feat(3rdparty): add eigen and ceres
This commit is contained in:
John Zhao
603
3rdparty/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h
vendored
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603
3rdparty/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h
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/*
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Copyright (c) 2011, Intel Corporation. All rights reserved.
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Redistribution and use in source and binary forms, with or without modification,
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are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the name of Intel Corporation nor the names of its contributors may
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be used to endorse or promote products derived from this software without
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specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
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ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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********************************************************************************
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* Content : Eigen bindings to Intel(R) MKL PARDISO
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********************************************************************************
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*/
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#ifndef EIGEN_PARDISOSUPPORT_H
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#define EIGEN_PARDISOSUPPORT_H
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namespace Eigen {
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template<typename _MatrixType> class PardisoLU;
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template<typename _MatrixType, int Options=Upper> class PardisoLLT;
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template<typename _MatrixType, int Options=Upper> class PardisoLDLT;
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namespace internal
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{
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template<typename Index>
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struct pardiso_run_selector
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{
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static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a,
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Index *ia, Index *ja, Index *perm, Index nrhs, Index *iparm, Index msglvl, void *b, void *x)
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{
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Index error = 0;
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::pardiso(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
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return error;
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}
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};
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template<>
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struct pardiso_run_selector<long long int>
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{
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typedef long long int Index;
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static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a,
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Index *ia, Index *ja, Index *perm, Index nrhs, Index *iparm, Index msglvl, void *b, void *x)
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{
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Index error = 0;
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::pardiso_64(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
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return error;
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}
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};
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template<class Pardiso> struct pardiso_traits;
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template<typename _MatrixType>
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struct pardiso_traits< PardisoLU<_MatrixType> >
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{
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typedef _MatrixType MatrixType;
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typedef typename _MatrixType::Scalar Scalar;
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typedef typename _MatrixType::RealScalar RealScalar;
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typedef typename _MatrixType::Index Index;
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};
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template<typename _MatrixType, int Options>
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struct pardiso_traits< PardisoLLT<_MatrixType, Options> >
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{
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typedef _MatrixType MatrixType;
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typedef typename _MatrixType::Scalar Scalar;
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typedef typename _MatrixType::RealScalar RealScalar;
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typedef typename _MatrixType::Index Index;
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};
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template<typename _MatrixType, int Options>
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struct pardiso_traits< PardisoLDLT<_MatrixType, Options> >
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{
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typedef _MatrixType MatrixType;
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typedef typename _MatrixType::Scalar Scalar;
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typedef typename _MatrixType::RealScalar RealScalar;
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typedef typename _MatrixType::Index Index;
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};
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}
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template<class Derived>
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class PardisoImpl
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{
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typedef internal::pardiso_traits<Derived> Traits;
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public:
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typedef typename Traits::MatrixType MatrixType;
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typedef typename Traits::Scalar Scalar;
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typedef typename Traits::RealScalar RealScalar;
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typedef typename Traits::Index Index;
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typedef SparseMatrix<Scalar,RowMajor,Index> SparseMatrixType;
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typedef Matrix<Scalar,Dynamic,1> VectorType;
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typedef Matrix<Index, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
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typedef Matrix<Index, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
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typedef Array<Index,64,1,DontAlign> ParameterType;
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enum {
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ScalarIsComplex = NumTraits<Scalar>::IsComplex
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};
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PardisoImpl()
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{
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eigen_assert((sizeof(Index) >= sizeof(_INTEGER_t) && sizeof(Index) <= 8) && "Non-supported index type");
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m_iparm.setZero();
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m_msglvl = 0; // No output
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m_initialized = false;
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}
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~PardisoImpl()
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{
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pardisoRelease();
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}
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inline Index cols() const { return m_size; }
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inline Index rows() const { return m_size; }
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/** \brief Reports whether previous computation was successful.
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*
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* \returns \c Success if computation was succesful,
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* \c NumericalIssue if the matrix appears to be negative.
