StructuredSparseLinearSolver

apex_solver::linalg

Trait StructuredSparseLinearSolver 

Source 
pub trait StructuredSparseLinearSolver {
    // Required methods
    fn initialize_structure(
        &mut self,
        variables: &HashMap<String, VariableEnum>,
        variable_index_map: &HashMap<String, usize>,
    ) -> LinAlgResult<()>;
    fn solve_normal_equation(
        &mut self,
        residuals: &Mat<f64>,
        jacobians: &SparseColMat<usize, f64>,
    ) -> LinAlgResult<Mat<f64>>;
    fn solve_augmented_equation(
        &mut self,
        residuals: &Mat<f64>,
        jacobians: &SparseColMat<usize, f64>,
        lambda: f64,
    ) -> LinAlgResult<Mat<f64>>;
    fn get_hessian(&self) -> Option<&SparseColMat<usize, f64>>;
    fn get_gradient(&self) -> Option<&Mat<f64>>;
}
Expand description

Trait for structured sparse linear solvers that require variable information.

This trait extends the basic sparse solver interface to support solvers that exploit problem structure (e.g., Schur complement for bundle adjustment). These solvers need access to variable information to partition the problem.

Required Methods§

Source

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.

This must be called before solving to set up the internal structure.

§Arguments
  • variables - Map of variable names to their typed instances
  • variable_index_map - Map from variable names to starting column indices
Source

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

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

Source

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

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

Source

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

Get the cached Hessian matrix

Source

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

Get the cached gradient vector

Implementors§