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114 lines
3.7 KiB
C++
114 lines
3.7 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.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 "sparse.h"
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#include <Eigen/SparseExtra>
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#ifdef EIGEN_UMFPACK_SUPPORT
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#include <Eigen/UmfPackSupport>
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#endif
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#ifdef EIGEN_SUPERLU_SUPPORT
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#include <Eigen/SuperLUSupport>
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#endif
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template<typename Scalar> void sparse_lu(int rows, int cols)
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{
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double density = std::max(8./(rows*cols), 0.01);
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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typedef Matrix<Scalar,Dynamic,1> DenseVector;
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DenseVector vec1 = DenseVector::Random(rows);
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std::vector<Vector2i> zeroCoords;
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std::vector<Vector2i> nonzeroCoords;
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SparseMatrix<Scalar> m2(rows, cols);
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DenseMatrix refMat2(rows, cols);
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DenseVector b = DenseVector::Random(cols);
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DenseVector refX(cols), x(cols);
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initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag, &zeroCoords, &nonzeroCoords);
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FullPivLU<DenseMatrix> refLu(refMat2);
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refX = refLu.solve(b);
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#if defined(EIGEN_SUPERLU_SUPPORT) || defined(EIGEN_UMFPACK_SUPPORT)
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Scalar refDet = refLu.determinant();
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#endif
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x.setZero();
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// // SparseLU<SparseMatrix<Scalar> > (m2).solve(b,&x);
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// // VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: default");
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#ifdef EIGEN_UMFPACK_SUPPORT
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{
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// check solve
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x.setZero();
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SparseLU<SparseMatrix<Scalar>,UmfPack> lu(m2);
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VERIFY(lu.succeeded() && "umfpack LU decomposition failed");
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VERIFY(lu.solve(b,&x) && "umfpack LU solving failed");
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VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: umfpack");
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VERIFY_IS_APPROX(refDet,lu.determinant());
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// TODO check the extracted data
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//std::cerr << slu.matrixL() << "\n";
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}
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#endif
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#ifdef EIGEN_SUPERLU_SUPPORT
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{
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x.setZero();
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SparseLU<SparseMatrix<Scalar>,SuperLU> slu(m2);
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if (slu.succeeded())
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{
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if (slu.solve(b,&x)) {
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VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: SuperLU");
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}
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// std::cerr << refDet << " == " << slu.determinant() << "\n";
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if (slu.solve(b, &x, SvTranspose)) {
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VERIFY(b.isApprox(m2.transpose() * x, test_precision<Scalar>()));
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}
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if (slu.solve(b, &x, SvAdjoint)) {
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VERIFY(b.isApprox(m2.adjoint() * x, test_precision<Scalar>()));
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}
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if (!NumTraits<Scalar>::IsComplex) {
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VERIFY_IS_APPROX(refDet,slu.determinant()); // FIXME det is not very stable for complex
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}
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}
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}
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#endif
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}
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void test_sparse_lu()
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_1(sparse_lu<double>(8, 8) );
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int s = internal::random<int>(1,300);
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CALL_SUBTEST_2(sparse_lu<std::complex<double> >(s,s) );
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CALL_SUBTEST_1(sparse_lu<double>(s,s) );
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}
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}
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