Squashed 'third_party/eigen/' content from commit 61d72f6
Change-Id: Iccc90fa0b55ab44037f018046d2fcffd90d9d025
git-subtree-dir: third_party/eigen
git-subtree-split: 61d72f6383cfa842868c53e30e087b0258177257
diff --git a/Eigen/src/UmfPackSupport/UmfPackSupport.h b/Eigen/src/UmfPackSupport/UmfPackSupport.h
new file mode 100644
index 0000000..29c60c3
--- /dev/null
+++ b/Eigen/src/UmfPackSupport/UmfPackSupport.h
@@ -0,0 +1,474 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// 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/.
+
+#ifndef EIGEN_UMFPACKSUPPORT_H
+#define EIGEN_UMFPACKSUPPORT_H
+
+namespace Eigen {
+
+/* TODO extract L, extract U, compute det, etc... */
+
+// generic double/complex<double> wrapper functions:
+
+inline void umfpack_free_numeric(void **Numeric, double)
+{ umfpack_di_free_numeric(Numeric); *Numeric = 0; }
+
+inline void umfpack_free_numeric(void **Numeric, std::complex<double>)
+{ umfpack_zi_free_numeric(Numeric); *Numeric = 0; }
+
+inline void umfpack_free_symbolic(void **Symbolic, double)
+{ umfpack_di_free_symbolic(Symbolic); *Symbolic = 0; }
+
+inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>)
+{ umfpack_zi_free_symbolic(Symbolic); *Symbolic = 0; }
+
+inline int umfpack_symbolic(int n_row,int n_col,
+ const int Ap[], const int Ai[], const double Ax[], void **Symbolic,
+ const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
+{
+ return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info);
+}
+
+inline int umfpack_symbolic(int n_row,int n_col,
+ const int Ap[], const int Ai[], const std::complex<double> Ax[], void **Symbolic,
+ const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
+{
+ return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info);
+}
+
+inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[],
+ void *Symbolic, void **Numeric,
+ const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
+{
+ return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info);
+}
+
+inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex<double> Ax[],
+ void *Symbolic, void **Numeric,
+ const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
+{
+ return umfpack_zi_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info);
+}
+
+inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[],
+ double X[], const double B[], void *Numeric,
+ const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
+{
+ return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info);
+}
+
+inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[],
+ std::complex<double> X[], const std::complex<double> B[], void *Numeric,
+ const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
+{
+ return umfpack_zi_solve(sys,Ap,Ai,&numext::real_ref(Ax[0]),0,&numext::real_ref(X[0]),0,&numext::real_ref(B[0]),0,Numeric,Control,Info);
+}
+
+inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double)
+{
+ return umfpack_di_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
+}
+
+inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, std::complex<double>)
+{
+ return umfpack_zi_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
+}
+
+inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[],
+ int P[], int Q[], double Dx[], int *do_recip, double Rs[], void *Numeric)
+{
+ return umfpack_di_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric);
+}
+
+inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex<double> Lx[], int Up[], int Ui[], std::complex<double> Ux[],
+ int P[], int Q[], std::complex<double> Dx[], int *do_recip, double Rs[], void *Numeric)
+{
+ double& lx0_real = numext::real_ref(Lx[0]);
+ double& ux0_real = numext::real_ref(Ux[0]);
+ double& dx0_real = numext::real_ref(Dx[0]);
+ return umfpack_zi_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q,
+ Dx?&dx0_real:0,0,do_recip,Rs,Numeric);
+}
+
+inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO])
+{
+ return umfpack_di_get_determinant(Mx,Ex,NumericHandle,User_Info);
+}
+
+inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO])
+{
+ double& mx_real = numext::real_ref(*Mx);
+ return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info);
+}
+
+namespace internal {
+ template<typename T> struct umfpack_helper_is_sparse_plain : false_type {};
+ template<typename Scalar, int Options, typename StorageIndex>
+ struct umfpack_helper_is_sparse_plain<SparseMatrix<Scalar,Options,StorageIndex> >
+ : true_type {};
+ template<typename Scalar, int Options, typename StorageIndex>
+ struct umfpack_helper_is_sparse_plain<MappedSparseMatrix<Scalar,Options,StorageIndex> >
+ : true_type {};
+}
+
+/** \ingroup UmfPackSupport_Module
+ * \brief A sparse LU factorization and solver based on UmfPack
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a LU factorization
+ * using the UmfPack library. The sparse matrix A must be squared and full rank.
+ * The vectors or matrices X and B can be either dense or sparse.
+ *
+ * \warning The input matrix A should be in a \b compressed and \b column-major form.
+ * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
+ * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+ *
+ * \sa \ref TutorialSparseDirectSolvers
+ */
+template<typename _MatrixType>
+class UmfPackLU : internal::noncopyable
+{
+ public:
+ typedef _MatrixType MatrixType;
+ typedef typename MatrixType::Scalar Scalar;
+ typedef typename MatrixType::RealScalar RealScalar;
+ typedef typename MatrixType::Index Index;
+ typedef Matrix<Scalar,Dynamic,1> Vector;
+ typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+ typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
+ typedef SparseMatrix<Scalar> LUMatrixType;
+ typedef SparseMatrix<Scalar,ColMajor,int> UmfpackMatrixType;
+
+ public:
+
+ UmfPackLU() { init(); }
+
+ UmfPackLU(const MatrixType& matrix)
+ {
+ init();
+ compute(matrix);
+ }
+
+ ~UmfPackLU()
+ {
+ if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
+ if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar());
+ }
+
+ inline Index rows() const { return m_copyMatrix.rows(); }
+ inline Index cols() const { return m_copyMatrix.cols(); }
+
+ /** \brief Reports whether previous computation was successful.
+ *
+ * \returns \c Success if computation was succesful,
+ * \c NumericalIssue if the matrix.appears to be negative.
+ */
+ ComputationInfo info() const
+ {
+ eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+ return m_info;
+ }
+
+ inline const LUMatrixType& matrixL() const
+ {
+ if (m_extractedDataAreDirty) extractData();
+ return m_l;
+ }
+
+ inline const LUMatrixType& matrixU() const
+ {
+ if (m_extractedDataAreDirty) extractData();
+ return m_u;
+ }
+
+ inline const IntColVectorType& permutationP() const
+ {
+ if (m_extractedDataAreDirty) extractData();
+ return m_p;
+ }
+
+ inline const IntRowVectorType& permutationQ() const
+ {
+ if (m_extractedDataAreDirty) extractData();
+ return m_q;
+ }
+
+ /** Computes the sparse Cholesky decomposition of \a matrix
+ * Note that the matrix should be column-major, and in compressed format for best performance.
+ * \sa SparseMatrix::makeCompressed().
+ */
+ template<typename InputMatrixType>
+ void compute(const InputMatrixType& matrix)
+ {
+ if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
+ if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar());
+ grapInput(matrix.derived());
+ analyzePattern_impl();
+ factorize_impl();
+ }
+
+ /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+ *
+ * \sa compute()
+ */
+ template<typename Rhs>
+ inline const internal::solve_retval<UmfPackLU, Rhs> solve(const MatrixBase<Rhs>& b) const
+ {
+ eigen_assert(m_isInitialized && "UmfPackLU is not initialized.");
+ eigen_assert(rows()==b.rows()
+ && "UmfPackLU::solve(): invalid number of rows of the right hand side matrix b");
+ return internal::solve_retval<UmfPackLU, Rhs>(*this, b.derived());
+ }
+
+ /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+ *
+ * \sa compute()
+ */
+ template<typename Rhs>
+ inline const internal::sparse_solve_retval<UmfPackLU, Rhs> solve(const SparseMatrixBase<Rhs>& b) const
+ {
+ eigen_assert(m_isInitialized && "UmfPackLU is not initialized.");
+ eigen_assert(rows()==b.rows()
+ && "UmfPackLU::solve(): invalid number of rows of the right hand side matrix b");
+ return internal::sparse_solve_retval<UmfPackLU, Rhs>(*this, b.derived());
+ }
+
+ /** Performs a symbolic decomposition on the sparcity of \a matrix.
+ *
+ * This function is particularly useful when solving for several problems having the same structure.
+ *
+ * \sa factorize(), compute()
+ */
+ template<typename InputMatrixType>
+ void analyzePattern(const InputMatrixType& matrix)
+ {
+ if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
+ if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar());
+
+ grapInput(matrix.derived());
+
+ analyzePattern_impl();
+ }
+
+ /** Performs a numeric decomposition of \a matrix
+ *
+ * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.
+ *
+ * \sa analyzePattern(), compute()
+ */
+ template<typename InputMatrixType>
+ void factorize(const InputMatrixType& matrix)
+ {
+ eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
+ if(m_numeric)
+ umfpack_free_numeric(&m_numeric,Scalar());
+
+ grapInput(matrix.derived());
+
+ factorize_impl();
+ }
+
+ #ifndef EIGEN_PARSED_BY_DOXYGEN
+ /** \internal */
+ template<typename BDerived,typename XDerived>
+ bool _solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
+ #endif
+
+ Scalar determinant() const;
+
+ void extractData() const;
+
+ protected:
+
+ void init()
+ {
+ m_info = InvalidInput;
+ m_isInitialized = false;
+ m_numeric = 0;
+ m_symbolic = 0;
+ m_outerIndexPtr = 0;
