nomalloc.cpp File Reference

#include "main.h"
#include <Eigen/Cholesky>
#include <Eigen/Eigenvalues>
#include <Eigen/LU>
#include <Eigen/QR>
#include <Eigen/SVD>

Macros
#define	EIGEN_STACK_ALLOCATION_LIMIT   0
#define	EIGEN_RUNTIME_NO_MALLOC

Functions
template<typename MatrixType >
void	nomalloc (const MatrixType &m)
template<typename Scalar >
void	ctms_decompositions ()
void	test_zerosized ()
template<typename MatrixType >
void	test_reference (const MatrixType &m)
	EIGEN_DECLARE_TEST (nomalloc)

Macro Definition Documentation

EIGEN_RUNTIME_NO_MALLOC

#define EIGEN_RUNTIME_NO_MALLOC

EIGEN_STACK_ALLOCATION_LIMIT

#define EIGEN_STACK_ALLOCATION_LIMIT   0

Function Documentation

ctms_decompositions()

template<typename Scalar >

void ctms_decompositions

(

)

                           {
  const int maxSize = 16;
  const int size = 12;
 
  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, 0, maxSize, maxSize> Matrix;
 
  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1, 0, maxSize, 1> Vector;
 
  typedef Eigen::Matrix<std::complex<Scalar>, Eigen::Dynamic, Eigen::Dynamic, 0, maxSize, maxSize> ComplexMatrix;
 
  const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size));
  Matrix X(size, size);
  const ComplexMatrix complexA(ComplexMatrix::Random(size, size));
  const Matrix saA = A.adjoint() * A;
  const Vector b(Vector::Random(size));
  Vector x(size);
 
  // Cholesky module
  Eigen::LLT<Matrix> LLT;
  LLT.compute(A);
  X = LLT.solve(B);
  x = LLT.solve(b);
  Eigen::LDLT<Matrix> LDLT;
  LDLT.compute(A);
  X = LDLT.solve(B);
  x = LDLT.solve(b);
 
  // Eigenvalues module
  Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp;
  hessDecomp.compute(complexA);
  Eigen::ComplexSchur<ComplexMatrix> cSchur(size);
  cSchur.compute(complexA);
  Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver;
  cEigSolver.compute(complexA);
  Eigen::EigenSolver<Matrix> eigSolver;
  eigSolver.compute(A);
  Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size);
  saEigSolver.compute(saA);
  Eigen::Tridiagonalization<Matrix> tridiag;
  tridiag.compute(saA);
 
  // LU module
  Eigen::PartialPivLU<Matrix> ppLU;
  ppLU.compute(A);
  X = ppLU.solve(B);
  x = ppLU.solve(b);
  Eigen::FullPivLU<Matrix> fpLU;
  fpLU.compute(A);
  X = fpLU.solve(B);
  x = fpLU.solve(b);
 
  // QR module
  Eigen::HouseholderQR<Matrix> hQR;
  hQR.compute(A);
  X = hQR.solve(B);
  x = hQR.solve(b);
  Eigen::ColPivHouseholderQR<Matrix> cpQR;
  cpQR.compute(A);
  X = cpQR.solve(B);
  x = cpQR.solve(b);
  Eigen::FullPivHouseholderQR<Matrix> fpQR;
  fpQR.compute(A);
  // FIXME X = fpQR.solve(B);
  x = fpQR.solve(b);
 
  // SVD module
  Eigen::JacobiSVD<Matrix, ComputeFullU | ComputeFullV> jSVD;
  jSVD.compute(A);
}

References b, Eigen::LDLT< MatrixType_, UpLo_ >::compute(), Eigen::LLT< MatrixType_, UpLo_ >::compute(), Eigen::HessenbergDecomposition< MatrixType_ >::compute(), Eigen::Tridiagonalization< MatrixType_ >::compute(), Eigen::FullPivLU< MatrixType_, PermutationIndex_ >::compute(), Eigen::PartialPivLU< MatrixType_, PermutationIndex_ >::compute(), Eigen::ColPivHouseholderQR< MatrixType_, PermutationIndex_ >::compute(), Eigen::FullPivHouseholderQR< MatrixType_, PermutationIndex_ >::compute(), Eigen::HouseholderQR< MatrixType_ >::compute(), Eigen::ComplexEigenSolver< MatrixType_ >::compute(), Eigen::EigenSolver< MatrixType_ >::compute(), Eigen::ComplexSchur< MatrixType_ >::compute(), Eigen::SelfAdjointEigenSolver< MatrixType_ >::compute(), Eigen::JacobiSVD< MatrixType_, Options_ >::compute(), Eigen::Dynamic, size, Eigen::SolverBase< Derived >::solve(), plotDoE::x, and X.

EIGEN_DECLARE_TEST()

EIGEN_DECLARE_TEST

(

nomalloc

)

                             {
  // create some dynamic objects
  Eigen::MatrixXd M1 = MatrixXd::Random(3, 3);
  Ref<const MatrixXd> R1 = 2.0 * M1;  // Ref requires temporary
 
  // from here on prohibit malloc:
  Eigen::internal::set_is_malloc_allowed(false);
 
  // check that our operator new is indeed called:
  VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3, 3)));
  CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()));
  CALL_SUBTEST_2(nomalloc(Matrix4d()));
  CALL_SUBTEST_3(nomalloc(Matrix<float, 32, 32>()));
 
  // Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms)
  CALL_SUBTEST_4(ctms_decompositions<float>());
 
  CALL_SUBTEST_5(test_zerosized());
 
