feat: v0.5.0 — comprehensive documentation release

Theme: documentation and project polish. No public-API changes; this
is the v0.5 release that elevates heuropt's docs/onboarding/governance
to bar-setting status.

Adds:
- mdbook user guide at docs/book/ with intro, getting-started,
  defining-problems, choosing-an-algorithm, cookbook (7 recipes),
  comparison vs other libraries, stability/SemVer, migration guides.
  Deploys to https://swaits.github.io/heuropt/ via .github/workflows/
  docs.yml.
- Runnable rustdoc examples on every algorithm (35 of them), all
  exercised by cargo test --doc.
- Three real-world examples: portfolio.rs (multi-obj with budget
  constraint), hyperparam_tuning.rs (BO + TPE), scheduling.rs
  (permutation via SA + SwapMutation against Smith's-rule oracle).
- Governance: CONTRIBUTING.md, SECURITY.md, CODE_OF_CONDUCT.md
  (adopting builderscode.org's Builder's Code of Conduct), GitHub
  issue templates, PR template.

Polishes:
- README hero with badges + user-guide link.
- lib.rs crate-level docs.
- CHANGELOG entry for 0.5.0.

Bumps Cargo.toml to 0.5.0.

src/algorithms/bayesian_opt.rs

@@ -61,6 +61,40 @@ impl Default for BayesianOptConfig {
 /// evaluation budgets (50–500). The GP kernel is anisotropic RBF; the
 /// acquisition function is EI; both are optimized by best-of-N random
 /// sampling each step (simple, predictable cost).
+///
+/// # Example
+///
+/// ```
+/// use heuropt::prelude::*;
+///
+/// struct Sphere;
+/// impl Problem for Sphere {
+///     type Decision = Vec<f64>;
+///     fn objectives(&self) -> ObjectiveSpace {
+///         ObjectiveSpace::new(vec![Objective::minimize("f")])
+///     }
+///     fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
+///         Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
+///     }
+/// }
+///
+/// let mut opt = BayesianOpt::new(
+///     BayesianOptConfig {
+///         initial_samples: 10,
+///         iterations: 30,
+///         length_scales: None, // default per-axis length scales
+///         signal_variance: 1.0,
+///         noise_variance: 1e-6,
+///         acquisition_samples: 200,
+///         seed: 42,
+///     },
+///     RealBounds::new(vec![(-3.0, 3.0); 3]),
+/// );
+/// let r = opt.run(&Sphere);
+/// // 10 random + 30 BO steps = 40 total evaluations.
+/// assert_eq!(r.evaluations, 40);
+/// assert!(r.best.is_some());
+/// ```
 #[derive(Debug, Clone)]
 pub struct BayesianOpt {
     /// Algorithm configuration.