Struct DenseMode 

pub struct DenseMode;

Dense linear algebra mode.

Uses Mat<f64> for Jacobians and Hessians. Optimal for small-to-medium problems (< 500 DOF) or dense Jacobians.

Trait Implementations

impl AssemblyBackend for DenseMode

fn assemble( problem: &Problem, variables: &HashMap<String, VariableEnum>, variable_index_map: &HashMap<String, usize>, _symbolic_structure: Option<&SymbolicStructure>, total_dof: usize, ) -> LinearizerResult<(Mat<f64>, Mat<f64>)>

Assemble residuals and Jacobian from the problem.

fn compute_column_norms(jacobian: &Mat<f64>) -> Vec<f64>

Compute column norms of the Jacobian (for Jacobi scaling).

fn apply_column_scaling(jacobian: &Mat<f64>, scaling: &[f64]) -> Mat<f64>

Apply diagonal column scaling to the Jacobian. Returns a new Jacobian with columns scaled by 1 / (1 + norm).

fn apply_inverse_scaling(step: &Mat<f64>, scaling: &[f64]) -> Mat<f64>

Apply inverse scaling to a step vector: step_i *= scaling_i

fn hessian_vec_product(hessian: &Mat<f64>, vec: &Mat<f64>) -> Mat<f64>

Hessian-vector product: H * v (needed by DogLeg for Cauchy point)

impl LinearSolver<DenseMode> for DenseCholeskySolver

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

Solve the normal equations: (J^T · J) · dx = −J^T · r

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

Solve the augmented equations: (J^T · J + λI) · dx = −J^T · r

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

Get the cached Hessian matrix (J^T · J) from the last solve

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

Get the cached gradient vector (J^T · r) from the last solve

fn compute_covariance_matrix(&mut self) -> Option<&Mat<f64>>

Compute the covariance matrix (H^{-1}) by inverting the cached Hessian.

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

Get the cached covariance matrix (H^{-1}) computed from the Hessian.

impl LinearSolver<DenseMode> for DenseQRSolver

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

Solve the normal equations: (J^T · J) · dx = −J^T · r

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

Solve the augmented equations: (J^T · J + λI) · dx = −J^T · r

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

Get the cached Hessian matrix (J^T · J) from the last solve

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

Get the cached gradient vector (J^T · r) from the last solve

fn compute_covariance_matrix(&mut self) -> Option<&Mat<f64>>

Compute the covariance matrix (H^{-1}) by inverting the cached Hessian.

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

Get the cached covariance matrix (H^{-1}) computed from the Hessian.

impl LinearizationMode for DenseMode

type Jacobian = Mat<Own<f64>>

The Jacobian matrix type (SparseColMat or Mat)

type Hessian = Mat<Own<f64>>

The Hessian matrix type (SparseColMat or Mat)