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*/
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ComputationInfo info() const
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{
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eigen_assert(m_initialized && "Decomposition is not initialized.");
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return m_info;
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}
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/** \warning for advanced usage only.
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* \returns a reference to the parameter array controlling PARDISO.
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* See the PARDISO manual to know how to use it. */
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ParameterType& pardisoParameterArray()
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{
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return m_iparm;
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}
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/** Performs a symbolic decomposition on the sparcity of \a matrix.
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*
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* This function is particularly useful when solving for several problems having the same structure.
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*
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* \sa factorize()
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*/
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Derived& analyzePattern(const MatrixType& matrix);
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/** Performs a numeric decomposition of \a matrix
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*
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* The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
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*
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* \sa analyzePattern()
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*/
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Derived& factorize(const MatrixType& matrix);
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Derived& compute(const MatrixType& matrix);
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/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
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*
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* \sa compute()
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*/
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template<typename Rhs>
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inline const internal::solve_retval<PardisoImpl, Rhs>
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solve(const MatrixBase<Rhs>& b) const
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{
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eigen_assert(m_initialized && "Pardiso solver is not initialized.");
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eigen_assert(rows()==b.rows()
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&& "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b");
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return internal::solve_retval<PardisoImpl, Rhs>(*this, b.derived());
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}
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/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
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*
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* \sa compute()
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*/
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template<typename Rhs>
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inline const internal::sparse_solve_retval<PardisoImpl, Rhs>
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solve(const SparseMatrixBase<Rhs>& b) const
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{
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eigen_assert(m_initialized && "Pardiso solver is not initialized.");
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eigen_assert(rows()==b.rows()
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&& "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b");
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return internal::sparse_solve_retval<PardisoImpl, Rhs>(*this, b.derived());
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}
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Derived& derived()
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{
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return *static_cast<Derived*>(this);
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}
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const Derived& derived() const
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{
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return *static_cast<const Derived*>(this);
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}
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template<typename BDerived, typename XDerived>
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bool _solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const;
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protected:
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void pardisoRelease()
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{
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if(m_initialized) // Factorization ran at least once
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{
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internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, -1, m_size, 0, 0, 0, m_perm.data(), 0,
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m_iparm.data(), m_msglvl, 0, 0);
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}
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}
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void pardisoInit(int type)
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{
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m_type = type;
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bool symmetric = std::abs(m_type) < 10;
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m_iparm[0] = 1; // No solver default
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m_iparm[1] = 2; // use Metis for the ordering
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m_iparm[2] = 0; // Reserved. Set to zero. (??Numbers of processors, value of OMP_NUM_THREADS??)
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m_iparm[3] = 0; // No iterative-direct algorithm
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m_iparm[4] = 0; // No user fill-in reducing permutation
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m_iparm[5] = 0; // Write solution into x, b is left unchanged
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m_iparm[6] = 0; // Not in use
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m_iparm[7] = 2; // Max numbers of iterative refinement steps
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m_iparm[8] = 0; // Not in use
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m_iparm[9] = 13; // Perturb the pivot elements with 1E-13
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m_iparm[10] = symmetric ? 0 : 1; // Use nonsymmetric permutation and scaling MPS
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m_iparm[11] = 0; // Not in use
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m_iparm[12] = symmetric ? 0 : 1; // Maximum weighted matching algorithm is switched-off (default for symmetric).
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// Try m_iparm[12] = 1 in case of inappropriate accuracy
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m_iparm[13] = 0; // Output: Number of perturbed pivots
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m_iparm[14] = 0; // Not in use
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m_iparm[15] = 0; // Not in use
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m_iparm[16] = 0; // Not in use
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m_iparm[17] = -1; // Output: Number of nonzeros in the factor LU
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m_iparm[18] = -1; // Output: Mflops for LU factorization
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m_iparm[19] = 0; // Output: Numbers of CG Iterations
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m_iparm[20] = 0; // 1x1 pivoting
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m_iparm[26] = 0; // No matrix checker
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m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0;
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m_iparm[34] = 1; // C indexing
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m_iparm[36] = 0; // CSR
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m_iparm[59] = 0; // 0 - In-Core ; 1 - Automatic switch between In-Core and Out-of-Core modes ; 2 - Out-of-Core
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memset(m_pt, 0, sizeof(m_pt));
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}
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protected:
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// cached data to reduce reallocation, etc.
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void manageErrorCode(Index error)
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{
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switch(error)
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{
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case 0:
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m_info = Success;
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break;
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case -4:
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case -7:
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m_info = NumericalIssue;
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break;
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default:
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m_info = InvalidInput;
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}
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}
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mutable SparseMatrixType m_matrix;
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ComputationInfo m_info;
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bool m_initialized, m_analysisIsOk, m_factorizationIsOk;
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Index m_type, m_msglvl;
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mutable void *m_pt[64];
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mutable ParameterType m_iparm;
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mutable IntColVectorType m_perm;
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Index m_size;
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private:
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PardisoImpl(PardisoImpl &) {}
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};
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template<class Derived>
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Derived& PardisoImpl<Derived>::compute(const MatrixType& a)
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{
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m_size = a.rows();
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eigen_assert(a.rows() == a.cols());
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pardisoRelease();
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memset(m_pt, 0, sizeof(m_pt));
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m_perm.setZero(m_size);
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derived().getMatrix(a);
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Index error;
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error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 12, m_size,
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m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
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m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
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manageErrorCode(error);
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m_analysisIsOk = true;
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m_factorizationIsOk = true;
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m_initialized = true;
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return derived();
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}
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template<class Derived>
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Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a)
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{
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m_size = a.rows();
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eigen_assert(m_size == a.cols());
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pardisoRelease();
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memset(m_pt, 0, sizeof(m_pt));
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m_perm.setZero(m_size);
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derived().getMatrix(a);
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Index error;
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error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 11, m_size,
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m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
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m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
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manageErrorCode(error);
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m_analysisIsOk = true;
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m_factorizationIsOk = false;
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m_initialized = true;
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return derived();
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}
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template<class Derived>
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Derived& PardisoImpl<Derived>::factorize(const MatrixType& a)
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{
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eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
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eigen_assert(m_size == a.rows() && m_size == a.cols());
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derived().getMatrix(a);
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Index error;
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error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 22, m_size,
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m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
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m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
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manageErrorCode(error);
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m_factorizationIsOk = true;
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return derived();
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}
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template<class Base>
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template<typename BDerived,typename XDerived>
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bool PardisoImpl<Base>::_solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const
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{
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if(m_iparm[0] == 0) // Factorization was not computed
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return false;
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//Index n = m_matrix.rows();
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Index nrhs = Index(b.cols());
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eigen_assert(m_size==b.rows());
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eigen_assert(((MatrixBase<BDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major right hand sides are not supported");
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eigen_assert(((MatrixBase<XDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major matrices of unknowns are not supported");
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eigen_assert(((nrhs == 1) || b.outerStride() == b.rows()));
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// switch (transposed) {
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// case SvNoTrans : m_iparm[11] = 0 ; break;
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// case SvTranspose : m_iparm[11] = 2 ; break;
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// case SvAdjoint : m_iparm[11] = 1 ; break;
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// default:
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// //std::cerr << "Eigen: transposition option \"" << transposed << "\" not supported by the PARDISO backend\n";
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// m_iparm[11] = 0;
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// }
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Scalar* rhs_ptr = const_cast<Scalar*>(b.derived().data());
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Matrix<Scalar,Dynamic,Dynamic,ColMajor> tmp;
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// Pardiso cannot solve in-place
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if(rhs_ptr == x.derived().data())
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{
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tmp = b;
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rhs_ptr = tmp.data();
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}
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Index error;
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error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 33, m_size,
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m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
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m_perm.data(), nrhs, m_iparm.data(), m_msglvl,
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rhs_ptr, x.derived().data());
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return error==0;
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}
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/** \ingroup PardisoSupport_Module
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* \class PardisoLU
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* \brief A sparse direct LU factorization and solver based on the PARDISO library
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*
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* This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
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* using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible.
