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Adding Sparse matrix with prefactor related changes

This commit is contained in:
iyaz 2013-05-29 17:25:16 +03:00
parent bb67aa6d0a
commit 57d6bf71bb
2 changed files with 412 additions and 403 deletions
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360
Solver_interface/include/CGAL/Eigen_matrix.h
View File

@ -27,225 +27,211 @@
namespace CGAL {
/// The class Eigen_sparse_matrix
/// is a C++ wrapper around Eigen' matrix type SparseMatrix<>.
///
/// This kind of matrix can be either symmetric or not. Symmetric
/// matrices store only the lower triangle.
///
/// @heading Is Model for the Concepts: Model of the SparseLinearAlgebraTraits_d::Matrix concept.
///
/// @heading Parameters:
/// @param T Number type.
template<class T>
struct Eigen_sparse_matrix
{
// Public types
public:
typedef Eigen::SparseMatrix<T> EigenType;
typedef T NT;
// Public operations
public:
/// Create a square matrix initialized with zeros.
Eigen_sparse_matrix(int dim, ///< Matrix dimension.
bool is_symmetric = false) ///< Symmetric/hermitian?
: m_is_already_built(false), m_matrix(dim,dim)
{
CGAL_precondition(dim > 0);
m_is_symmetric = is_symmetric;
// reserve memory for a regular 3D grid
m_triplets.reserve(dim);
}
/// Create a rectangular matrix initialized with zeros.
/// The class Eigen_sparse_matrix
/// is a C++ wrapper around Eigen' matrix type SparseMatrix<>.
///
/// @commentheading Precondition: rows == columns if is_symmetric is true.
Eigen_sparse_matrix(int rows, ///< Number of rows.
int columns, ///< Number of columns.
bool is_symmetric = false) ///< Symmetric/hermitian?
: m_is_already_built(false), m_matrix(rows,columns)
{
CGAL_precondition(rows > 0);
CGAL_precondition(columns > 0);
if (m_is_symmetric) {
CGAL_precondition(rows == columns);
}
m_is_symmetric = is_symmetric;
// reserve memory for a regular 3D grid
m_triplets.reserve(rows);
}
/// Delete this object and the wrapped TAUCS matrix.
~Eigen_sparse_matrix()
{
}
/// Return the matrix number of rows
int row_dimension() const { return m_matrix.rows(); }
/// Return the matrix number of columns
int column_dimension() const { return m_matrix.cols(); }
/// Write access to a matrix coefficient: a_ij <- val.
/// This kind of matrix can be either symmetric or not. Symmetric
/// matrices store only the lower triangle.
///
/// Optimizations:
/// - For symmetric matrices, Eigen_sparse_matrix stores only the lower triangle
/// set_coef() does nothing if (i, j) belongs to the upper triangle.
/// - Caller can optimize this call by setting 'new_coef' to true
/// if the coefficient does not already exist in the matrix.
/// @heading Is Model for the Concepts: Model of the SparseLinearAlgebraTraits_d::Matrix concept.
///
/// @commentheading Preconditions:
/// - 0 <= i < row_dimension().
/// - 0 <= j < column_dimension().
void set_coef(int i, int j, T val, bool new_coef = false)
/// @heading Parameters:
/// @param T Number type.
template<class T, int Options = Eigen::RowMajor>
struct Eigen_sparse_matrix
{
CGAL_precondition(i < row_dimension());
CGAL_precondition(j < column_dimension());
// Public types
public:
if (m_is_symmetric && (j > i))
return;
typedef Eigen::SparseMatrix<T, Options> EigenType;
typedef T NT;
if (m_is_already_built)
m_matrix.coeffRef(i,j)=val;
else
// Public operations
public:
/// Create a square matrix initialized with zeros.
Eigen_sparse_matrix(int dim, ///< Matrix dimension.
bool is_symmetric = false) ///< Symmetric/hermitian?
: m_is_uptodate(false), m_matrix(dim,dim)
{
if ( new_coef == false )
{
assemble_matrix();
m_matrix.coeffRef(i,j)=val;
}
else
m_triplets.push_back(Triplet(i,j,val));
CGAL_precondition(dim > 0);
m_is_symmetric = is_symmetric;
// reserve memory for a regular 3D grid
m_triplets.reserve(dim);
}
}
/// Write access to a matrix coefficient: a_ij <- a_ij+val.
