232 lines
8.3 KiB
C++
232 lines
8.3 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
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// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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// discard stack allocation as that too bypasses malloc
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#define EIGEN_STACK_ALLOCATION_LIMIT 0
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// heap allocation will raise an assert if enabled at runtime
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#define EIGEN_RUNTIME_NO_MALLOC
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#include "main.h"
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#include <Eigen/Cholesky>
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#include <Eigen/Eigenvalues>
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#include <Eigen/LU>
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#include <Eigen/QR>
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#include <Eigen/SVD>
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template <typename MatrixType>
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void nomalloc(const MatrixType& m) {
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/* this test check no dynamic memory allocation are issued with fixed-size matrices
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*/
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typedef typename MatrixType::Scalar Scalar;
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Index rows = m.rows();
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Index cols = m.cols();
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MatrixType m1 = MatrixType::Random(rows, cols), m2 = MatrixType::Random(rows, cols), m3(rows, cols);
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Scalar s1 = internal::random<Scalar>();
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Index r = internal::random<Index>(0, rows - 1), c = internal::random<Index>(0, cols - 1);
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VERIFY_IS_APPROX((m1 + m2) * s1, s1 * m1 + s1 * m2);
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VERIFY_IS_APPROX((m1 + m2)(r, c), (m1(r, c)) + (m2(r, c)));
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VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0, 0, rows, cols)), (m1.array() * m1.array()).matrix());
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VERIFY_IS_APPROX((m1 * m1.transpose()) * m2, m1 * (m1.transpose() * m2));
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m2.col(0).noalias() = m1 * m1.col(0);
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m2.col(0).noalias() -= m1.adjoint() * m1.col(0);
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m2.col(0).noalias() -= m1 * m1.row(0).adjoint();
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m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint();
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m2.row(0).noalias() = m1.row(0) * m1;
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m2.row(0).noalias() -= m1.row(0) * m1.adjoint();
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1;
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint();
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VERIFY_IS_APPROX(m2, m2);
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m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0);
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m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0);
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m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint();
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m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint();
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m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>();
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m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>();
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>();
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>();
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VERIFY_IS_APPROX(m2, m2);
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m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0);
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m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0);
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m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint();
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m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint();
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m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>();
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m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>();
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>();
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>();
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VERIFY_IS_APPROX(m2, m2);
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m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), -1);
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m2.template selfadjointView<Upper>().rankUpdate(m1.row(0), -1);
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m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), m1.col(0)); // rank-2
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// The following fancy matrix-matrix products are not safe yet regarding static allocation
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m2.template selfadjointView<Lower>().rankUpdate(m1);
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m2 += m2.template triangularView<Upper>() * m1;
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m2.template triangularView<Upper>() = m2 * m2;
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m1 += m1.template selfadjointView<Lower>() * m2;
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VERIFY_IS_APPROX(m2, m2);
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}
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template <typename Scalar>
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void ctms_decompositions() {
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const int maxSize = 16;
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const int size = 12;
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typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, 0, maxSize, maxSize> Matrix;
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typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1, 0, maxSize, 1> Vector;
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typedef Eigen::Matrix<std::complex<Scalar>, Eigen::Dynamic, Eigen::Dynamic, 0, maxSize, maxSize> ComplexMatrix;
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const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size));
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Matrix X(size, size);
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const ComplexMatrix complexA(ComplexMatrix::Random(size, size));
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const Matrix saA = A.adjoint() * A;
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const Vector b(Vector::Random(size));
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Vector x(size);
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// Cholesky module
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Eigen::LLT<Matrix> LLT;
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LLT.compute(A);
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X = LLT.solve(B);
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x = LLT.solve(b);
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Eigen::LDLT<Matrix> LDLT;
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LDLT.compute(A);
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X = LDLT.solve(B);
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x = LDLT.solve(b);
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// Eigenvalues module
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Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp;
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hessDecomp.compute(complexA);
