510 lines
17 KiB
C++
510 lines
17 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
|
|
//
|
|
// This Source Code Form is subject to the terms of the Mozilla
|
|
// Public License v. 2.0. If a copy of the MPL was not distributed
|
|
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
|
|
|
|
#ifndef EIGEN_NO_ASSERTION_CHECKING
|
|
#define EIGEN_NO_ASSERTION_CHECKING
|
|
#endif
|
|
|
|
#define TEST_ENABLE_TEMPORARY_TRACKING
|
|
|
|
#include "main.h"
|
|
#include <Eigen/Cholesky>
|
|
#include <Eigen/QR>
|
|
|
|
template<typename MatrixType, int UpLo>
|
|
typename MatrixType::RealScalar matrix_l1_norm(const MatrixType& m) {
|
|
MatrixType symm = m.template selfadjointView<UpLo>();
|
|
return symm.cwiseAbs().colwise().sum().maxCoeff();
|
|
}
|
|
|
|
template<typename MatrixType,template <typename,int> class CholType> void test_chol_update(const MatrixType& symm)
|
|
{
|
|
typedef typename MatrixType::Scalar Scalar;
|
|
typedef typename MatrixType::RealScalar RealScalar;
|
|
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
|
|
|
|
MatrixType symmLo = symm.template triangularView<Lower>();
|
|
MatrixType symmUp = symm.template triangularView<Upper>();
|
|
MatrixType symmCpy = symm;
|
|
|
|
CholType<MatrixType,Lower> chollo(symmLo);
|
|
CholType<MatrixType,Upper> cholup(symmUp);
|
|
|
|
for (int k=0; k<10; ++k)
|
|
{
|
|
VectorType vec = VectorType::Random(symm.rows());
|
|
RealScalar sigma = internal::random<RealScalar>();
|
|
symmCpy += sigma * vec * vec.adjoint();
|
|
|
|
// we are doing some downdates, so it might be the case that the matrix is not SPD anymore
|
|
CholType<MatrixType,Lower> chol(symmCpy);
|
|
if(chol.info()!=Success)
|
|
break;
|
|
|
|
chollo.rankUpdate(vec, sigma);
|
|
VERIFY_IS_APPROX(symmCpy, chollo.reconstructedMatrix());
|
|
|
|
cholup.rankUpdate(vec, sigma);
|
|
VERIFY_IS_APPROX(symmCpy, cholup.reconstructedMatrix());
|
|
}
|
|
}
|
|
|
|
template<typename MatrixType> void cholesky(const MatrixType& m)
|
|
{
|
|
typedef typename MatrixType::Index Index;
|
|
/* this test covers the following files:
|
|
LLT.h LDLT.h
|
|
*/
|
|
Index rows = m.rows();
|
|
Index cols = m.cols();
|
|
|
|
typedef typename MatrixType::Scalar Scalar;
|
|
typedef typename NumTraits<Scalar>::Real RealScalar;
|
|
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
|
|
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
|
|
|
|
MatrixType a0 = MatrixType::Random(rows,cols);
|
|
VectorType vecB = VectorType::Random(rows), vecX(rows);
|
|
MatrixType matB = MatrixType::Random(rows,cols), matX(rows,cols);
|
|
SquareMatrixType symm = a0 * a0.adjoint();
|
|
// let's make sure the matrix is not singular or near singular
|
|
for (int k=0; k<3; ++k)
|
|
{
|
|
MatrixType a1 = MatrixType::Random(rows,cols);
|
|
symm += a1 * a1.adjoint();
|
|
}
|
|
|
|
{
|
|
SquareMatrixType symmUp = symm.template triangularView<Upper>();
|
|
SquareMatrixType symmLo = symm.template triangularView<Lower>();
|
|
|
|
LLT<SquareMatrixType,Lower> chollo(symmLo);
|
|
VERIFY_IS_APPROX(symm, chollo.reconstructedMatrix());
|
|
vecX = chollo.solve(vecB);
|
|
VERIFY_IS_APPROX(symm * vecX, vecB);
|
|
matX = chollo.solve(matB);
|
|
VERIFY_IS_APPROX(symm * matX, matB);
|
|
|
|
const MatrixType symmLo_inverse = chollo.solve(MatrixType::Identity(rows,cols));
|
|
RealScalar rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) /
|
|
matrix_l1_norm<MatrixType, Lower>(symmLo_inverse);
|
|
RealScalar rcond_est = chollo.rcond();
|
|
// Verify that the estimated condition number is within a factor of 10 of the
|
|
// truth.
