Trait SparseLinearSolver

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pub trait SparseLinearSolver {
    // Required methods
    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>>;
    fn compute_covariance_matrix(&mut self) -> Option<&Mat<f64>>;
    fn get_covariance_matrix(&self) -> Option<&Mat<f64>>;
}

Expand description

Trait for sparse linear solvers that can solve both normal and augmented equations

Required Methods§

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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

§Errors

Returns LinAlgError if:

Matrix factorization fails
Matrix is singular or ill-conditioned
Numerical instability is detected

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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

§Errors

Returns LinAlgError if:

Matrix factorization fails
Matrix is singular or ill-conditioned
Numerical instability is detected

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

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

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

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

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

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

Returns a reference to the covariance matrix if successful, None otherwise. The covariance matrix represents parameter uncertainty in the tangent space.

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