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Tobias Wood
2023-11-29 11:12:48 +00:00
parent 9ea520fc45
commit f38e16c193
534 changed files with 103368 additions and 116934 deletions
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383
Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
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@@ -17,191 +17,157 @@ namespace Eigen {
namespace internal {
template<typename MatrixType>
struct is_ref_compatible_impl
{
private:
template <typename MatrixType>
struct is_ref_compatible_impl {
private:
template <typename T0>
struct any_conversion
{
template <typename T> any_conversion(const volatile T&);
template <typename T> any_conversion(T&);
struct any_conversion {
template <typename T>
any_conversion(const volatile T&);
template <typename T>
any_conversion(T&);
};
struct yes {
int a[1];
};
struct no {
int a[2];
};
struct yes {int a[1];};
struct no {int a[2];};
template<typename T>
template <typename T>
static yes test(const Ref<const T>&, int);
template<typename T>
static no test(any_conversion<T>, ...);
template <typename T>
static no test(any_conversion<T>, ...);
public:
public:
static MatrixType ms_from;
enum { value = sizeof(test<MatrixType>(ms_from, 0))==sizeof(yes) };
enum { value = sizeof(test<MatrixType>(ms_from, 0)) == sizeof(yes) };
};
template<typename MatrixType>
struct is_ref_compatible
{
template <typename MatrixType>
struct is_ref_compatible {
enum { value = is_ref_compatible_impl<remove_all_t<MatrixType>>::value };
};
template<typename MatrixType, bool MatrixFree = !internal::is_ref_compatible<MatrixType>::value>
template <typename MatrixType, bool MatrixFree = !internal::is_ref_compatible<MatrixType>::value>
class generic_matrix_wrapper;
// We have an explicit matrix at hand, compatible with Ref<>
template<typename MatrixType>
class generic_matrix_wrapper<MatrixType,false>
{
public:
template <typename MatrixType>
class generic_matrix_wrapper<MatrixType, false> {
public:
typedef Ref<const MatrixType> ActualMatrixType;
template<int UpLo> struct ConstSelfAdjointViewReturnType {
template <int UpLo>
struct ConstSelfAdjointViewReturnType {
typedef typename ActualMatrixType::template ConstSelfAdjointViewReturnType<UpLo>::Type Type;
};
enum {
MatrixFree = false
};
enum { MatrixFree = false };
generic_matrix_wrapper()
: m_dummy(0,0), m_matrix(m_dummy)
{}
generic_matrix_wrapper() : m_dummy(0, 0), m_matrix(m_dummy) {}
template<typename InputType>
generic_matrix_wrapper(const InputType &mat)
: m_matrix(mat)
{}
template <typename InputType>
generic_matrix_wrapper(const InputType& mat) : m_matrix(mat) {}
const ActualMatrixType& matrix() const
{
return m_matrix;
}
const ActualMatrixType& matrix() const { return m_matrix; }
template<typename MatrixDerived>
void grab(const EigenBase<MatrixDerived> &mat)
{
template <typename MatrixDerived>
void grab(const EigenBase<MatrixDerived>& mat) {
internal::destroy_at(&m_matrix);
internal::construct_at(&m_matrix, mat.derived());
}
void grab(const Ref<const MatrixType> &mat)
{
if(&(mat.derived()) != &m_matrix)
{
void grab(const Ref<const MatrixType>& mat) {
if (&(mat.derived()) != &m_matrix) {
internal::destroy_at(&m_matrix);
internal::construct_at(&m_matrix, mat);
}
}
protected:
MatrixType m_dummy; // used to default initialize the Ref<> object
protected:
MatrixType m_dummy; // used to default initialize the Ref<> object
ActualMatrixType m_matrix;
};
// MatrixType is not compatible with Ref<> -> matrix-free wrapper
template<typename MatrixType>
class generic_matrix_wrapper<MatrixType,true>
{
public:
template <typename MatrixType>
class generic_matrix_wrapper<MatrixType, true> {
public:
typedef MatrixType ActualMatrixType;
template<int UpLo> struct ConstSelfAdjointViewReturnType
{
template <int UpLo>
struct ConstSelfAdjointViewReturnType {
typedef ActualMatrixType Type;
};
enum {
MatrixFree = true
};
enum { MatrixFree = true };
generic_matrix_wrapper()
: mp_matrix(0)
{}
generic_matrix_wrapper() : mp_matrix(0) {}
generic_matrix_wrapper(const MatrixType &mat)
: mp_matrix(&mat)
{}
generic_matrix_wrapper(const MatrixType& mat) : mp_matrix(&mat) {}
const ActualMatrixType& matrix() const
{
return *mp_matrix;
}
const ActualMatrixType& matrix() const { return *mp_matrix; }
void grab(const MatrixType &mat)
{
mp_matrix = &mat;
}
void grab(const MatrixType& mat) { mp_matrix = &mat; }
protected:
const ActualMatrixType *mp_matrix;
protected:
const ActualMatrixType* mp_matrix;
};
}
} // namespace internal
/** \ingroup IterativeLinearSolvers_Module
* \brief Base class for linear iterative solvers
*
* \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
*/
template< typename Derived>
class IterativeSolverBase : public SparseSolverBase<Derived>
{
protected:
* \brief Base class for linear iterative solvers
*
* \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
*/
template <typename Derived>
class IterativeSolverBase : public SparseSolverBase<Derived> {
protected:
typedef SparseSolverBase<Derived> Base;
using Base::m_isInitialized;
public:
public:
typedef typename internal::traits<Derived>::MatrixType MatrixType;
typedef typename internal::traits<Derived>::Preconditioner Preconditioner;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::StorageIndex StorageIndex;
typedef typename MatrixType::RealScalar RealScalar;
enum {
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
};
public:
enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
public:
using Base::derived;
/** Default constructor. */
IterativeSolverBase()
{
init();
}
IterativeSolverBase() { init(); }
/** Initialize the solver with matrix \a A for further \c Ax=b solving.
