_zerosized() { 169615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray // default constructors: 170615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray Eigen::MatrixXd A; 171615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray Eigen::VectorXd v; 172615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray // explicit zero-sized: 173615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray Eigen::ArrayXXd A0(0,0); 174615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray Eigen::ArrayXd v0(std::ptrdiff_t(0)); // FIXME ArrayXd(0) is ambiguous 175615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray 176615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray // assigning empty objects to each other: 177615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray A=A0; 178615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray v=v0; 179615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray} 180615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray 181615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murraytemplate<typename MatrixType> void test_reference(const MatrixType& m) { 182615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray typedef typename MatrixType::Scalar Scalar; 183615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray enum { Flag = MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor}; 184615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor}; 185615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray typename MatrixType::Index rows = m.rows(), cols=m.cols(); 186615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray // Dynamic reference: 187615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray typedef Eigen::Ref<const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag > > Ref; 188615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray typedef Eigen::Ref<const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> > RefT; 189615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray 190615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray Ref r1(m); 191615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray Ref r2(m.block(rows/3, cols/4, rows/2, cols/2)); 192615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray RefT r3(m.transpose()); 193615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray RefT r4(m.topLeftCorner(rows/2, cols/2).transpose()); 194615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray 195615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray VERIFY_RAISES_ASSERT(RefT r5(m)); 196615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray VERIFY_RAISES_ASSERT(Ref r6(m.transpose())); 197615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m)); 198615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray} 199615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray 200c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_nomalloc() 201c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 202c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // check that our operator new is indeed called: 203c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3))); 204c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) ); 205c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2(nomalloc(Matrix4d()) ); 206c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3(nomalloc(Matrix<float,32,32>()) ); 207c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 208c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms) 209c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4(ctms_decompositions<float>()); 210615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray CALL_SUBTEST_5(test_zerosized()); 211615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray CALL_SUBTEST_6(test_reference(Matrix<float,32,32>())); 212c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 213