pub struct CG<'mat, Mat: MatrixOp> { /* private fields */ }Expand description
Conjugate Gradient (CG) method for solving linear systems Ax = b.
The Conjugate Gradient method is an iterative algorithm for solving systems of linear equations where the coefficient matrix A is symmetric and positive definite. It is particularly effective for large sparse systems.
§Algorithm Overview
The CG method generates a sequence of approximations to the solution by minimizing the quadratic form associated with the linear system. At each iteration, it computes a search direction that is conjugate to all previous search directions with respect to the matrix A.
§Convergence
For a symmetric positive definite matrix, the CG method is guaranteed to converge to the exact solution in at most n iterations (where n is the size of the matrix), though in practice it often converges much faster, especially for well-conditioned systems.
§Examples
use nalgebra::{DMatrix, DVector};
use iterative_solvers::CG;
// Create a simple 2x2 symmetric positive definite system
let mat = DMatrix::from_row_slice(2, 2, &[4.0, 1.0, 1.0, 3.0]);
let rhs = DVector::from_vec(vec![1.0, 2.0]);
let abstol = 1e-10;
let reltol = 1e-8;
let mut cg = CG::new(&mat, &rhs, abstol, reltol).unwrap();
let solution = cg.solve();Implementations§
Source§impl<'mat, Mat: MatrixOp> CG<'mat, Mat>
impl<'mat, Mat: MatrixOp> CG<'mat, Mat>
Sourcepub fn new(
mat: &'mat Mat,
rhs: &'mat DVector<f64>,
abstol: f64,
reltol: f64,
) -> IterSolverResult<Self>
pub fn new( mat: &'mat Mat, rhs: &'mat DVector<f64>, abstol: f64, reltol: f64, ) -> IterSolverResult<Self>
Create a new CG solver with the given matrix, right-hand side, and tolerance.
§Arguments
mat- The coefficient matrix A.rhs- The right-hand side vector b.abstol- The absolute tolerance for the residual.reltol- The relative tolerance for the residual.
§Tolerance
The tolerance is the stopping criterion for the CG method. It is the maximum of the absolute tolerance and the relative tolerance.
The stopping criterion is |r_k| ≤ max(reltol * |r_0|, abstol),
where r_k ≈ A x_k - b is the residual at the kth iteration, and r_0 ≈ A x_0 - b is the initial residual.
§Examples
use nalgebra::{DMatrix, DVector};
use iterative_solvers::CG;
// Create a simple 2x2 symmetric positive definite system
let mat = DMatrix::from_row_slice(2, 2, &[4.0, 1.0, 1.0, 3.0]);
let rhs = DVector::from_vec(vec![1.0, 2.0]);
let abstol = 1e-10;
let reltol = 1e-8;
let mut cg = CG::new(&mat, &rhs, abstol, reltol).unwrap();
let solution = cg.solve();Sourcepub fn new_with_initial_guess(
mat: &'mat Mat,
rhs: &'mat DVector<f64>,
initial_guess: DVector<f64>,
abstol: f64,
reltol: f64,
) -> IterSolverResult<Self>
pub fn new_with_initial_guess( mat: &'mat Mat, rhs: &'mat DVector<f64>, initial_guess: DVector<f64>, abstol: f64, reltol: f64, ) -> IterSolverResult<Self>
Create a new CG solver with the given matrix, right-hand side, tolerance, and initial guess.
§Arguments
mat- The coefficient matrix A.rhs- The right-hand side vector b.abstol- The absolute tolerance for the residual.reltol- The relative tolerance for the residual.initial_guess- The initial guess for the solution.
§Tolerance
The tolerance is the stopping criterion for the CG method. It is the maximum of the absolute tolerance and the relative tolerance.
The stopping criterion is |r_k| ≤ max(reltol * |r_0|, abstol),
where r_k ≈ A x_k - b is the residual at the kth iteration, and r_0 ≈ A x_0 - b is the initial residual.
§Initial Guess
The initial guess is the starting point for the CG method. It is used to compute the initial residual.
§Examples
use nalgebra::{DMatrix, DVector};
use iterative_solvers::CG;
// Create a simple 2x2 symmetric positive definite system
let mat = DMatrix::from_row_slice(2, 2, &[4.0, 1.0, 1.0, 3.0]);
let rhs = DVector::from_vec(vec![1.0, 2.0]);
let abstol = 1e-10;
let reltol = 1e-8;
let initial_guess = DVector::from_vec(vec![0.0, 0.0]);
let mut cg = CG::new_with_initial_guess(&mat, &rhs, initial_guess, abstol, reltol).unwrap();
let solution = cg.solve();Sourcepub fn residual_vector(&self) -> &DVector<f64>
pub fn residual_vector(&self) -> &DVector<f64>
Get the residual vector
Sourcepub fn conjugate_direction(&self) -> &DVector<f64>
pub fn conjugate_direction(&self) -> &DVector<f64>
Get the conjugate direction
Trait Implementations§
Source§impl<'mat, Mat: MatrixOp> Iterator for CG<'mat, Mat>
impl<'mat, Mat: MatrixOp> Iterator for CG<'mat, Mat>
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Auto Trait Implementations§
impl<'mat, Mat> Freeze for CG<'mat, Mat>
impl<'mat, Mat> RefUnwindSafe for CG<'mat, Mat>where
Mat: RefUnwindSafe,
impl<'mat, Mat> Send for CG<'mat, Mat>where
Mat: Sync,
impl<'mat, Mat> Sync for CG<'mat, Mat>where
Mat: Sync,
impl<'mat, Mat> Unpin for CG<'mat, Mat>
impl<'mat, Mat> UnwindSafe for CG<'mat, Mat>where
Mat: RefUnwindSafe,
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impl<T> CloneToUninit for Twhere
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Source§impl<SS, SP> SupersetOf<SS> for SPwhere
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