Struct IterativeSchurSolver 

pub struct IterativeSchurSolver { /* private fields */ }

Iterative Schur complement solver using Preconditioned Conjugate Gradients

Implementations

impl IterativeSchurSolver

pub fn new() -> Self

Create a new iterative Schur solver with default parameters Default: Schur-Jacobi preconditioner, 500 max iterations, 1e-9 relative tolerance These tighter settings match Ceres Solver behavior for accurate step computation.

pub fn with_cg_params(max_iterations: usize, tolerance: f64) -> Self

Create solver with custom CG parameters

pub fn with_config( max_iterations: usize, tolerance: f64, preconditioner: SchurPreconditioner, ) -> Self

Create solver with full configuration

Trait Implementations

impl Clone for IterativeSchurSolver

fn clone(&self) -> IterativeSchurSolver

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for IterativeSchurSolver

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for IterativeSchurSolver

fn default() -> Self

Returns the “default value” for a type.

impl StructuredSparseLinearSolver for IterativeSchurSolver

fn initialize_structure( &mut self, variables: &HashMap<String, VariableEnum>, variable_index_map: &HashMap<String, usize>, ) -> LinAlgResult<()>

Initialize the solver’s block structure from problem variables.

fn solve_normal_equation( &mut self, residuals: &Mat<f64>, jacobian: &SparseColMat<usize, f64>, ) -> LinAlgResult<Mat<f64>>

Solve the normal equation: (J^T * J) * dx = -J^T * r

fn solve_augmented_equation( &mut self, residuals: &Mat<f64>, jacobian: &SparseColMat<usize, f64>, lambda: f64, ) -> LinAlgResult<Mat<f64>>

Solve the augmented equation: (J^T * J + λI) * dx = -J^T * r

fn get_hessian(&self) -> Option<&SparseColMat<usize, f64>>

Get the cached Hessian matrix

fn get_gradient(&self) -> Option<&Mat<f64>>

Get the cached gradient vector