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* add LU unit-test. Seems like we have very good numerical stability!
* some cleanup, and grant me a copyright line on the determinant test.
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test/lu.cpp
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119
test/lu.cpp
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra. Eigen itself is part of the KDE project.
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//
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// Copyright (C) 2008 Benoit Jacob <jacob@math.jussieu.fr>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#include "main.h"
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#include <Eigen/LU>
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template<typename Derived>
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void doSomeRankPreservingOperations(Eigen::MatrixBase<Derived>& m)
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{
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for(int a = 0; a < 3*(m.rows()+m.cols()); a++)
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{
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double d = Eigen::ei_random<double>(-1,1);
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int i = Eigen::ei_random<int>(0,m.rows()-1); // i is a random row number
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int j;
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do {
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j = Eigen::ei_random<int>(0,m.rows()-1);
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} while (i==j); // j is another one (must be different)
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m.row(i) += d * m.row(j);
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i = Eigen::ei_random<int>(0,m.cols()-1); // i is a random column number
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do {
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j = Eigen::ei_random<int>(0,m.cols()-1);
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} while (i==j); // j is another one (must be different)
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m.col(i) += d * m.col(j);
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}
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}
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template<typename MatrixType> void lu_non_invertible()
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{
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/* this test covers the following files:
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LU.h
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*/
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int rows = ei_random<int>(10,200), cols = ei_random<int>(10,200), cols2 = ei_random<int>(10,200);
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int rank = ei_random<int>(1, std::min(rows, cols)-1);
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MatrixType m1(rows, cols), m2(cols, cols2), m3(rows, cols2), k(1,1);
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m1.setRandom();
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if(rows <= cols)
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for(int i = rank; i < rows; i++) m1.row(i).setZero();
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else
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for(int i = rank; i < cols; i++) m1.col(i).setZero();
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doSomeRankPreservingOperations(m1);
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LU<MatrixType> lu(m1);
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VERIFY(cols - rank == lu.dimensionOfKernel());
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VERIFY(rank == lu.rank());
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VERIFY(!lu.isInjective());
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VERIFY(!lu.isInvertible());
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VERIFY(lu.isSurjective() == (lu.rank() == rows));
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VERIFY((m1 * lu.kernel()).isMuchSmallerThan(m1));
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lu.computeKernel(&k);
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VERIFY((m1 * k).isMuchSmallerThan(m1));
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m2.setRandom();
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m3 = m1*m2;
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m2.setRandom();
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lu.solve(m3, &m2);
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VERIFY_IS_APPROX(m3, m1*m2);
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m3.setRandom();
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VERIFY(!lu.solve(m3, &m2));
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}
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template<typename MatrixType> void lu_invertible()
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{
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/* this test covers the following files:
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LU.h
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*/
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int size = ei_random<int>(10,200);
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MatrixType m1(size, size), m2(size, size), m3(size, size);
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m1.setRandom();
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LU<MatrixType> lu(m1);
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VERIFY(0 == lu.dimensionOfKernel());
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VERIFY(size == lu.rank());
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VERIFY(lu.isInjective());
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VERIFY(lu.isSurjective());
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VERIFY(lu.isInvertible());
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m3.setRandom();
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lu.solve(m3, &m2);
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VERIFY_IS_APPROX(m3, m1*m2);
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VERIFY_IS_APPROX(m2, lu.inverse()*m3);
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m3.setRandom();
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VERIFY(lu.solve(m3, &m2));
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}
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void test_lu()
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST( lu_non_invertible<MatrixXf>() );
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CALL_SUBTEST( lu_non_invertible<MatrixXd>() );
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CALL_SUBTEST( lu_non_invertible<MatrixXcf>() );
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CALL_SUBTEST( lu_non_invertible<MatrixXcd>() );
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CALL_SUBTEST( lu_invertible<MatrixXf>() );
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CALL_SUBTEST( lu_invertible<MatrixXd>() );
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CALL_SUBTEST( lu_invertible<MatrixXcf>() );
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CALL_SUBTEST( lu_invertible<MatrixXcd>() );
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}
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}
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