|
|
VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10);
|
|
|
|
// test the upper mode
|
|
LLT<SquareMatrixType,Upper> cholup(symmUp);
|
|
VERIFY_IS_APPROX(symm, cholup.reconstructedMatrix());
|
|
vecX = cholup.solve(vecB);
|
|
VERIFY_IS_APPROX(symm * vecX, vecB);
|
|
matX = cholup.solve(matB);
|
|
VERIFY_IS_APPROX(symm * matX, matB);
|
|
|
|
// Verify that the estimated condition number is within a factor of 10 of the
|
|
// truth.
|
|
const MatrixType symmUp_inverse = cholup.solve(MatrixType::Identity(rows,cols));
|
|
rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Upper>(symmUp)) /
|
|
matrix_l1_norm<MatrixType, Upper>(symmUp_inverse);
|
|
rcond_est = cholup.rcond();
|
|
VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10);
|
|
|
|
|
|
MatrixType neg = -symmLo;
|
|
chollo.compute(neg);
|
|
VERIFY(chollo.info()==NumericalIssue);
|
|
|
|
VERIFY_IS_APPROX(MatrixType(chollo.matrixL().transpose().conjugate()), MatrixType(chollo.matrixU()));
|
|
VERIFY_IS_APPROX(MatrixType(chollo.matrixU().transpose().conjugate()), MatrixType(chollo.matrixL()));
|
|
VERIFY_IS_APPROX(MatrixType(cholup.matrixL().transpose().conjugate()), MatrixType(cholup.matrixU()));
|
|
VERIFY_IS_APPROX(MatrixType(cholup.matrixU().transpose().conjugate()), MatrixType(cholup.matrixL()));
|
|
|
|
// test some special use cases of SelfCwiseBinaryOp:
|
|
MatrixType m1 = MatrixType::Random(rows,cols), m2(rows,cols);
|
|
m2 = m1;
|
|
m2 += symmLo.template selfadjointView<Lower>().llt().solve(matB);
|
|
VERIFY_IS_APPROX(m2, m1 + symmLo.template selfadjointView<Lower>().llt().solve(matB));
|
|
m2 = m1;
|
|
m2 -= symmLo.template selfadjointView<Lower>().llt().solve(matB);
|
|
VERIFY_IS_APPROX(m2, m1 - symmLo.template selfadjointView<Lower>().llt().solve(matB));
|
|
m2 = m1;
|
|
m2.noalias() += symmLo.template selfadjointView<Lower>().llt().solve(matB);
|
|
VERIFY_IS_APPROX(m2, m1 + symmLo.template selfadjointView<Lower>().llt().solve(matB));
|
|
m2 = m1;
|
|
m2.noalias() -= symmLo.template selfadjointView<Lower>().llt().solve(matB);
|
|
VERIFY_IS_APPROX(m2, m1 - symmLo.template selfadjointView<Lower>().llt().solve(matB));
|
|
}
|
|
|
|
// LDLT
|
|
{
|
|
int sign = internal::random<int>()%2 ? 1 : -1;
|
|
|
|
if(sign == -1)
|
|
{
|
|
symm = -symm; // test a negative matrix
|
|
}
|
|
|
|
SquareMatrixType symmUp = symm.template triangularView<Upper>();
|
|
SquareMatrixType symmLo = symm.template triangularView<Lower>();
|
|
|
|
LDLT<SquareMatrixType,Lower> ldltlo(symmLo);
|
|
VERIFY(ldltlo.info()==Success);
|
|
VERIFY_IS_APPROX(symm, ldltlo.reconstructedMatrix());
|
|
vecX = ldltlo.solve(vecB);
|
|
VERIFY_IS_APPROX(symm * vecX, vecB);
|
|
matX = ldltlo.solve(matB);
|
|
VERIFY_IS_APPROX(symm * matX, matB);
|
|
|
|
const MatrixType symmLo_inverse = ldltlo.solve(MatrixType::Identity(rows,cols));
|
|
RealScalar rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) /
|
|
matrix_l1_norm<MatrixType, Lower>(symmLo_inverse);
|
|
RealScalar rcond_est = ldltlo.rcond();
|
|
// Verify that the estimated condition number is within a factor of 10 of the
|
|
// truth.
