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https://gitlab.com/libeigen/eigen.git
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4ed87c59c7
- Add support for Upper or Lower inputs. - Add supports for sparse RHS - Remove transposed cases, remove ordering method interface - Add full access to PARDISO parameters
195 lines
6.0 KiB
C++
195 lines
6.0 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) 2011 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/SparseCore>
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template<typename Solver, typename Rhs, typename DenseMat, typename DenseRhs>
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void check_sparse_solving(Solver& solver, const typename Solver::MatrixType& A, const Rhs& b, const DenseMat& dA, const DenseRhs& db)
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{
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typedef typename Solver::MatrixType Mat;
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typedef typename Mat::Scalar Scalar;
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DenseRhs refX = dA.lu().solve(db);
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Rhs x(b.rows(), b.cols());
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Rhs oldb = b;
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solver.compute(A);
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if (solver.info() != Success)
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{
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std::cerr << "sparse solver testing: factorization failed (check_sparse_solving)\n";
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exit(0);
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return;
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}
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x = solver.solve(b);
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if (solver.info() != Success)
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{
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std::cerr << "sparse SPD: solving failed\n";
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return;
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}
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VERIFY(oldb.isApprox(b) && "sparse SPD: the rhs should not be modified!");
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VERIFY(x.isApprox(refX,test_precision<Scalar>()));
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}
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template<typename Solver, typename DenseMat>
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void check_sparse_determinant(Solver& solver, const typename Solver::MatrixType& A, const DenseMat& dA)
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{
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typedef typename Solver::MatrixType Mat;
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typedef typename Mat::Scalar Scalar;
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typedef typename Mat::RealScalar RealScalar;
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solver.compute(A);
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if (solver.info() != Success)
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{
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std::cerr << "sparse solver testing: factorization failed (check_sparse_determinant)\n";
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return;
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}
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Scalar refDet = dA.determinant();
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VERIFY_IS_APPROX(refDet,solver.determinant());
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}
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template<typename Solver, typename DenseMat>
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int generate_sparse_spd_problem(Solver& , typename Solver::MatrixType& A, typename Solver::MatrixType& halfA, DenseMat& dA, int maxSize = 300)
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{
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typedef typename Solver::MatrixType Mat;
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typedef typename Mat::Scalar Scalar;
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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int size = internal::random<int>(1,maxSize);
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double density = (std::max)(8./(size*size), 0.01);
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Mat M(size, size);
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DenseMatrix dM(size, size);
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initSparse<Scalar>(density, dM, M, ForceNonZeroDiag);
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A = M * M.adjoint();
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dA = dM * dM.adjoint();
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halfA.resize(size,size);
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halfA.template selfadjointView<Solver::UpLo>().rankUpdate(M);
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return size;
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}
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template<typename Solver> void check_sparse_spd_solving(Solver& solver)
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{
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typedef typename Solver::MatrixType Mat;
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typedef typename Mat::Scalar Scalar;
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typedef SparseMatrix<Scalar,ColMajor> SpMat;
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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typedef Matrix<Scalar,Dynamic,1> DenseVector;
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// generate the problem
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Mat A, halfA;
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DenseMatrix dA;
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int size = generate_sparse_spd_problem(solver, A, halfA, dA);
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// generate the right hand sides
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int rhsCols = internal::random<int>(1,16);
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double density = (std::max)(8./(size*rhsCols), 0.1);
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SpMat B(size,rhsCols);
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DenseVector b = DenseVector::Random(size);
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DenseMatrix dB(size,rhsCols);
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initSparse<Scalar>(density, dB, B);
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check_sparse_solving(solver, A, b, dA, b);
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check_sparse_solving(solver, halfA, b, dA, b);
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check_sparse_solving(solver, A, dB, dA, dB);
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check_sparse_solving(solver, halfA, dB, dA, dB);
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check_sparse_solving(solver, A, B, dA, dB);
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check_sparse_solving(solver, halfA, B, dA, dB);
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}
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template<typename Solver> void check_sparse_spd_determinant(Solver& solver)
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{
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typedef typename Solver::MatrixType Mat;
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typedef typename Mat::Scalar Scalar;
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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// generate the problem
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Mat A, halfA;
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DenseMatrix dA;
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generate_sparse_spd_problem(solver, A, halfA, dA, 30);
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check_sparse_determinant(solver, A, dA);
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check_sparse_determinant(solver, halfA, dA );
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}
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template<typename Solver, typename DenseMat>
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int generate_sparse_square_problem(Solver&, typename Solver::MatrixType& A, DenseMat& dA, int maxSize = 300)
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{
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typedef typename Solver::MatrixType Mat;
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typedef typename Mat::Scalar Scalar;
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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int size = internal::random<int>(1,maxSize);
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double density = (std::max)(8./(size*size), 0.01);
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A.resize(size,size);
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dA.resize(size,size);
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initSparse<Scalar>(density, dA, A, ForceNonZeroDiag);
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return size;
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}
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template<typename Solver> void check_sparse_square_solving(Solver& solver)
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{
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typedef typename Solver::MatrixType Mat;
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typedef typename Mat::Scalar Scalar;
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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typedef Matrix<Scalar,Dynamic,1> DenseVector;
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int rhsCols = internal::random<int>(1,16);
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Mat A;
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DenseMatrix dA;
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int size = generate_sparse_square_problem(solver, A, dA);
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DenseVector b = DenseVector::Random(size);
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DenseMatrix dB = DenseMatrix::Random(size,rhsCols);
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check_sparse_solving(solver, A, b, dA, b);
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check_sparse_solving(solver, A, dB, dA, dB);
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}
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template<typename Solver> void check_sparse_square_determinant(Solver& solver)
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{
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typedef typename Solver::MatrixType Mat;
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typedef typename Mat::Scalar Scalar;
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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// generate the problem
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Mat A;
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DenseMatrix dA;
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generate_sparse_square_problem(solver, A, dA, 30);
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check_sparse_determinant(solver, A, dA);
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}
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