+ m_innerIndexPtr = 0;
+ m_valuePtr = 0;
+ m_extractedDataAreDirty = true;
+ }
+
+ template<typename InputMatrixType>
+ void grapInput_impl(const InputMatrixType& mat, internal::true_type)
+ {
+ m_copyMatrix.resize(mat.rows(), mat.cols());
+ if( ((MatrixType::Flags&RowMajorBit)==RowMajorBit) || sizeof(typename MatrixType::Index)!=sizeof(int) || !mat.isCompressed() )
+ {
+ // non supported input -> copy
+ m_copyMatrix = mat;
+ m_outerIndexPtr = m_copyMatrix.outerIndexPtr();
+ m_innerIndexPtr = m_copyMatrix.innerIndexPtr();
+ m_valuePtr = m_copyMatrix.valuePtr();
+ }
+ else
+ {
+ m_outerIndexPtr = mat.outerIndexPtr();
+ m_innerIndexPtr = mat.innerIndexPtr();
+ m_valuePtr = mat.valuePtr();
+ }
+ }
+
+ template<typename InputMatrixType>
+ void grapInput_impl(const InputMatrixType& mat, internal::false_type)
+ {
+ m_copyMatrix = mat;
+ m_outerIndexPtr = m_copyMatrix.outerIndexPtr();
+ m_innerIndexPtr = m_copyMatrix.innerIndexPtr();
+ m_valuePtr = m_copyMatrix.valuePtr();
+ }
+
+ template<typename InputMatrixType>
+ void grapInput(const InputMatrixType& mat)
+ {
+ grapInput_impl(mat, internal::umfpack_helper_is_sparse_plain<InputMatrixType>());
+ }
+
+ void analyzePattern_impl()
+ {
+ int errorCode = 0;
+ errorCode = umfpack_symbolic(m_copyMatrix.rows(), m_copyMatrix.cols(), m_outerIndexPtr, m_innerIndexPtr, m_valuePtr,
+ &m_symbolic, 0, 0);
+
+ m_isInitialized = true;
+ m_info = errorCode ? InvalidInput : Success;
+ m_analysisIsOk = true;
+ m_factorizationIsOk = false;
+ m_extractedDataAreDirty = true;
+ }
+
+ void factorize_impl()
+ {
+ int errorCode;
+ errorCode = umfpack_numeric(m_outerIndexPtr, m_innerIndexPtr, m_valuePtr,
+ m_symbolic, &m_numeric, 0, 0);
+
+ m_info = errorCode ? NumericalIssue : Success;
+ m_factorizationIsOk = true;
+ m_extractedDataAreDirty = true;
+ }
+
+ // cached data to reduce reallocation, etc.
+ mutable LUMatrixType m_l;
+ mutable LUMatrixType m_u;
+ mutable IntColVectorType m_p;
+ mutable IntRowVectorType m_q;
+
+ UmfpackMatrixType m_copyMatrix;
+ const Scalar* m_valuePtr;
+ const int* m_outerIndexPtr;
+ const int* m_innerIndexPtr;
+ void* m_numeric;
+ void* m_symbolic;
+
+ mutable ComputationInfo m_info;
+ bool m_isInitialized;
+ int m_factorizationIsOk;
+ int m_analysisIsOk;
+ mutable bool m_extractedDataAreDirty;
+
+ private:
+ UmfPackLU(UmfPackLU& ) { }
+};
+
+
+template<typename MatrixType>
+void UmfPackLU<MatrixType>::extractData() const
+{
+ if (m_extractedDataAreDirty)
+ {
+ // get size of the data
+ int lnz, unz, rows, cols, nz_udiag;
+ umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
+
+ // allocate data
+ m_l.resize(rows,(std::min)(rows,cols));
+ m_l.resizeNonZeros(lnz);
+
+ m_u.resize((std::min)(rows,cols),cols);
+ m_u.resizeNonZeros(unz);
+
+ m_p.resize(rows);
+ m_q.resize(cols);
+
+ // extract
+ umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(),
+ m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(),
+ m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
+
+ m_extractedDataAreDirty = false;
+ }
+}
+
+template<typename MatrixType>
+typename UmfPackLU<MatrixType>::Scalar UmfPackLU<MatrixType>::determinant() const
+{
+ Scalar det;
+ umfpack_get_determinant(&det, 0, m_numeric, 0);
+ return det;
+}
+
+template<typename MatrixType>
+template<typename BDerived,typename XDerived>
+bool UmfPackLU<MatrixType>::_solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const
+{
+ const int rhsCols = b.cols();
+ eigen_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major rhs yet");
+ eigen_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major result yet");
+ eigen_assert(b.derived().data() != x.derived().data() && " Umfpack does not support inplace solve");
+
+ int errorCode;
+ for (int j=0; j<rhsCols; ++j)
+ {
+ errorCode = umfpack_solve(UMFPACK_A,
+ m_outerIndexPtr, m_innerIndexPtr, m_valuePtr,
+ &x.col(j).coeffRef(0), &b.const_cast_derived().col(j).coeffRef(0), m_numeric, 0, 0);
+ if (errorCode!=0)
+ return false;
+ }
+
+ return true;
+}
+
+
+namespace internal {
+
+template<typename _MatrixType, typename Rhs>
+struct solve_retval<UmfPackLU<_MatrixType>, Rhs>
+ : solve_retval_base<UmfPackLU<_MatrixType>, Rhs>
+{
+ typedef UmfPackLU<_MatrixType> Dec;
+ EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+ template<typename Dest> void evalTo(Dest& dst) const
+ {
+ dec()._solve(rhs(),dst);
+ }
+};
+
+template<typename _MatrixType, typename Rhs>
+struct sparse_solve_retval<UmfPackLU<_MatrixType>, Rhs>
+ : sparse_solve_retval_base<UmfPackLU<_MatrixType>, Rhs>
+{
+ typedef UmfPackLU<_MatrixType> 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_UMFPACKSUPPORT_H