|
|
VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10);
|
|
|
|
|
|
LDLT<SquareMatrixType,Upper> ldltup(symmUp);
|
|
VERIFY(ldltup.info()==Success);
|
|
VERIFY_IS_APPROX(symm, ldltup.reconstructedMatrix());
|
|
vecX = ldltup.solve(vecB);
|
|
VERIFY_IS_APPROX(symm * vecX, vecB);
|
|
matX = ldltup.solve(matB);
|
|
VERIFY_IS_APPROX(symm * matX, matB);
|
|
|
|
// Verify that the estimated condition number is within a factor of 10 of the
|
|
// truth.
|
|
const MatrixType symmUp_inverse = ldltup.solve(MatrixType::Identity(rows,cols));
|
|
rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Upper>(symmUp)) /
|
|
matrix_l1_norm<MatrixType, Upper>(symmUp_inverse);
|
|
rcond_est = ldltup.rcond();
|
|
VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10);
|
|
|
|
VERIFY_IS_APPROX(MatrixType(ldltlo.matrixL().transpose().conjugate()), MatrixType(ldltlo.matrixU()));
|
|
VERIFY_IS_APPROX(MatrixType(ldltlo.matrixU().transpose().conjugate()), MatrixType(ldltlo.matrixL()));
|
|
VERIFY_IS_APPROX(MatrixType(ldltup.matrixL().transpose().conjugate()), MatrixType(ldltup.matrixU()));
|
|
VERIFY_IS_APPROX(MatrixType(ldltup.matrixU().transpose().conjugate()), MatrixType(ldltup.matrixL()));
|
|
|
|
if(MatrixType::RowsAtCompileTime==Dynamic)
|
|
{
|
|
// note : each inplace permutation requires a small temporary vector (mask)
|
|
|
|
// check inplace solve
|
|
matX = matB;
|
|
VERIFY_EVALUATION_COUNT(matX = ldltlo.solve(matX), 0);
|
|
VERIFY_IS_APPROX(matX, ldltlo.solve(matB).eval());
|
|
|
|
|
|
matX = matB;
|
|
VERIFY_EVALUATION_COUNT(matX = ldltup.solve(matX), 0);
|
|
VERIFY_IS_APPROX(matX, ldltup.solve(matB).eval());
|
|
}
|
|
|
|
// restore
|
|
if(sign == -1)
|
|
symm = -symm;
|
|
|
|
// check matrices coming from linear constraints with Lagrange multipliers
|
|
if(rows>=3)
|
|
{
|
|
SquareMatrixType A = symm;
|
|
Index c = internal::random<Index>(0,rows-2);
|
|
A.bottomRightCorner(c,c).setZero();
|
|
// Make sure a solution exists:
|
|
vecX.setRandom();
|
|
vecB = A * vecX;
|
|
vecX.setZero();
|
|
ldltlo.compute(A);
|
|
VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
|
|
vecX = ldltlo.solve(vecB);
|
|
VERIFY_IS_APPROX(A * vecX, vecB);
|
|
}
|
|
|
|
// check non-full rank matrices
|
|
if(rows>=3)
|
|
{
|
|
Index r = internal::random<Index>(1,rows-1);
|
|
Matrix<Scalar,Dynamic,Dynamic> a = Matrix<Scalar,Dynamic,Dynamic>::Random(rows,r);
|
|
SquareMatrixType A = a * a.adjoint();
|
|
// Make sure a solution exists:
|
|
vecX.setRandom();
|
|
vecB = A * vecX;
|
|
vecX.setZero();
|
|
ldltlo.compute(A);
|
|
VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
|
|
vecX = ldltlo.solve(vecB);
|