///
/// Optimizations:
/// - For symmetric matrices, Eigen_sparse_matrix stores only the lower triangle
/// add_coef() does nothing if (i, j) belongs to the upper triangle.
///
/// @commentheading Preconditions:
/// - 0 <= i < row_dimension().
/// - 0 <= j < column_dimension().
void add_coef(int i, int j, T val)
{
CGAL_precondition(i < row_dimension());
CGAL_precondition(j < column_dimension());
/// Create a rectangular matrix initialized with zeros.
///
/// @commentheading Precondition: rows == columns if is_symmetric is true.
Eigen_sparse_matrix(int rows, ///< Number of rows.
int columns, ///< Number of columns.
bool is_symmetric = false) ///< Symmetric/hermitian?
: m_is_uptodate(false), m_matrix(rows,columns)
{
CGAL_precondition(rows > 0);
CGAL_precondition(columns > 0);
if (m_is_symmetric) {
CGAL_precondition(rows == columns);
}
if (m_is_symmetric && (j > i))
return;
m_is_symmetric = is_symmetric;
// reserve memory for a regular 3D grid
m_triplets.reserve(rows);
}
/// Delete this object and the wrapped TAUCS matrix.
~Eigen_sparse_matrix()
{
}
/// Return the matrix number of rows
int row_dimension() const { return m_matrix.rows(); }
/// Return the matrix number of columns
int column_dimension() const { return m_matrix.cols(); }
/// Write access to a matrix coefficient: a_ij <- val.
///
/// Optimizations:
/// - For symmetric matrices, Eigen_sparse_matrix stores only the lower triangle
/// set_coef() does nothing if (i, j) belongs to the upper triangle.
/// - Caller can optimize this call by setting 'new_coef' to true
/// if the coefficient does not already exist in the matrix.
///
/// @commentheading Preconditions:
/// - 0 <= i < row_dimension().
/// - 0 <= j < column_dimension().
void set_coef(int i, int j, T val, bool /* new_coef */ = false)
{
CGAL_precondition(i < row_dimension());
CGAL_precondition(j < column_dimension());
if (m_is_symmetric && (j > i))
return;
if (m_is_already_built)
m_matrix.coeffRef(i,j)+=val;
else
m_triplets.push_back(Triplet(i,j,val));
}
m_is_uptodate = false;
}
void assemble_matrix() const
{
m_matrix.setFromTriplets(m_triplets.begin(), m_triplets.end());
m_is_already_built = true;
m_triplets.clear(); //the matrix is built and will not be rebuilt
}
/// Write access to a matrix coefficient: a_ij <- a_ij+val.
///
/// Optimizations:
/// - For symmetric matrices, Eigen_sparse_matrix stores only the lower triangle
/// add_coef() does nothing if (i, j) belongs to the upper triangle.
///
/// @commentheading Preconditions:
/// - 0 <= i < row_dimension().
/// - 0 <= j < column_dimension().
void add_coef(int i, int j, T val)
{
CGAL_precondition(i < row_dimension());
CGAL_precondition(j < column_dimension());
const EigenType& eigen_object() const
{
if(!m_is_already_built) assemble_matrix();
if (m_is_symmetric && (j > i))
return;
// turns the matrix into compressed mode:
// -> release some memory
// -> required for some external solvers
m_matrix.makeCompressed();
return m_matrix;
}
private:
/// Eigen_sparse_matrix cannot be copied (yet)
Eigen_sparse_matrix(const Eigen_sparse_matrix& rhs);
Eigen_sparse_matrix& operator=(const Eigen_sparse_matrix& rhs);
// Fields
private:
mutable bool m_is_already_built;
typedef Eigen::Triplet<T,int> Triplet;
mutable std::vector<Triplet> m_triplets;
mutable EigenType m_matrix;
// Symmetric/hermitian?
bool m_is_symmetric;
}; // Eigen_sparse_matrix
m_triplets.push_back(Triplet(i,j,val));
m_is_uptodate = false;
}
/// The class Eigen_sparse_symmetric_matrix is a C++ wrapper
/// around a Eigen sparse matrix (type Eigen::SparseMatrix).
///
/// Symmetric matrices store only the lower triangle.