*
* This constructor is a shortcut for the default constructor followed
* by a call to compute().
*
* \warning this class stores a reference to the matrix A as well as some
* precomputed values that depend on it. Therefore, if \a A is changed
* this class becomes invalid. Call compute() to update it with the new
* matrix A, or modify a copy of A.
*/
template<typename MatrixDerived>
explicit IterativeSolverBase(const EigenBase<MatrixDerived>& A)
: m_matrixWrapper(A.derived())
{
*
* This constructor is a shortcut for the default constructor followed
* by a call to compute().
*
* \warning this class stores a reference to the matrix A as well as some
* precomputed values that depend on it. Therefore, if \a A is changed
* this class becomes invalid. Call compute() to update it with the new
* matrix A, or modify a copy of A.
*/
template <typename MatrixDerived>
explicit IterativeSolverBase(const EigenBase<MatrixDerived>& A) : m_matrixWrapper(A.derived()) {
init();
compute(matrix());
}
IterativeSolverBase(IterativeSolverBase&&) = default;
~IterativeSolverBase() {}
/** Initializes the iterative solver for the sparsity pattern of the matrix \a A for further solving \c Ax=b problems.
*
* Currently, this function mostly calls analyzePattern on the preconditioner. In the future
* we might, for instance, implement column reordering for faster matrix vector products.
*/
template<typename MatrixDerived>
Derived& analyzePattern(const EigenBase<MatrixDerived>& A)
{
*
* Currently, this function mostly calls analyzePattern on the preconditioner. In the future
* we might, for instance, implement column reordering for faster matrix vector products.
*/
template <typename MatrixDerived>
Derived& analyzePattern(const EigenBase<MatrixDerived>& A) {
grab(A.derived());
m_preconditioner.analyzePattern(matrix());
m_isInitialized = true;
@@ -210,18 +176,18 @@ public:
return derived();
}
/** Initializes the iterative solver with the numerical values of the matrix \a A for further solving \c Ax=b problems.
*
* Currently, this function mostly calls factorize on the preconditioner.
*
* \warning this class stores a reference to the matrix A as well as some
* precomputed values that depend on it. Therefore, if \a A is changed
* this class becomes invalid. Call compute() to update it with the new
* matrix A, or modify a copy of A.
*/
template<typename MatrixDerived>
Derived& factorize(const EigenBase<MatrixDerived>& A)
{
/** Initializes the iterative solver with the numerical values of the matrix \a A for further solving \c Ax=b
* problems.
*
* Currently, this function mostly calls factorize on the preconditioner.
*
* \warning this class stores a reference to the matrix A as well as some
* precomputed values that depend on it. Therefore, if \a A is changed
* this class becomes invalid. Call compute() to update it with the new
* matrix A, or modify a copy of A.
*/
template <typename MatrixDerived>
Derived& factorize(const EigenBase<MatrixDerived>& A) {
eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
grab(A.derived());
m_preconditioner.factorize(matrix());
@@ -231,18 +197,17 @@ public:
}
/** Initializes the iterative solver with the matrix \a A for further solving \c Ax=b problems.
*
* Currently, this function mostly initializes/computes the preconditioner. In the future
* we might, for instance, implement column reordering for faster matrix vector products.
*
* \warning this class stores a reference to the matrix A as well as some
* precomputed values that depend on it. Therefore, if \a A is changed
* this class becomes invalid. Call compute() to update it with the new
* matrix A, or modify a copy of A.