|
VERIFY_IS_APPROX(A * vecX, vecB);
|
|
}
|
|
|
|
// check matrices with a wide spectrum
|
|
if(rows>=3)
|
|
{
|
|
using std::pow;
|
|
using std::sqrt;
|
|
RealScalar s = (std::min)(16,std::numeric_limits<RealScalar>::max_exponent10/8);
|
|
Matrix<Scalar,Dynamic,Dynamic> a = Matrix<Scalar,Dynamic,Dynamic>::Random(rows,rows);
|
|
Matrix<RealScalar,Dynamic,1> d = Matrix<RealScalar,Dynamic,1>::Random(rows);
|
|
for(Index k=0; k<rows; ++k)
|
|
d(k) = d(k)*pow(RealScalar(10),internal::random<RealScalar>(-s,s));
|
|
SquareMatrixType A = a * d.asDiagonal() * a.adjoint();
|
|
// Make sure a solution exists:
|
|
vecX.setRandom();
|
|
vecB = A * vecX;
|
|
vecX.setZero();
|
|
ldltlo.compute(A);
|
|
VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
|
|
vecX = ldltlo.solve(vecB);
|
|
|
|
if(ldltlo.vectorD().real().cwiseAbs().minCoeff()>RealScalar(0))
|
|
{
|
|
VERIFY_IS_APPROX(A * vecX,vecB);
|
|
}
|
|
else
|
|
{
|
|
RealScalar large_tol = sqrt(test_precision<RealScalar>());
|
|
VERIFY((A * vecX).isApprox(vecB, large_tol));
|
|
|
|
++g_test_level;
|
|
VERIFY_IS_APPROX(A * vecX,vecB);
|
|
--g_test_level;
|
|
}
|
|
}
|
|
}
|
|
|
|
// update/downdate
|
|
CALL_SUBTEST(( test_chol_update<SquareMatrixType,LLT>(symm) ));
|
|
CALL_SUBTEST(( test_chol_update<SquareMatrixType,LDLT>(symm) ));
|
|
}
|
|
|
|
template<typename MatrixType> void cholesky_cplx(const MatrixType& m)
|
|
{
|
|
// classic test
|
|
cholesky(m);
|
|
|
|
// test mixing real/scalar types
|
|
|
|
typedef typename MatrixType::Index Index;
|
|
|
|
Index rows = m.rows();
|
|
Index cols = m.cols();
|
|
|
|
typedef typename MatrixType::Scalar Scalar;
|
|
typedef typename NumTraits<Scalar>::Real RealScalar;
|
|
typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> RealMatrixType;
|
|
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
|
|
|
|
RealMatrixType a0 = RealMatrixType::Random(rows,cols);
|
|
VectorType vecB = VectorType::Random(rows), vecX(rows);
|
|
MatrixType matB = MatrixType::Random(rows,cols), matX(rows,cols);
|
|
RealMatrixType symm = a0 * a0.adjoint();
|
|
// let's make sure the matrix is not singular or near singular
|
|
for (int k=0; k<3; ++k)
|
|
{
|
|
RealMatrixType a1 = RealMatrixType::Random(rows,cols);
|
|
symm += a1 * a1.adjoint();
|
|
}
|
|
|
|
{
|
|
RealMatrixType symmLo = symm.template triangularView<Lower>();
|
|
|
|
LLT<RealMatrixType,Lower> chollo(symmLo);
|
|
VERIFY_IS_APPROX(symm, chollo.reconstructedMatrix());
|
|
vecX = chollo.solve(vecB);
|
|
VERIFY_IS_APPROX(symm * vecX, vecB);
|
|
// matX = chollo.solve(matB);
|
|
// VERIFY_IS_APPROX(symm * matX, matB);
|
|
}
|
|
|
|
// LDLT
|
|
{
|
|