///
/// @heading Is Model for the Concepts: Model of the SparseLinearAlgebraTraits_d::Matrix concept.
///
/// @heading Parameters:
/// @param T Number type.
const EigenType& eigen_object() const
{
if(!m_is_uptodate)
{
m_matrix.setFromTriplets(m_triplets.begin(), m_triplets.end());
m_is_uptodate = true;
}
// turns the matrix into compressed mode:
// -> release some memory
// -> required for some external solvers
m_matrix.makeCompressed();
return m_matrix;
}
template<class T>
struct Eigen_sparse_symmetric_matrix
: public Eigen_sparse_matrix<T>
{
// Public types
typedef T NT;
private:
// Public operations
/// Create a square *symmetric* matrix initialized with zeros.
Eigen_sparse_symmetric_matrix(int dim) ///< Matrix dimension.
: Eigen_sparse_matrix<T>(dim, true /* symmetric */)
{
}
/// Eigen_sparse_matrix cannot be copied (yet)
Eigen_sparse_matrix(const Eigen_sparse_matrix& rhs);
Eigen_sparse_matrix& operator=(const Eigen_sparse_matrix& rhs);
/// Create a square *symmetric* matrix initialized with zeros.
// Fields
private:
mutable bool m_is_uptodate;
typedef Eigen::Triplet<T,int> Triplet;
mutable std::vector<Triplet> m_triplets;
mutable EigenType m_matrix;
// Symmetric/hermitian?
bool m_is_symmetric;
}; // Eigen_sparse_matrix
/// The class Eigen_sparse_symmetric_matrix is a C++ wrapper
/// around a Eigen sparse matrix (type Eigen::SparseMatrix).
///
/// @commentheading Precondition: rows == columns.
Eigen_sparse_symmetric_matrix(int rows, ///< Number of rows.
int columns) ///< Number of columns.
: Eigen_sparse_matrix<T>(rows, columns, true /* symmetric */)
/// Symmetric matrices store only the lower triangle.
///
/// @heading Is Model for the Concepts: Model of the SparseLinearAlgebraTraits_d::Matrix concept.
///
/// @heading Parameters:
/// @param T Number type.
template<class T>
struct Eigen_sparse_symmetric_matrix
: public Eigen_sparse_matrix<T>
{
}
};
// Public types
typedef T NT;
template <class FT>
struct Eigen_matrix : public ::Eigen::Matrix<FT,::Eigen::Dynamic,::Eigen::Dynamic>
{
typedef ::Eigen::Matrix<FT,::Eigen::Dynamic,::Eigen::Dynamic> EigenType;
// Public operations
Eigen_matrix( std::size_t n1, std::size_t n2):EigenType(n1,n2){}
/// Create a square *symmetric* matrix initialized with zeros.
Eigen_sparse_symmetric_matrix(int dim) ///< Matrix dimension.
: Eigen_sparse_matrix<T>(dim, true /* symmetric */)
{
}
std::size_t number_of_rows () const {return this->rows();}
/// Create a square *symmetric* matrix initialized with zeros.
///
/// @commentheading Precondition: rows == columns.
Eigen_sparse_symmetric_matrix(int rows, ///< Number of rows.
int columns) ///< Number of columns.