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* The vectors or matrices X and B can be either dense or sparse.
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*
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* By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
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* \code solver.pardisoParameterArray()[59] = 1; \endcode
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*
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* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
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*
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* \sa \ref TutorialSparseDirectSolvers
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*/
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template<typename MatrixType>
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class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
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||||
{
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||||
protected:
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typedef PardisoImpl< PardisoLU<MatrixType> > Base;
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||||
typedef typename Base::Scalar Scalar;
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||||
typedef typename Base::RealScalar RealScalar;
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using Base::pardisoInit;
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using Base::m_matrix;
|
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friend class PardisoImpl< PardisoLU<MatrixType> >;
|
||||
|
||||
public:
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||||
|
||||
using Base::compute;
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using Base::solve;
|
||||
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||||
PardisoLU()
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||||
: Base()
|
||||
{
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||||
pardisoInit(Base::ScalarIsComplex ? 13 : 11);
|
||||
}
|
||||
|
||||
PardisoLU(const MatrixType& matrix)
|
||||
: Base()
|
||||
{
|
||||
pardisoInit(Base::ScalarIsComplex ? 13 : 11);
|
||||
compute(matrix);
|
||||
}
|
||||
protected:
|
||||
void getMatrix(const MatrixType& matrix)
|
||||
{
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||||
m_matrix = matrix;
|
||||
}
|
||||
|
||||
private:
|
||||
PardisoLU(PardisoLU& ) {}
|
||||
};
|
||||
|
||||
/** \ingroup PardisoSupport_Module
|
||||
* \class PardisoLLT
|
||||
* \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
|
||||
*
|
||||
* This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization
|
||||
* using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
|
||||
* The vectors or matrices X and B can be either dense or sparse.
|
||||
*
|
||||
* By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
|
||||
* \code solver.pardisoParameterArray()[59] = 1; \endcode
|
||||
*
|
||||
* \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
|
||||
* \tparam UpLo can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular part has to be used.
|
||||
* Upper|Lower can be used to tell both triangular parts can be used as input.
|
||||
*
|
||||
* \sa \ref TutorialSparseDirectSolvers
|
||||
*/
|
||||
template<typename MatrixType, int _UpLo>
|
||||
class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,_UpLo> >
|
||||
{
|
||||
protected:
|
||||
typedef PardisoImpl< PardisoLLT<MatrixType,_UpLo> > Base;
|
||||
typedef typename Base::Scalar Scalar;
|
||||
typedef typename Base::Index Index;
|
||||
typedef typename Base::RealScalar RealScalar;
|
||||
using Base::pardisoInit;
|
||||
using Base::m_matrix;
|
||||
friend class PardisoImpl< PardisoLLT<MatrixType,_UpLo> >;
|
||||
|
||||
public:
|
||||
|
||||
enum { UpLo = _UpLo };
|
||||
using Base::compute;
|
||||
using Base::solve;
|
||||
|
||||
PardisoLLT()
|
||||
: Base()
|
||||
{
|
||||
pardisoInit(Base::ScalarIsComplex ? 4 : 2);
|
||||
}
|
||||
|
||||
PardisoLLT(const MatrixType& matrix)
|
||||
: Base()
|
||||
{
|
||||
pardisoInit(Base::ScalarIsComplex ? 4 : 2);
|
||||
compute(matrix);
|
||||
}
|
||||
|
||||
protected:
|
||||
|
||||
void getMatrix(const MatrixType& matrix)
|
||||
{
|
||||
// PARDISO supports only upper, row-major matrices
|
||||
PermutationMatrix<Dynamic,Dynamic,Index> p_null;
|
||||
m_matrix.resize(matrix.rows(), matrix.cols());
|
||||
m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
|
||||
}
|
||||
|
||||
private:
|
||||
PardisoLLT(PardisoLLT& ) {}
|
||||
};
|
||||
|
||||
/** \ingroup PardisoSupport_Module
|
||||
* \class PardisoLDLT
|
||||
* \brief A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library
|
||||
*
|
||||
* This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization
|
||||
* using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite.