  CALL_SUBTEST_6(test_reference(Matrix<float, 32, 32>()));
  CALL_SUBTEST_7(test_reference(R1));
  CALL_SUBTEST_8(Ref<MatrixXd> R2 = M1.topRows<2>(); test_reference(R2));
 
  // freeing is now possible
  Eigen::internal::set_is_malloc_allowed(true);
}

References CALL_SUBTEST_1, CALL_SUBTEST_2, CALL_SUBTEST_3, CALL_SUBTEST_4, CALL_SUBTEST_5, CALL_SUBTEST_6, CALL_SUBTEST_7, CALL_SUBTEST_8, M1, nomalloc(), test_reference(), test_zerosized(), and VERIFY_RAISES_ASSERT.

nomalloc()

template<typename MatrixType >

void nomalloc

(

const MatrixType &

m

)

                                   {
  /* this test check no dynamic memory allocation are issued with fixed-size matrices
   */
  typedef typename MatrixType::Scalar Scalar;
 
  Index rows = m.rows();
  Index cols = m.cols();
 
  MatrixType m1 = MatrixType::Random(rows, cols), m2 = MatrixType::Random(rows, cols), m3(rows, cols);
 
  Scalar s1 = internal::random<Scalar>();
 
  Index r = internal::random<Index>(0, rows - 1), c = internal::random<Index>(0, cols - 1);
 
  VERIFY_IS_APPROX((m1 + m2) * s1, s1 * m1 + s1 * m2);
  VERIFY_IS_APPROX((m1 + m2)(r, c), (m1(r, c)) + (m2(r, c)));
  VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0, 0, rows, cols)), (m1.array() * m1.array()).matrix());
  VERIFY_IS_APPROX((m1 * m1.transpose()) * m2, m1 * (m1.transpose() * m2));
 
  m2.col(0).noalias() = m1 * m1.col(0);
  m2.col(0).noalias() -= m1.adjoint() * m1.col(0);
  m2.col(0).noalias() -= m1 * m1.row(0).adjoint();
  m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint();
 
  m2.row(0).noalias() = m1.row(0) * m1;
  m2.row(0).noalias() -= m1.row(0) * m1.adjoint();
  m2.row(0).noalias() -= m1.col(0).adjoint() * m1;
  m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint();
  VERIFY_IS_APPROX(m2, m2);
 
  m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0);
  m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0);
  m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint();
  m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint();
 
  m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>();
  m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>();
  m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>();
  m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>();
  VERIFY_IS_APPROX(m2, m2);
 
  m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0);
  m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0);
  m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint();
  m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint();
 
  m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>();
  m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>();
  m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>();
  m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>();
  VERIFY_IS_APPROX(m2, m2);
 
  m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), -1);
  m2.template selfadjointView<Upper>().rankUpdate(m1.row(0), -1);
  m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), m1.col(0));  // rank-2
 
  // The following fancy matrix-matrix products are not safe yet regarding static allocation
  m2.template selfadjointView<Lower>().rankUpdate(m1);
  m2 += m2.template triangularView<Upper>() * m1;
  m2.template triangularView<Upper>() = m2 * m2;
  m1 += m1.template selfadjointView<Lower>() * m2;
  VERIFY_IS_APPROX(m2, m2);
}

References calibrate::c, cols, m, m1, m2(), UniformPSDSelfTest::r, rows, and VERIFY_IS_APPROX.

Referenced by EIGEN_DECLARE_TEST().

test_reference()

template<typename MatrixType >

void test_reference

(

const MatrixType &

m

)

                                         {
  typedef typename MatrixType::Scalar Scalar;
  enum { Flag = MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor };
  enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor };
  Index rows = m.rows(), cols = m.cols();
  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag> MatrixX;
  typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> MatrixXT;
  // Dynamic reference:
  typedef Eigen::Ref<const MatrixX> Ref;
  typedef Eigen::Ref<const MatrixXT> RefT;
 
  Ref r1(m);
  Ref r2(m.block(rows / 3, cols / 4, rows / 2, cols / 2));
  RefT r3(m.transpose());
  RefT r4(m.topLeftCorner(rows / 2, cols / 2).transpose());
 
  VERIFY_RAISES_ASSERT(RefT r5(m));
  VERIFY_RAISES_ASSERT(Ref r6(m.transpose()));
  VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m));
 
  // Copy constructors shall also never malloc
  Ref r8 = r1;
  RefT r9 = r3;
 
  // Initializing from a compatible Ref shall also never malloc
  Eigen::Ref<const MatrixX, Unaligned, Stride<Dynamic, Dynamic> > r10 = r8, r11 = m;
 
  // Initializing from an incompatible Ref will malloc:
  typedef Eigen::Ref<const MatrixX, Aligned> RefAligned;
  VERIFY_RAISES_ASSERT(RefAligned r12 = r10);
  VERIFY_RAISES_ASSERT(Ref r13 = r10);  // r10 has more dynamic strides
}

References Eigen::ColMajor, cols, oomph::PressureAdvectionDiffusionValidation::Flag, m, Eigen::RowMajor, rows, and VERIFY_RAISES_ASSERT.

Referenced by EIGEN_DECLARE_TEST().

test_zerosized()

void test_zerosized

(

)

                      {
  // default constructors:
  Eigen::MatrixXd A;
  Eigen::VectorXd v;
  // explicit zero-sized:
  Eigen::ArrayXXd A0(0, 0);
  Eigen::ArrayXd v0(0);
 
  // assigning empty objects to each other:
  A = A0;
  v = v0;
}

References v.

Referenced by EIGEN_DECLARE_TEST().