: Eigen_sparse_matrix<T>(rows, columns, true /* symmetric */)
{
}
};
std::size_t number_of_columns () const {return this->cols();}
template <class FT>
struct Eigen_matrix : public ::Eigen::Matrix<FT,::Eigen::Dynamic,::Eigen::Dynamic>
{
typedef ::Eigen::Matrix<FT,::Eigen::Dynamic,::Eigen::Dynamic> EigenType;
FT operator()( std::size_t i , std::size_t j ) const {return this->operator()(i,j);}
Eigen_matrix( std::size_t n1, std::size_t n2):EigenType(n1,n2){}
void set( std::size_t i, std::size_t j,FT value){
this->coeffRef(i,j)=value;
}
std::size_t number_of_rows () const {return this->rows();}
const EigenType& eigen_object() const{
return static_cast<const EigenType&>(*this);
}
std::size_t number_of_columns () const {return this->cols();}
};
FT operator()( std::size_t i , std::size_t j ) const {return this->operator()(i,j);}
void set( std::size_t i, std::size_t j,FT value){
this->coeffRef(i,j)=value;
}
const EigenType& eigen_object() const{
return static_cast<const EigenType&>(*this);
}
};
} //namespace CGAL

183
Solver_interface/include/CGAL/Eigen_solver_traits.h
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@ -22,6 +22,7 @@
#include <CGAL/basic.h> // include basic.h before testing #defines
#include <Eigen/Sparse>
#include <Eigen/SparseLU>
#include <CGAL/Eigen_matrix.h>
#include <CGAL/Eigen_vector.h>
#include <boost/shared_ptr.hpp>
@ -29,122 +30,144 @@
namespace CGAL {
namespace internal {
template <class EigenSolver,class FT>
struct Get_eigen_matrix{
typedef Eigen_sparse_matrix<FT> type;
};
namespace internal {
template <class EigenSolver,class FT>
struct Get_eigen_matrix{
typedef Eigen_sparse_matrix<FT> type;
};
template <class FT,class EigenMatrix>
struct Get_eigen_matrix< ::Eigen::ConjugateGradient<EigenMatrix>,FT>{
typedef Eigen_sparse_symmetric_matrix<FT> type;
};
template <class FT,class EigenMatrix>
struct Get_eigen_matrix< ::Eigen::ConjugateGradient<EigenMatrix>,FT>{
typedef Eigen_sparse_symmetric_matrix<FT> type;
};
template <class FT,class EigenMatrix>
struct Get_eigen_matrix< ::Eigen::SimplicialCholesky<EigenMatrix>,FT>{
typedef Eigen_sparse_symmetric_matrix<FT> type;
};
} //internal
template <class FT,class EigenMatrix>
struct Get_eigen_matrix< ::Eigen::SimplicialCholesky<EigenMatrix>,FT>{
typedef Eigen_sparse_symmetric_matrix<FT> type;
};
/// The class Eigen_solver_traits
/// is a generic traits class for solving asymmetric or symmetric positive definite (SPD)
/// sparse linear systems using one of the Eigen solvers.
/// The default solver is the iterative bi-congugate gradient stabilized solver
/// Eigen::BiCGSTAB for double.
///
/// @heading Is Model for the Concepts: Model of the SparseLinearAlgebraTraits_d concept.
template <class FT, class EigenMatrix, class EigenOrdering>
struct Get_eigen_matrix< ::Eigen::SparseLU<EigenMatrix, EigenOrdering >, FT> {
typedef Eigen_sparse_matrix<FT, ::Eigen::ColMajor> type;
};
} //internal
template<class EigenSolverT = Eigen::BiCGSTAB<Eigen_sparse_matrix<double>::EigenType> >
class Eigen_solver_traits
{
typedef typename EigenSolverT::Scalar Scalar;
// Public types
public:
typedef Scalar NT;
typedef typename internal::Get_eigen_matrix<EigenSolverT,NT>::type Matrix;
typedef Eigen_vector<Scalar> Vector;
/// The class Eigen_solver_traits
/// is a generic traits class for solving asymmetric or symmetric positive definite (SPD)
/// sparse linear systems using one of the Eigen solvers.
/// The default solver is the iterative bi-congugate gradient stabilized solver
/// Eigen::BiCGSTAB for double.
///
/// @heading Is Model for the Concepts: Model of the SparseLinearAlgebraTraits_d concept.
template<class EigenSolverT = Eigen::BiCGSTAB<Eigen_sparse_matrix<double>::EigenType> >
class Eigen_solver_traits
{
typedef typename EigenSolverT::Scalar Scalar;
// Public types
public:
typedef Scalar NT;
typedef typename internal::Get_eigen_matrix<EigenSolverT,NT>::type Matrix;
typedef Eigen_vector<Scalar> Vector;
// Public operations
public:
// Public operations
public:
Eigen_solver_traits(): m_solver_sptr(new EigenSolverT)
{
}
Eigen_solver_traits():m_mat(NULL), m_solver_sptr(new EigenSolverT)
{
}
EigenSolverT& solver() { return *m_solver_sptr; }
EigenSolverT& solver() { return *m_solver_sptr; }
/// Solve the sparse linear system "A*X = B".
/// Return true on success. The solution is then (1/D) * X.
///
/// @commentheading Preconditions:
/// - A.row_dimension() == B.dimension().
