feat(algorithms): add BayesianOpt — GP-based Bayesian Optimization

The first sample-efficient algorithm in heuropt. Bayesian optimization
maintains a Gaussian-process surrogate of the objective and at each
step picks the next decision by maximizing an acquisition function on
that surrogate, so the evaluation budget is used surgically.

Implementation:
- **Kernel**: anisotropic RBF (squared-exponential) with per-axis
  length scales, signal variance, and a small noise/jitter floor.
  Hyperparameters are exposed in the config; a future version can add
  marginal-likelihood maximization.
- **Posterior**: standard formulation. Cholesky factorizes K (using
  the new internal helper); mean and variance predictions follow.
- **Acquisition**: Expected Improvement against the best observed
  feasible point. Optimized by best-of-N random sampling — simple,
  predictable cost, no inner-optimizer footgun.
- **Initial design**: `initial_samples` uniform-random points in
  bounds before the BO loop starts.
- **Constraints**: feasibility-aware EI — best observed value uses
  only feasible points; infeasible candidates are penalized.

Vec<f64> decisions, single-objective only. Targets the regime no
existing heuropt algorithm covers: 50–500 evaluations on an
expensive black-box function (CFD sim, ML training run, real-world
measurement).

Tests cover convergence on the 1-D sphere within a tight evaluation
budget (~30 evals get to f < 1e-6 — vs population-based methods
needing thousands), deterministic reruns, and panic on
multi-objective + dim mismatches.

src/algorithms/mod.rs

@@ -2,6 +2,7 @@
 
 pub mod age_moea;
 pub mod ant_colony_tsp;
+pub mod bayesian_opt;
 pub mod cma_es;
 pub mod differential_evolution;
 pub mod epsilon_moea;
@@ -33,6 +34,7 @@ pub mod umda;
 
 pub use age_moea::*;
 pub use ant_colony_tsp::*;
+pub use bayesian_opt::*;
 pub use cma_es::*;
 pub use differential_evolution::*;
 pub use epsilon_moea::*;