refactor(examples): split multi-concept examples into focused single-topic files

- Split pruning_and_callbacks into pruning and early_stopping
- Split advanced_features into async_parallel, journal_storage, ask_and_tell, multi_objective
- Each example now requires only its own feature flag
- Trim sampler_comparison winner logic and verbose header
- Update CI workflow and README to match new example names

examples/pruning.rs

@@ -0,0 +1,67 @@
+//! Trial pruning — stop unpromising trials early with `MedianPruner`.
+//!
+//! When your objective involves an iterative loop (e.g. training epochs),
+//! the pruner compares intermediate values across trials and kills the
+//! ones that fall below the median — saving compute on bad configurations.
+//!
+//! Run with: `cargo run --example pruning`
+
+use optimizer::prelude::*;
+
+fn main() -> optimizer::Result<()> {
+    // MedianPruner prunes trials whose intermediate value falls below the
+    // median of previously completed trials at the same step.
+    let study: Study<f64> = Study::builder()
+        .minimize()
+        .sampler(RandomSampler::with_seed(42))
+        .pruner(
+            MedianPruner::new(Direction::Minimize)
+                .n_warmup_steps(3) // run at least 3 epochs before pruning
+                .n_min_trials(3), // need 3 completed trials before pruning kicks in
+        )
+        .build();
+
+    let lr = FloatParam::new(1e-4, 1.0).name("learning_rate");
+    let momentum = FloatParam::new(0.0, 0.99).name("momentum");
+
+    let n_epochs: u64 = 20;
+
+    study.optimize(30, |trial| {
+        let lr_val = lr.suggest(trial)?;
+        let mom = momentum.suggest(trial)?;
+
+        // Simulated training loop — good hyperparameters converge to low loss,
+        // bad ones plateau high, giving the pruner something to cut.
+        let mut loss = 1.0;
+        for epoch in 0..n_epochs {
+            let lr_penalty = (lr_val.log10() - 0.01_f64.log10()).powi(2);
+            let mom_penalty = (mom - 0.8).powi(2);
+            let base_loss = 0.02 + 0.05 * lr_penalty + 1.5 * mom_penalty;
+            let progress = (epoch as f64 + 1.0) / n_epochs as f64;
+            loss = base_loss + (1.0 - base_loss) * (-3.5 * progress).exp();
+
+            // Report intermediate value so the pruner can evaluate this trial.
+            trial.report(epoch, loss);
+
+            // Check whether the pruner recommends stopping early.
+            if trial.should_prune() {
+                Err(TrialPruned)?;
+            }
+        }
+
+        Ok::<_, Error>(loss)
+    })?;
+
+    // --- Results ---
+    let best = study.best_trial()?;
+    println!(
+        "Completed {} trials ({} pruned)",
+        study.n_trials(),
+        study.n_pruned_trials()
+    );
+    println!("Best trial #{}: loss = {:.6}", best.id, best.value);
+    println!("  learning_rate = {:.6}", best.get(&lr).unwrap());
+    println!("  momentum      = {:.4}", best.get(&momentum).unwrap());
+
+    Ok(())
+}