|
||||
* For complex matrices, A can also be symmetric only, see the \a Options template parameter.
|
||||
* The vectors or matrices X and B can be either dense or sparse.
|
||||
*
|
||||
* By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
|
||||
* \code solver.pardisoParameterArray()[59] = 1; \endcode
|
||||
*
|
||||
* \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
|
||||
* \tparam Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used.
|
||||
* Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix.
|
||||
* Upper|Lower can be used to tell both triangular parts can be used as input.
|
||||
*
|
||||
* \sa \ref TutorialSparseDirectSolvers
|
||||
*/
|
||||
template<typename MatrixType, int Options>
|
||||
class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType,Options> >
|
||||
{
|
||||
protected:
|
||||
typedef PardisoImpl< PardisoLDLT<MatrixType,Options> > Base;
|
||||
typedef typename Base::Scalar Scalar;
|
||||
typedef typename Base::Index Index;
|
||||
typedef typename Base::RealScalar RealScalar;
|
||||
using Base::pardisoInit;
|
||||
using Base::m_matrix;
|
||||
friend class PardisoImpl< PardisoLDLT<MatrixType,Options> >;
|
||||
|
||||
public:
|
||||
|
||||
using Base::compute;
|
||||
using Base::solve;
|
||||
enum { UpLo = Options&(Upper|Lower) };
|
||||
|
||||
PardisoLDLT()
|
||||
: Base()
|
||||
{
|
||||
pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
|
||||
}
|
||||
|
||||
PardisoLDLT(const MatrixType& matrix)
|
||||
: Base()
|
||||
{
|
||||
pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
|
||||
compute(matrix);
|
||||
}
|
||||
|
||||
void getMatrix(const MatrixType& matrix)
|
||||
{
|
||||
// PARDISO supports only upper, row-major matrices
|
||||
PermutationMatrix<Dynamic,Dynamic,Index> p_null;
|
||||
m_matrix.resize(matrix.rows(), matrix.cols());
|
||||
m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
|
||||
}
|
||||
|
||||
private:
|
||||
PardisoLDLT(PardisoLDLT& ) {}
|
||||
};
|
||||
|
||||
namespace internal {
|
||||
|
||||
template<typename _Derived, typename Rhs>
|
||||
struct solve_retval<PardisoImpl<_Derived>, Rhs>
|
||||
: solve_retval_base<PardisoImpl<_Derived>, Rhs>
|
||||
{
|
||||
typedef PardisoImpl<_Derived> Dec;
|
||||
EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
|
||||
|
||||
template<typename Dest> void evalTo(Dest& dst) const
|
||||
{
|
||||
dec()._solve(rhs(),dst);
|
||||
}
|
||||
};
|
||||
|
||||
template<typename Derived, typename Rhs>
|
||||
struct sparse_solve_retval<PardisoImpl<Derived>, Rhs>
|
||||
: sparse_solve_retval_base<PardisoImpl<Derived>, Rhs>
|
||||
{
|
||||
typedef PardisoImpl<Derived> Dec;
|
||||
EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)
|
||||
|
||||
template<typename Dest> void evalTo(Dest& dst) const
|
||||
{
|
||||
this->defaultEvalTo(dst);
|
||||
}
|
||||
};
|
||||
|
||||
} // end namespace internal
|
||||
|
||||
} // end namespace Eigen
|
||||
|
||||
#endif // EIGEN_PARDISOSUPPORT_H
|
||||
Reference in New Issue
Block a user