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Eigen::ComplexSchur<ComplexMatrix> cSchur(size);
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cSchur.compute(complexA);
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Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver;
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cEigSolver.compute(complexA);
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Eigen::EigenSolver<Matrix> eigSolver;
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eigSolver.compute(A);
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Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size);
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saEigSolver.compute(saA);
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Eigen::Tridiagonalization<Matrix> tridiag;
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tridiag.compute(saA);
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// LU module
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Eigen::PartialPivLU<Matrix> ppLU;
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ppLU.compute(A);
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X = ppLU.solve(B);
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x = ppLU.solve(b);
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Eigen::FullPivLU<Matrix> fpLU;
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fpLU.compute(A);
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X = fpLU.solve(B);
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x = fpLU.solve(b);
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// QR module
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Eigen::HouseholderQR<Matrix> hQR;
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hQR.compute(A);
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X = hQR.solve(B);
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x = hQR.solve(b);
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Eigen::ColPivHouseholderQR<Matrix> cpQR;
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cpQR.compute(A);
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X = cpQR.solve(B);
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x = cpQR.solve(b);
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Eigen::FullPivHouseholderQR<Matrix> fpQR;
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fpQR.compute(A);
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// FIXME X = fpQR.solve(B);
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x = fpQR.solve(b);
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// SVD module
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Eigen::JacobiSVD<Matrix, ComputeFullU | ComputeFullV> jSVD;
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jSVD.compute(A);
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}
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void test_zerosized() {
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// default constructors:
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Eigen::MatrixXd A;
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Eigen::VectorXd v;
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// explicit zero-sized:
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Eigen::ArrayXXd A0(0, 0);
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Eigen::ArrayXd v0(0);
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// assigning empty objects to each other:
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A = A0;
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v = v0;
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}
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template <typename MatrixType>
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void test_reference(const MatrixType& m) {
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typedef typename MatrixType::Scalar Scalar;
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enum { Flag = MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor };
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enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor };
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Index rows = m.rows(), cols = m.cols();
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typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag> MatrixX;
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typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> MatrixXT;
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// Dynamic reference:
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typedef Eigen::Ref<const MatrixX> Ref;
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typedef Eigen::Ref<const MatrixXT> RefT;
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Ref r1(m);
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Ref r2(m.block(rows / 3, cols / 4, rows / 2, cols / 2));
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RefT r3(m.transpose());
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RefT r4(m.topLeftCorner(rows / 2, cols / 2).transpose());
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VERIFY_RAISES_ASSERT(RefT r5(m));
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VERIFY_RAISES_ASSERT(Ref r6(m.transpose()));
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VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m));
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// Copy constructors shall also never malloc
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Ref r8 = r1;
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RefT r9 = r3;
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// Initializing from a compatible Ref shall also never malloc
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Eigen::Ref<const MatrixX, Unaligned, Stride<Dynamic, Dynamic> > r10 = r8, r11 = m;
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// Initializing from an incompatible Ref will malloc:
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typedef Eigen::Ref<const MatrixX, Aligned> RefAligned;
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VERIFY_RAISES_ASSERT(RefAligned r12 = r10);
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VERIFY_RAISES_ASSERT(Ref r13 = r10); // r10 has more dynamic strides
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}
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EIGEN_DECLARE_TEST(nomalloc) {
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// create some dynamic objects
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Eigen::MatrixXd M1 = MatrixXd::Random(3, 3);
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Ref<const MatrixXd> R1 = 2.0 * M1; // Ref requires temporary
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// from here on prohibit malloc:
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Eigen::internal::set_is_malloc_allowed(false);
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// check that our operator new is indeed called:
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VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3, 3)));
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CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()));
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CALL_SUBTEST_2(nomalloc(Matrix4d()));
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CALL_SUBTEST_3(nomalloc(Matrix<float, 32, 32>()));
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// Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms)
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CALL_SUBTEST_4(ctms_decompositions<float>());
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CALL_SUBTEST_5(test_zerosized());
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CALL_SUBTEST_6(test_reference(Matrix<float, 32, 32>()));
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CALL_SUBTEST_7(test_reference(R1));
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CALL_SUBTEST_8(Ref<MatrixXd> R2 = M1.topRows<2>(); test_reference(R2));
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// freeing is now possible
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Eigen::internal::set_is_malloc_allowed(true);
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}
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