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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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/.
#include "main.h"
#include <Eigen/LU>
template<typename Derived>
void doSomeRankPreservingOperations(Eigen::MatrixBase<Derived>& m)
{
typedef typename Derived::RealScalar RealScalar;
for(int a = 0; a < 3*(m.rows()+m.cols()); a++)
{
RealScalar d = Eigen::ei_random<RealScalar>(-1,1);
int i = Eigen::ei_random<int>(0,m.rows()-1); // i is a random row number
int j;
do {
j = Eigen::ei_random<int>(0,m.rows()-1);
} while (i==j); // j is another one (must be different)
m.row(i) += d * m.row(j);
i = Eigen::ei_random<int>(0,m.cols()-1); // i is a random column number
do {
j = Eigen::ei_random<int>(0,m.cols()-1);
} while (i==j); // j is another one (must be different)
m.col(i) += d * m.col(j);
}
}
template<typename MatrixType> void lu_non_invertible()
{
/* this test covers the following files:
LU.h
*/
// NOTE there seems to be a problem with too small sizes -- could easily lie in the doSomeRankPreservingOperations function
int rows = ei_random<int>(20,200), cols = ei_random<int>(20,200), cols2 = ei_random<int>(20,200);
int rank = ei_random<int>(1, std::min(rows, cols)-1);
MatrixType m1(rows, cols), m2(cols, cols2), m3(rows, cols2), k(1,1);
m1 = MatrixType::Random(rows,cols);
if(rows <= cols)
for(int i = rank; i < rows; i++) m1.row(i).setZero();
else
for(int i = rank; i < cols; i++) m1.col(i).setZero();
doSomeRankPreservingOperations(m1);
LU<MatrixType> lu(m1);
typename LU<MatrixType>::KernelResultType m1kernel = lu.kernel();
typename LU<MatrixType>::ImageResultType m1image = lu.image();
VERIFY(rank == lu.rank());
VERIFY(cols - lu.rank() == lu.dimensionOfKernel());
VERIFY(!lu.isInjective());
VERIFY(!lu.isInvertible());
VERIFY(lu.isSurjective() == (lu.rank() == rows));
VERIFY((m1 * m1kernel).isMuchSmallerThan(m1));
VERIFY(m1image.lu().rank() == rank);
MatrixType sidebyside(m1.rows(), m1.cols() + m1image.cols());
sidebyside << m1, m1image;
VERIFY(sidebyside.lu().rank() == rank);
m2 = MatrixType::Random(cols,cols2);
m3 = m1*m2;
m2 = MatrixType::Random(cols,cols2);
lu.solve(m3, &m2);
VERIFY_IS_APPROX(m3, m1*m2);
/* solve now always returns true
m3 = MatrixType::Random(rows,cols2);
VERIFY(!lu.solve(m3, &m2));
*/
}
template<typename MatrixType> void lu_invertible()
{
/* this test covers the following files:
LU.h
*/
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
int size = ei_random<int>(10,200);
MatrixType m1(size, size), m2(size, size), m3(size, size);
m1 = MatrixType::Random(size,size);
if (ei_is_same_type<RealScalar,float>::ret)
{
// let's build a matrix more stable to inverse
MatrixType a = MatrixType::Random(size,size*2);
m1 += a * a.adjoint();
}
LU<MatrixType> lu(m1);
VERIFY(0 == lu.dimensionOfKernel());
VERIFY(size == lu.rank());
VERIFY(lu.isInjective());
VERIFY(lu.isSurjective());
VERIFY(lu.isInvertible());
VERIFY(lu.image().lu().isInvertible());
m3 = MatrixType::Random(size,size);
lu.solve(m3, &m2);
VERIFY_IS_APPROX(m3, m1*m2);
VERIFY_IS_APPROX(m2, lu.inverse()*m3);
m3 = MatrixType::Random(size,size);
VERIFY(lu.solve(m3, &m2));
}
void test_eigen2_lu()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( lu_non_invertible<MatrixXf>() );
CALL_SUBTEST_2( lu_non_invertible<MatrixXd>() );
CALL_SUBTEST_3( lu_non_invertible<MatrixXcf>() );
CALL_SUBTEST_4( lu_non_invertible<MatrixXcd>() );
CALL_SUBTEST_1( lu_invertible<MatrixXf>() );
CALL_SUBTEST_2( lu_invertible<MatrixXd>() );
CALL_SUBTEST_3( lu_invertible<MatrixXcf>() );
CALL_SUBTEST_4( lu_invertible<MatrixXcd>() );
}
}
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