/// - A.column_dimension() == X.dimension().
bool linear_solver(const Matrix& A, const Vector& B, Vector& X, NT& D)
{
/// Solve the sparse linear system "A*X = B".
/// Return true on success. The solution is then (1/D) * X.
///
/// @commentheading Preconditions:
/// - A.row_dimension() == B.dimension().
/// - A.column_dimension() == X.dimension().
bool linear_solver(const Matrix& A, const Vector& B, Vector& X, NT& D)
{
D = 1; // Eigen does not support homogeneous coordinates
m_solver_sptr->compute(A.eigen_object());
if(m_solver_sptr->info() != Eigen::Success)
return false;
return false;
X = m_solver_sptr->solve(B);
return m_solver_sptr->info() == Eigen::Success;
}
protected:
boost::shared_ptr<EigenSolverT> m_solver_sptr;
}
};
bool pre_factor (const Matrix& A, NT& D)
{
D = 1;
//specilization of the solver for BiCGSTAB as for surface parameterization, the
//intializer should be a vector of one's (this was the case in 3.1-alpha but not in the official 3.1).
template<>
class Eigen_solver_traits< Eigen::BiCGSTAB<Eigen_sparse_matrix<double>::EigenType> >
{
typedef Eigen::BiCGSTAB<Eigen_sparse_matrix<double>::EigenType> EigenSolverT;
typedef EigenSolverT::Scalar Scalar;
// Public types
public:
typedef Scalar NT;
typedef internal::Get_eigen_matrix<EigenSolverT,NT>::type Matrix;
typedef Eigen_vector<Scalar> Vector;
m_mat = &A.eigen_object();
solver().compute(*m_mat);
return solver().info() == Eigen::Success;
}
bool linear_solver(const Vector& B, Vector& X)
{
CGAL_precondition(m_mat!=NULL); //pre_factor should have been called first
X = solver().solve(B);
return solver().info() == Eigen::Success;
}
protected:
const typename Matrix::EigenType* m_mat;
boost::shared_ptr<EigenSolverT> m_solver_sptr;
};
//specilization of the solver for BiCGSTAB as for surface parameterization, the
//intializer should be a vector of one's (this was the case in 3.1-alpha but not in the official 3.1).
template<>
class Eigen_solver_traits< Eigen::BiCGSTAB<Eigen_sparse_matrix<double>::EigenType> >
{
typedef Eigen::BiCGSTAB<Eigen_sparse_matrix<double>::EigenType> EigenSolverT;
typedef EigenSolverT::Scalar Scalar;
// Public types
public:
typedef Scalar NT;
typedef internal::Get_eigen_matrix<EigenSolverT,NT>::type Matrix;
typedef Eigen_vector<Scalar> Vector;
// Public operations
public:
// Public operations
public:
Eigen_solver_traits(): m_solver_sptr(new EigenSolverT)
{
}
Eigen_solver_traits(): m_solver_sptr(new EigenSolverT)
{
}
EigenSolverT& solver() { return *m_solver_sptr; }
EigenSolverT& solver() { return *m_solver_sptr; }
/// Solve the sparse linear system "A*X = B".
/// Return true on success. The solution is then (1/D) * X.
///
/// @commentheading Preconditions:
/// - A.row_dimension() == B.dimension().
/// - A.column_dimension() == X.dimension().
bool linear_solver(const Matrix& A, const Vector& B, Vector& X, NT& D)
{
/// Solve the sparse linear system "A*X = B".
/// Return true on success. The solution is then (1/D) * X.
///
/// @commentheading Preconditions:
/// - A.row_dimension() == B.dimension().
/// - A.column_dimension() == X.dimension().
bool linear_solver(const Matrix& A, const Vector& B, Vector& X, NT& D)
{
D = 1; // Eigen does not support homogeneous coordinates
m_solver_sptr->compute(A.eigen_object());
if(m_solver_sptr->info() != Eigen::Success)
return false;
return false;
X.setOnes(B.rows());
X = m_solver_sptr->solveWithGuess(B,X);
return m_solver_sptr->info() == Eigen::Success;
}
protected:
boost::shared_ptr<EigenSolverT> m_solver_sptr;
}
protected:
boost::shared_ptr<EigenSolverT> m_solver_sptr;
};
};
} //namespace CGAL