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eigen/test/cholesky.cpp

333 lines
11 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
static int nb_temporaries;
#define EIGEN_DENSE_STORAGE_CTOR_PLUGIN { if(size!=0) nb_temporaries++; }
#include "main.h"
#include <Eigen/Cholesky>
#include <Eigen/QR>
#define VERIFY_EVALUATION_COUNT(XPR,N) {\
nb_temporaries = 0; \
XPR; \
if(nb_temporaries!=N) std::cerr << "nb_temporaries == " << nb_temporaries << "\n"; \
VERIFY( (#XPR) && nb_temporaries==N ); \
}
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 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();
}
// to test if really Cholesky only uses the upper triangular part, uncomment the following
// FIXME: currently that fails !!
//symm.template part<StrictlyLower>().setZero();
{
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);
// 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);
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_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);
LDLT<SquareMatrixType,Upper> ldltup(symmUp);
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_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;
}
// 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_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
template<typename MatrixType> void cholesky_indefinite(const MatrixType& m)
{
eigen_assert(m.rows() == 2 && m.cols() == 2);
MatrixType mat;
{
mat << 1, 0, 0, -1;
LDLT<MatrixType> ldlt(mat);
VERIFY(!ldlt.isNegative());
VERIFY(!ldlt.isPositive());
}
{
mat << 1, 2, 2, 1;
LDLT<MatrixType> ldlt(mat);
VERIFY(!ldlt.isNegative());
VERIFY(!ldlt.isPositive());
}
}
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_indefinite(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)) );
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
CALL_SUBTEST_6( cholesky_cplx(MatrixXcd(s,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) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
TEST_SET_BUT_UNUSED_VARIABLE(nb_temporaries)
}