feat(traits,algorithms): add PartialProblem trait and Hyperband

Multi-fidelity optimization. Hyperband (Li et al. 2017) and its
foundation Successive Halving (Karnin et al. 2013) tune
hyperparameters by allocating *uneven* compute across configurations:
sample many cheap-to-evaluate-at-low-budget configs, then promote
the survivors to higher budgets. Crucial for ML hyperparameter
tuning where each evaluation is a partial training run.

This requires a new trait — `Problem::evaluate` is a single-shot
black box, but Hyperband needs to evaluate the SAME decision at
different fidelity budgets:

  pub trait PartialProblem {
      type Decision: Clone;
      fn objectives(&self) -> ObjectiveSpace;
      fn evaluate_at_budget(&self, decision: &Self::Decision,
                            budget: f64) -> Evaluation;
  }

`PartialProblem` is intentionally NOT a sub-trait of `Problem`.
Implementors who already have a `Problem` and want their
`evaluate_at_budget` to ignore budget can write a one-line wrapper.

`Hyperband` is the optimizer:

  pub struct HyperbandConfig {
      max_budget: f64, eta: f64, max_brackets: usize, seed: u64,
  }
  pub struct Hyperband<I> { config, initializer, ... }

Single-objective only. The decision sampler is an `Initializer<D>` so
it works the same way as every other heuropt algorithm. Generic over
decision type.

src/core/mod.rs

@@ -3,6 +3,7 @@
 pub mod candidate;
 pub mod evaluation;
 pub mod objective;
+pub mod partial_problem;
 pub mod population;
 pub mod problem;
 pub mod result;
@@ -11,6 +12,7 @@ pub mod rng;
 pub use candidate::*;
 pub use evaluation::*;
 pub use objective::*;
+pub use partial_problem::*;
 pub use population::*;
 pub use problem::*;
 pub use result::*;