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

struct	traits< ConstrainedConjugateGradient< _MatrixType, _UpLo, _FilterMatrixType, _Preconditioner > >

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.

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