Struct BoundedCmaInject 

pub struct BoundedCmaInject<I, V, M>
where
    I: MemeticInner<V>,
{ /* private fields */ }

Memetic BoundedCmaEs with Hansen (2011) injection. Sibling of CmaInject; the outer is the bounded variant of CMA-ES, and bound-respecting inners (notably LBFGSB) see the same bounds the outer enforces via BoundPenalty.

The injection mechanism (Hansen 2011 eq. 4 — Mahalanobis clip, plug back into the standard CMA update) is unchanged from CmaInject; only the outer solver is replaced. Adaptive boundary-penalty bookkeeping inside BoundedCmaEs is orthogonal to injection — BoundedCmaInject reads the same post-update m, σ, B, D^{-1} for clipping, and re-evaluates injected candidates with the same γ-weighted penalty regular samples receive (otherwise an out-of-box LM/L-BFGS-B refinement would dominate the population at a spuriously low raw cost — bug noticed when writing the LM example).

Inner solver

Generic over any I: MemeticInner<V>. The associated I::State determines the inner state shape. Shipped impls cover NelderMead, LevenbergMarquardt, and LBFGSB. L-BFGS-B is the natural inner here — its P: BoxConstraints bound matches the outer’s, and the same box flows through both ends of the composition.

Note an asymmetry: NM and LM are unconstrained solvers, so even when the outer enforces bounds, their inner polish may step outside the box. The outer’s BoundPenalty re-pulls injected candidates back into feasibility through the penalized re-evaluation below.

Backends

Same coverage as BoundedCmaEs: nalgebra (DVector / DMatrix) and faer (Col / Mat). Vec<f64> and ndarray produce a compile-time error per tenet 5.

Examples

See BoundedCmaEs for the bounded population-based Executor pattern; BoundedCmaInject adds a bound-respecting local-search inner.

Implementations

impl<I, V, M> BoundedCmaInject<I, V, M>

where I: MemeticInner<V>,

pub fn with_inner_solver(cma: BoundedCmaEs<V, M>, inner: I) -> Self

Wrap a configured BoundedCmaEs with inner as the local refinement step. Defaults: k = 1, inner max_iter = 50, c_y = Hansen-2011 Table 1 default.

pub fn with_k(self, k: usize) -> Self

Number of best-ranked candidates to refine and inject each generation. Default 1.

Panics

Panics if k == 0.

pub fn with_c_y(self, c_y: f64) -> Self

Override the Hansen-2011 clipping threshold c_y (default √n + 2n/(n+2)).

Panics

Panics if c_y <= 0.

pub fn with_inner_max_iter(self, n: u64) -> Self

Inner solver iteration budget per outer generation (default 50).

pub fn inner_terminate_on<C>(self, criterion: C) -> Self

where C: TerminationCriterion<I::State> + 'static,

Register a stateless termination criterion on the inner loop. See CmaInject::inner_terminate_on for the statelessness contract.

Trait Implementations

impl<P, I, V, M> Solver<P, BasicPopulationState<V>> for BoundedCmaInject<I, V, M>

where P: CostFunction<Param = V, Output = f64> + BoxConstraints, I: MemeticInner<V> + Solver<P, <I as WarmStart<V>>::State>, I::State: State<Param = V, Float = f64>, V: VectorLen + Clone + ScaledAdd<f64> + ScaleInPlace + ComponentMulAssign + ClampInPlace + NormSquared + SampleStandardNormal + Index<usize, Output = f64> + IndexMut<usize, Output = f64>, M: MatrixIdentity + MatrixFromDiagonal<V> + MatVec<V> + MatTransposeVec<V> + MatDiagonal<V> + ScaleInPlace + RankOneUpdate<V> + SymmetricEigen<V> + Clone,

fn init( &mut self, problem: &P, state: BasicPopulationState<V>, ) -> BasicPopulationState<V>

One-time setup before the iteration loop.

fn next_iter( &mut self, problem: &P, state: BasicPopulationState<V>, ) -> (BasicPopulationState<V>, Option<TerminationReason>)

Advance one iteration.

fn terminate( &self, state: &BasicPopulationState<V>, ) -> Option<TerminationReason>

Optional pre-iteration solver-specific termination test.