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choelzl
2022-04-07 18:46:57 +02:00
parent 88cb3426ad
commit 15e7120d6d
5316 changed files with 4563444 additions and 6 deletions
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// 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) {
if(m.cols()==0) return typename MatrixType::RealScalar(0);
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)
{
/* 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(neg.size()==0 || 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
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());
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
}
{
mat << 1, 2, 2, 1;
ldlt.compute(mat);
VERIFY(ldlt.info()==Success);
VERIFY(!ldlt.isNegative());
VERIFY(!ldlt.isPositive());
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
}
{
mat << 0, 0, 0, 0;
ldlt.compute(mat);
VERIFY(ldlt.info()==Success);
VERIFY(ldlt.isNegative());
VERIFY(ldlt.isPositive());
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
}
{
mat << 0, 0, 0, 1;
ldlt.compute(mat);
VERIFY(ldlt.info()==Success);
VERIFY(!ldlt.isNegative());
VERIFY(ldlt.isPositive());
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
}
{
mat << -1, 0, 0, 0;
ldlt.compute(mat);
VERIFY(ldlt.info()==Success);
VERIFY(ldlt.isNegative());
VERIFY(!ldlt.isPositive());
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
}
}
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());
}
// bug 1479
{
mat.resize(4,4);
mat << 1, 2, 0, 1,
2, 4, 0, 2,
0, 0, 0, 1,
1, 2, 1, 1;
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)
}
// empty matrix, regression test for Bug 785:
CALL_SUBTEST_2( cholesky(MatrixXd(0,0)) );
// This does not work yet:
// CALL_SUBTEST_2( cholesky(Matrix<double,0,0>()) );
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)
}