*/
template<typename MatrixDerived>
Derived& compute(const EigenBase<MatrixDerived>& A)
{
*
* Currently, this function mostly initializes/computes the preconditioner. In the future
* we might, for instance, implement column reordering for faster matrix vector products.
*
* \warning this class stores a reference to the matrix A as well as some
* precomputed values that depend on it. Therefore, if \a A is changed
* this class becomes invalid. Call compute() to update it with the new
* matrix A, or modify a copy of A.
*/
template <typename MatrixDerived>
Derived& compute(const EigenBase<MatrixDerived>& A) {
grab(A.derived());
m_preconditioner.compute(matrix());
m_isInitialized = true;
@@ -259,17 +224,16 @@ public:
EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return matrix().cols(); }
/** \returns the tolerance threshold used by the stopping criteria.
* \sa setTolerance()
*/
* \sa setTolerance()
*/
RealScalar tolerance() const { return m_tolerance; }
/** Sets the tolerance threshold used by the stopping criteria.
*
* This value is used as an upper bound to the relative residual error: |Ax-b|/|b|.
* The default value is the machine precision given by NumTraits<Scalar>::epsilon()
*/
Derived& setTolerance(const RealScalar& tolerance)
{
*
* This value is used as an upper bound to the relative residual error: |Ax-b|/|b|.
* The default value is the machine precision given by NumTraits<Scalar>::epsilon()
*/
Derived& setTolerance(const RealScalar& tolerance) {
m_tolerance = tolerance;
return derived();
}
@@ -281,137 +245,122 @@ public:
const Preconditioner& preconditioner() const { return m_preconditioner; }
/** \returns the max number of iterations.
* It is either the value set by setMaxIterations or, by default,
* twice the number of columns of the matrix.
*/
Index maxIterations() const
{
return (m_maxIterations<0) ? 2*matrix().cols() : m_maxIterations;
}
* It is either the value set by setMaxIterations or, by default,
* twice the number of columns of the matrix.
*/
Index maxIterations() const { return (m_maxIterations < 0) ? 2 * matrix().cols() : m_maxIterations; }
/** Sets the max number of iterations.
* Default is twice the number of columns of the matrix.
*/
Derived& setMaxIterations(Index maxIters)
{
* Default is twice the number of columns of the matrix.
*/
Derived& setMaxIterations(Index maxIters) {
m_maxIterations = maxIters;
return derived();
}
/** \returns the number of iterations performed during the last solve */
Index iterations() const
{
Index iterations() const {
eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
return m_iterations;
}
/** \returns the tolerance error reached during the last solve.
* It is a close approximation of the true relative residual error |Ax-b|/|b|.
*/
RealScalar error() const
{
* It is a close approximation of the true relative residual error |Ax-b|/|b|.
*/
RealScalar error() const {
eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
return m_error;
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A
* and \a x0 as an initial solution.
*
* \sa solve(), compute()
*/
template<typename Rhs,typename Guess>
inline const SolveWithGuess<Derived, Rhs, Guess>
solveWithGuess(const MatrixBase<Rhs>& b, const Guess& x0) const
{
* and \a x0 as an initial solution.
*
* \sa solve(), compute()
*/
template <typename Rhs, typename Guess>
inline const SolveWithGuess<Derived, Rhs, Guess> solveWithGuess(const MatrixBase<Rhs>& b, const Guess& x0) const {
eigen_assert(m_isInitialized && "Solver is not initialized.");
eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
eigen_assert(derived().rows() == b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
return SolveWithGuess<Derived, Rhs, Guess>(derived(), b.derived(), x0);
}
/** \returns Success if the iterations converged, and NoConvergence otherwise. */
ComputationInfo info() const
{
ComputationInfo info() const {
eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
return m_info;
}
/** \internal */
template<typename Rhs, typename DestDerived>
void _solve_with_guess_impl(const Rhs& b, SparseMatrixBase<DestDerived> &aDest) const
{
eigen_assert(rows()==b.rows());
template <typename Rhs, typename DestDerived>
void _solve_with_guess_impl(const Rhs& b, SparseMatrixBase<DestDerived>& aDest) const {
eigen_assert(rows() == b.rows());
Index rhsCols = b.cols();
Index size = b.rows();
DestDerived& dest(aDest.derived());
typedef typename DestDerived::Scalar DestScalar;
Eigen::Matrix<DestScalar,Dynamic,1> tb(size);
Eigen::Matrix<DestScalar,Dynamic,1> tx(cols());
Eigen::Matrix<DestScalar, Dynamic, 1> tb(size);
Eigen::Matrix<DestScalar, Dynamic, 1> tx(cols());
// We do not directly fill dest because sparse expressions have to be free of aliasing issue.