int sign = internal::random<int>()%2 ? 1 : -1;
|
|
|
|
if(sign == -1)
|
|
{
|
|
symm = -symm; // test a negative matrix
|
|
}
|
|
|
|
RealMatrixType symmLo = symm.template triangularView<Lower>();
|
|
|
|
LDLT<RealMatrixType,Lower> ldltlo(symmLo);
|
|
VERIFY(ldltlo.info()==Success);
|
|
VERIFY_IS_APPROX(symm, ldltlo.reconstructedMatrix());
|
|
vecX = ldltlo.solve(vecB);
|
|
VERIFY_IS_APPROX(symm * vecX, vecB);
|
|
// matX = ldltlo.solve(matB);
|
|
// VERIFY_IS_APPROX(symm * matX, matB);
|
|
}
|
|
}
|
|
|
|
// regression test for bug 241
|
|
template<typename MatrixType> void cholesky_bug241(const MatrixType& m)
|
|
{
|
|
eigen_assert(m.rows() == 2 && m.cols() == 2);
|
|
|
|
typedef typename MatrixType::Scalar Scalar;
|
|
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
|
|
|
|
MatrixType matA;
|
|
matA << 1, 1, 1, 1;
|
|
VectorType vecB;
|
|
vecB << 1, 1;
|
|
VectorType vecX = matA.ldlt().solve(vecB);
|
|
VERIFY_IS_APPROX(matA * vecX, vecB);
|
|
}
|
|
|
|
// LDLT is not guaranteed to work for indefinite matrices, but happens to work fine if matrix is diagonal.
|
|
// This test checks that LDLT reports correctly that matrix is indefinite.
|
|
// See http://forum.kde.org/viewtopic.php?f=74&t=106942 and bug 736
|
|
template<typename MatrixType> void cholesky_definiteness(const MatrixType& m)
|
|
{
|
|
eigen_assert(m.rows() == 2 && m.cols() == 2);
|
|
MatrixType mat;
|
|
LDLT<MatrixType> ldlt(2);
|
|
|
|
{
|
|
mat << 1, 0, 0, -1;
|
|
ldlt.compute(mat);
|
|
VERIFY(ldlt.info()==Success);
|
|
VERIFY(!ldlt.isNegative());
|
|
VERIFY(!ldlt.isPositive());
|
|
}
|
|
{
|
|
mat << 1, 2, 2, 1;
|
|
ldlt.compute(mat);
|
|
VERIFY(ldlt.info()==Success);
|
|
VERIFY(!ldlt.isNegative());
|
|
VERIFY(!ldlt.isPositive());
|
|
}
|
|
{
|
|
mat << 0, 0, 0, 0;
|
|
ldlt.compute(mat);
|
|
VERIFY(ldlt.info()==Success);
|
|
VERIFY(ldlt.isNegative());
|
|
VERIFY(ldlt.isPositive());
|
|
}
|
|
{
|
|
mat << 0, 0, 0, 1;
|
|
ldlt.compute(mat);
|
|
VERIFY(ldlt.info()==Success);
|
|
VERIFY(!ldlt.isNegative());
|
|
VERIFY(ldlt.isPositive());
|
|
}
|
|
{
|
|
mat << -1, 0, 0, 0;
|
|
ldlt.compute(mat);
|
|
VERIFY(ldlt.info()==Success);
|
|
VERIFY(ldlt.isNegative());
|
|
VERIFY(!ldlt.isPositive());
|
|
}
|
|
}
|
|
|
|
template<typename>
|
|
void cholesky_faillure_cases()
|
|
{
|
|
MatrixXd mat;
|
|
LDLT<MatrixXd> ldlt;
|
|
|
|
{
|
|
mat.resize(2,2);
|
|
mat << 0, 1, 1, 0;
|
|
ldlt.compute(mat);
|
|
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
|
|
VERIFY(ldlt.info()==NumericalIssue);
|
|
}
|
|
#if (!EIGEN_ARCH_i386) || defined(EIGEN_VECTORIZE_SSE2)
|
|
{
|
|
mat.resize(3,3);
|
|
mat << -1, -3, 3,
|
|
-3, -8.9999999999999999999, 1,
|
|
3, 1, 0;
|
|
ldlt.compute(mat);
|
|
VERIFY(ldlt.info()==NumericalIssue);
|
|
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
|
|
}
|
|
#endif
|
|
{
|
|
mat.resize(3,3);
|
|
mat << 1, 2, 3,
|
|
2, 4, 1,
|
|
3, 1, 0;
|
|
ldlt.compute(mat);
|
|
VERIFY(ldlt.info()==NumericalIssue);
|
|
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
|
|
}
|
|
|
|
{
|
|
mat.resize(8,8);
|
|
mat << 0.1, 0, -0.1, 0, 0, 0, 1, 0,
|
|
0, 4.24667, 0, 2.00333, 0, 0, 0, 0,
|
|
-0.1, 0, 0.2, 0, -0.1, 0, 0, 0,
|
|
0, 2.00333, 0, 8.49333, 0, 2.00333, 0, 0,
|
|
0, 0, -0.1, 0, 0.1, 0, 0, 1,
|
|
0, 0, 0, 2.00333, 0, 4.24667, 0, 0,
|
|
1, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 1, 0, 0, 0;
|
|
ldlt.compute(mat);
|
|
VERIFY(ldlt.info()==NumericalIssue);
|
|
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
|
|
}
|
|
}
|
|
|
|
template<typename MatrixType> void cholesky_verify_assert()
|
|
{
|
|
MatrixType tmp;
|
|
|
|
LLT<MatrixType> llt;
|
|
VERIFY_RAISES_ASSERT(llt.matrixL())
|
|
VERIFY_RAISES_ASSERT(llt.matrixU())
|
|
VERIFY_RAISES_ASSERT(llt.solve(tmp))
|
|
VERIFY_RAISES_ASSERT(llt.solveInPlace(&tmp))
|
|
|
|
LDLT<MatrixType> ldlt;
|
|
VERIFY_RAISES_ASSERT(ldlt.matrixL())
|
|
VERIFY_RAISES_ASSERT(ldlt.permutationP())
|
|
VERIFY_RAISES_ASSERT(ldlt.vectorD())
|
|
VERIFY_RAISES_ASSERT(ldlt.isPositive())
|
|
VERIFY_RAISES_ASSERT(ldlt.isNegative())
|
|
VERIFY_RAISES_ASSERT(ldlt.solve(tmp))
|
|
VERIFY_RAISES_ASSERT(ldlt.solveInPlace(&tmp))
|
|
}
|
|
|
|
void test_cholesky()
|
|
{
|
|
int s = 0;
|
|
for(int i = 0; i < g_repeat; i++) {
|
|
CALL_SUBTEST_1( cholesky(Matrix<double,1,1>()) );
|
|
CALL_SUBTEST_3( cholesky(Matrix2d()) );
|
|
CALL_SUBTEST_3( cholesky_bug241(Matrix2d()) );
|
|
CALL_SUBTEST_3( cholesky_definiteness(Matrix2d()) );
|
|
CALL_SUBTEST_4( cholesky(Matrix3f()) );
|
|
CALL_SUBTEST_5( cholesky(Matrix4d()) );
|
|
|
|
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
|
|
CALL_SUBTEST_2( cholesky(MatrixXd(s,s)) );
|
|
TEST_SET_BUT_UNUSED_VARIABLE(s)
|
|
|
|
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
|
|
CALL_SUBTEST_6( cholesky_cplx(MatrixXcd(s,s)) );
|
|
TEST_SET_BUT_UNUSED_VARIABLE(s)
|
|
}
|
|
|
|
CALL_SUBTEST_4( cholesky_verify_assert<Matrix3f>() );
|
|
CALL_SUBTEST_7( cholesky_verify_assert<Matrix3d>() );
|
|
CALL_SUBTEST_8( cholesky_verify_assert<MatrixXf>() );
|
|
CALL_SUBTEST_2( cholesky_verify_assert<MatrixXd>() );
|
|
|
|
// Test problem size constructors
|
|
CALL_SUBTEST_9( LLT<MatrixXf>(10) );
|
|
CALL_SUBTEST_9( LDLT<MatrixXf>(10) );
|
|
|
|
CALL_SUBTEST_2( cholesky_faillure_cases<void>() );
|
|
|
|
TEST_SET_BUT_UNUSED_VARIABLE(nb_temporaries)
|
|
}
|