Struct VarianceReductionAcquisition 

pub struct VarianceReductionAcquisition { /* private fields */ }

Expected reduction in posterior integral variance from evaluating one candidate node.

Implementations

impl VarianceReductionAcquisition

pub const fn new( kernel: RbfKernel, measure: GaussianMeasure, jitter: f64, ) -> Self

Construct a variance-reduction acquisition function.

pub const fn kernel(&self) -> RbfKernel

Return the configured kernel.

pub const fn measure(&self) -> GaussianMeasure

Return the configured integration measure.

pub const fn jitter(&self) -> f64

Return the explicit diagonal jitter.

pub fn reduction( &self, nodes: &[f64], candidate: f64, ) -> Result<f64, BayesianQuadratureError>

Evaluate the posterior integral-variance reduction at candidate.

Errors

Returns BayesianQuadratureError for invalid current nodes, a non-finite candidate, or a Gaussian-conditioning failure on the current design.

pub fn select_best( &self, nodes: &[f64], candidates: &[f64], ) -> Result<SelectedCandidate, ActiveSelectionError>

Select the candidate with the largest predicted variance reduction.

Ties are resolved deterministically by keeping the first maximum in the supplied candidate slice.

Errors

Returns ActiveSelectionError for empty/non-finite candidate sets or when acquisition evaluation fails for the current design.

Trait Implementations

impl Clone for VarianceReductionAcquisition

fn clone(&self) -> VarianceReductionAcquisition

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Copy for VarianceReductionAcquisition

impl Debug for VarianceReductionAcquisition

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

Formats the value using the given formatter.

impl PartialEq for VarianceReductionAcquisition

fn eq(&self, other: &VarianceReductionAcquisition) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl StructuralPartialEq for VarianceReductionAcquisition