// For non square least-square problems, b and dest might not have the same size whereas they might alias each-other.
typename DestDerived::PlainObject tmp(cols(),rhsCols);
// For non square least-square problems, b and dest might not have the same size whereas they might alias
// each-other.
typename DestDerived::PlainObject tmp(cols(), rhsCols);
ComputationInfo global_info = Success;
for(Index k=0; k<rhsCols; ++k)
{
for (Index k = 0; k < rhsCols; ++k) {
tb = b.col(k);
tx = dest.col(k);
derived()._solve_vector_with_guess_impl(tb,tx);
derived()._solve_vector_with_guess_impl(tb, tx);
tmp.col(k) = tx.sparseView(0);
// The call to _solve_vector_with_guess_impl updates m_info, so if it failed for a previous column
// we need to restore it to the worst value.
if(m_info==NumericalIssue)
if (m_info == NumericalIssue)
global_info = NumericalIssue;
else if(m_info==NoConvergence)
else if (m_info == NoConvergence)
global_info = NoConvergence;
}
m_info = global_info;
dest.swap(tmp);
}
template<typename Rhs, typename DestDerived>
std::enable_if_t<Rhs::ColsAtCompileTime!=1 && DestDerived::ColsAtCompileTime!=1>
_solve_with_guess_impl(const Rhs& b, MatrixBase<DestDerived> &aDest) const
{
eigen_assert(rows()==b.rows());
template <typename Rhs, typename DestDerived>
std::enable_if_t<Rhs::ColsAtCompileTime != 1 && DestDerived::ColsAtCompileTime != 1> _solve_with_guess_impl(
const Rhs& b, MatrixBase<DestDerived>& aDest) const {
eigen_assert(rows() == b.rows());
Index rhsCols = b.cols();
DestDerived& dest(aDest.derived());
ComputationInfo global_info = Success;
for(Index k=0; k<rhsCols; ++k)
{
typename DestDerived::ColXpr xk(dest,k);
typename Rhs::ConstColXpr bk(b,k);
derived()._solve_vector_with_guess_impl(bk,xk);
for (Index k = 0; k < rhsCols; ++k) {
typename DestDerived::ColXpr xk(dest, k);
typename Rhs::ConstColXpr bk(b, k);
derived()._solve_vector_with_guess_impl(bk, xk);
// The call to _solve_vector_with_guess updates m_info, so if it failed for a previous column
// we need to restore it to the worst value.
if(m_info==NumericalIssue)
if (m_info == NumericalIssue)
global_info = NumericalIssue;
else if(m_info==NoConvergence)
else if (m_info == NoConvergence)
global_info = NoConvergence;
}
m_info = global_info;
}
template<typename Rhs, typename DestDerived>
std::enable_if_t<Rhs::ColsAtCompileTime==1 || DestDerived::ColsAtCompileTime==1>
_solve_with_guess_impl(const Rhs& b, MatrixBase<DestDerived> &dest) const
{
derived()._solve_vector_with_guess_impl(b,dest.derived());
template <typename Rhs, typename DestDerived>
std::enable_if_t<Rhs::ColsAtCompileTime == 1 || DestDerived::ColsAtCompileTime == 1> _solve_with_guess_impl(
const Rhs& b, MatrixBase<DestDerived>& dest) const {
derived()._solve_vector_with_guess_impl(b, dest.derived());
}
/** \internal default initial guess = 0 */
template<typename Rhs,typename Dest>
void _solve_impl(const Rhs& b, Dest& x) const
{
template <typename Rhs, typename Dest>
void _solve_impl(const Rhs& b, Dest& x) const {
x.setZero();
derived()._solve_with_guess_impl(b,x);
derived()._solve_with_guess_impl(b, x);
}
protected:
void init()
{
protected:
void init() {
m_isInitialized = false;
m_analysisIsOk = false;
m_factorizationIsOk = false;
@@ -422,14 +371,10 @@ protected:
typedef internal::generic_matrix_wrapper<MatrixType> MatrixWrapper;
typedef typename MatrixWrapper::ActualMatrixType ActualMatrixType;
const ActualMatrixType& matrix() const
{
return m_matrixWrapper.matrix();
}
const ActualMatrixType& matrix() const { return m_matrixWrapper.matrix(); }
template<typename InputType>
void grab(const InputType &A)
{
template <typename InputType>
void grab(const InputType& A) {
m_matrixWrapper.grab(A);
}
@@ -445,6 +390,6 @@ protected:
mutable bool m_analysisIsOk, m_factorizationIsOk;
};
} // end namespace Eigen
} // end namespace Eigen
#endif // EIGEN_ITERATIVE_SOLVER_BASE_H
#endif // EIGEN_ITERATIVE_SOLVER_BASE_H