Eigen::internal Namespace Reference

Classes
struct	traits< ConstrainedConjugateGradient< _MatrixType, _UpLo, _FilterMatrixType, _Preconditioner > >
struct	solve_retval< ConstrainedConjugateGradient< _MatrixType, _UpLo, _Filter, _Preconditioner >, Rhs >

Functions
template<typename MatrixType, typename Rhs, typename Dest, typename FilterMatrixType, typename Preconditioner>
EIGEN_DONT_INLINE void	constrained_conjugate_gradient (const MatrixType &mat, const Rhs &rhs, Dest &x, const FilterMatrixType &filter, const Preconditioner &precond, int &iters, typename Dest::RealScalar &tol_error)

Function Documentation

constrained_conjugate_gradient()

template<typename MatrixType, typename Rhs, typename Dest, typename FilterMatrixType, typename Preconditioner>

EIGEN_DONT_INLINE void Eigen::internal::constrained_conjugate_gradient	(	const MatrixType &	mat,
		const Rhs &	rhs,
		Dest &	x,
		const FilterMatrixType &	filter,
		const Preconditioner &	precond,
		int &	iters,
		typename Dest::RealScalar &	tol_error )

Low-level conjugate gradient algorithm

Parameters

mat	The matrix A
rhs	The right hand side vector b
x	On input and initial solution, on output the computed solution.
precond	A preconditioner being able to efficiently solve for an approximation of Ax=b (regardless of b)
iters	On input the max number of iteration, on output the number of performed iterations.
tol_error	On input the tolerance error, on output an estimation of the relative error.

Definition at line 36 of file ConstrainedConjugateGradient.h.

References beta(), filter, i, rhs, sqrt, x, and z().

Referenced by Eigen::ConstrainedConjugateGradient< _MatrixType, _UpLo, _Filter, _Preconditioner >::_solveWithGuess().