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AssemblyBackend

apex_solver::linearizer

Trait AssemblyBackend 

Source 
pub trait AssemblyBackend: LinearizationMode {
    // Required methods
    fn assemble(
        problem: &Problem,
        variables: &HashMap<String, VariableEnum>,
        variable_index_map: &HashMap<String, usize>,
        symbolic_structure: Option<&SymbolicStructure>,
        total_dof: usize,
    ) -> LinearizerResult<(Mat<f64>, Self::Jacobian)>;
    fn compute_column_norms(jacobian: &Self::Jacobian) -> Vec<f64>;
    fn apply_column_scaling(
        jacobian: &Self::Jacobian,
        scaling: &[f64],
    ) -> Self::Jacobian;
    fn apply_inverse_scaling(step: &Mat<f64>, scaling: &[f64]) -> Mat<f64>;
    fn hessian_vec_product(hessian: &Self::Hessian, vec: &Mat<f64>) -> Mat<f64>;
}
Expand description

Type-level backend for assembling (residuals, Jacobian) and performing matrix operations. Implemented by SparseMode and DenseMode.

All methods are static — this trait is used as a compile-time strategy selector, not as an object interface. Extends LinearizationMode with the five operations an optimizer needs each iteration: building (r, J), scaling J, unscaling dx, and H·v.

All three optimizers (LM, GN, DogLeg) are generic over M: AssemblyBackend, giving zero-cost static dispatch through the entire pipeline.

Required Methods§

Source

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

Assemble residuals and Jacobian from the problem.

Source

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

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

Source

fn apply_column_scaling( jacobian: &Self::Jacobian, scaling: &[f64], ) -> Self::Jacobian

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

Source

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

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

Source

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

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

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors§