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// discard stack allocation as that too bypasses malloc 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_STACK_ALLOCATION_LIMIT 0 132b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// heap allocation will raise an assert if enabled at runtime 142b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#define EIGEN_RUNTIME_NO_MALLOC 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "main.h" 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/Cholesky> 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/Eigenvalues> 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/LU> 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/QR> 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/SVD> 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void nomalloc(const MatrixType& m) 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /* this test check no dynamic memory allocation are issued with fixed-size matrices 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Index Index; 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rows = m.rows(); 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index cols = m.cols(); 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1 = MatrixType::Random(rows, cols), 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2 = MatrixType::Random(rows, cols), 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3(rows, cols); 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s1 = internal::random<Scalar>(); 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index r = internal::random<Index>(0, rows-1), 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath c = internal::random<Index>(0, cols-1); 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2); 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c))); 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix()); 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2)); 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() = m1 * m1.col(0); 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint() * m1.col(0); 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1 * m1.row(0).adjoint(); 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint(); 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() = m1.row(0) * m1; 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.row(0) * m1.adjoint(); 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1; 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint(); 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2,m2); 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0); 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0); 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint(); 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint(); 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>(); 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>(); 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>(); 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>(); 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2,m2); 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0); 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0); 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint(); 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint(); 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>(); 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>(); 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>(); 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>(); 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2,m2); 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1); 812b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang m2.template selfadjointView<Upper>().rankUpdate(m1.row(0),-1); 822b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), m1.col(0)); // rank-2 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // The following fancy matrix-matrix products are not safe yet regarding static allocation 852b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang m2.template selfadjointView<Lower>().rankUpdate(m1); 862b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang m2 += m2.template triangularView<Upper>() * m1; 872b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang m2.template triangularView<Upper>() = m2 * m2; 882b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang m1 += m1.template selfadjointView<Lower>() * m2; 892b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang VERIFY_IS_APPROX(m2,m2); 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar> 93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid ctms_decompositions() 94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const int maxSize = 16; 96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const int size = 12; 97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Eigen::Matrix<Scalar, 99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::Dynamic, Eigen::Dynamic, 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 0, 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath maxSize, maxSize> Matrix; 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Eigen::Matrix<Scalar, 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::Dynamic, 1, 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 0, 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath maxSize, 1> Vector; 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Eigen::Matrix<std::complex<Scalar>, 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::Dynamic, Eigen::Dynamic, 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 0, 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath maxSize, maxSize> ComplexMatrix; 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size)); 114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Matrix X(size,size); 115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ComplexMatrix complexA(ComplexMatrix::Random(size, size)); 116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Matrix saA = A.adjoint() * A; 117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Vector b(Vector::Random(size)); 118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Vector x(size); 119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Cholesky module 121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::LLT<Matrix> LLT; LLT.compute(A); 122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = LLT.solve(B); 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = LLT.solve(b); 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::LDLT<Matrix> LDLT; LDLT.compute(A); 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = LDLT.solve(B); 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = LDLT.solve(b); 127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Eigenvalues module 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp; hessDecomp.compute(complexA); 130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::ComplexSchur<ComplexMatrix> cSchur(size); cSchur.compute(complexA); 131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver; cEigSolver.compute(complexA); 132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::EigenSolver<Matrix> eigSolver; eigSolver.compute(A); 133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size); saEigSolver.compute(saA); 134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::Tridiagonalization<Matrix> tridiag; tridiag.compute(saA); 135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // LU module 137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::PartialPivLU<Matrix> ppLU; ppLU.compute(A); 138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = ppLU.solve(B); 139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = ppLU.solve(b); 140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::FullPivLU<Matrix> fpLU; fpLU.compute(A); 141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = fpLU.solve(B); 142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = fpLU.solve(b); 143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // QR module 145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::HouseholderQR<Matrix> hQR; hQR.compute(A); 146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = hQR.solve(B); 147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = hQR.solve(b); 148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::ColPivHouseholderQR<Matrix> cpQR; cpQR.compute(A); 149c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath X = cpQR.solve(B); 150c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = cpQR.solve(b); 151c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A); 152c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // FIXME X = fpQR.solve(B); 153c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath x = fpQR.solve(b); 154c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 155c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // SVD module 156c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV); 157c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 158c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 159615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murrayvoid test_zerosized() { 160615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray // default constructors: 161615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray Eigen::MatrixXd A; 162615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray Eigen::VectorXd v; 163615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray // explicit zero-sized: 164615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray Eigen::ArrayXXd A0(0,0); 1652b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Eigen::ArrayXd v0(0); 166615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray 167615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray // assigning empty objects to each other: 168615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray A=A0; 169615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray v=v0; 170615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray} 171615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray 172615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murraytemplate<typename MatrixType> void test_reference(const MatrixType& m) { 173615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray typedef typename MatrixType::Scalar Scalar; 174615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray enum { Flag = MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor}; 175615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor}; 176615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray typename MatrixType::Index rows = m.rows(), cols=m.cols(); 1772b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag > MatrixX; 1782b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> MatrixXT; 179615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray // Dynamic reference: 1802b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef Eigen::Ref<const MatrixX > Ref; 1812b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef Eigen::Ref<const MatrixXT > RefT; 182615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray 183615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray Ref r1(m); 184615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray Ref r2(m.block(rows/3, cols/4, rows/2, cols/2)); 185615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray RefT r3(m.transpose()); 186615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray RefT r4(m.topLeftCorner(rows/2, cols/2).transpose()); 187615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray 188615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray VERIFY_RAISES_ASSERT(RefT r5(m)); 189615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray VERIFY_RAISES_ASSERT(Ref r6(m.transpose())); 190615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m)); 1912b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1922b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang // Copy constructors shall also never malloc 1932b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Ref r8 = r1; 1942b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang RefT r9 = r3; 1952b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1962b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang // Initializing from a compatible Ref shall also never malloc 1972b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Eigen::Ref<const MatrixX, Unaligned, Stride<Dynamic, Dynamic> > r10=r8, r11=m; 1982b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1992b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang // Initializing from an incompatible Ref will malloc: 2002b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef Eigen::Ref<const MatrixX, Aligned> RefAligned; 2012b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang VERIFY_RAISES_ASSERT(RefAligned r12=r10); 2022b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang VERIFY_RAISES_ASSERT(Ref r13=r10); // r10 has more dynamic strides 2032b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 204615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray} 205615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray 206c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_nomalloc() 207c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 2082b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang // create some dynamic objects 2092b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Eigen::MatrixXd M1 = MatrixXd::Random(3,3); 2102b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Ref<const MatrixXd> R1 = 2.0*M1; // Ref requires temporary 2112b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 2122b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang // from here on prohibit malloc: 2132b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Eigen::internal::set_is_malloc_allowed(false); 2142b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 215c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // check that our operator new is indeed called: 216c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3))); 217c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) ); 218c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2(nomalloc(Matrix4d()) ); 219c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3(nomalloc(Matrix<float,32,32>()) ); 220c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 221c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms) 222c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4(ctms_decompositions<float>()); 2232b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 224615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray CALL_SUBTEST_5(test_zerosized()); 2252b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 226615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray CALL_SUBTEST_6(test_reference(Matrix<float,32,32>())); 2272b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang CALL_SUBTEST_7(test_reference(R1)); 2282b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang CALL_SUBTEST_8(Ref<MatrixXd> R2 = M1.topRows<2>(); test_reference(R2)); 229c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 230