feat: unify optimize and optimize_with via blanket Objective impl

- Add blanket `impl Objective<V> for Fn(&mut Trial) -> Result<V, E>`
  so closures work directly with `optimize`
- Rewrite optimize, optimize_async, optimize_parallel to accept
  `impl Objective<V>` with before_trial/after_trial hooks
- Remove optimize_with, optimize_with_async, optimize_with_parallel
- Remove max_retries and retry logic from Objective trait
- Add explicit closure type annotations for HRTB inference
- Convert FnMut test closures to Fn via RefCell/Cell

examples/basic_optimization.rs

@@ -17,7 +17,7 @@ fn main() {
 
     // Run 50 trials, each evaluating f(x) = (x - 3)²
     study
-        .optimize(50, |trial| {
+        .optimize(50, |trial: &mut optimizer::Trial| {
             let x_val = x.suggest(trial)?;
             let value = (x_val - 3.0).powi(2);
             Ok::<_, Error>(value)

examples/early_stopping.rs

@@ -44,7 +44,7 @@ fn main() -> optimizer::Result<()> {
         target: 0.01,
     };
 
-    study.optimize_with(100, objective)?;
+    study.optimize(100, objective)?;
 
     let best = study.best_trial()?;
     println!(

examples/journal_storage.rs

@@ -24,7 +24,7 @@ fn main() -> optimizer::Result<()> {
             .storage(storage)
             .build();
 
-        study.optimize(20, |trial| {
+        study.optimize(20, |trial: &mut optimizer::Trial| {
             let xv = x.suggest(trial)?;
             Ok::<_, optimizer::Error>(xv * xv)
         })?;
@@ -46,7 +46,7 @@ fn main() -> optimizer::Result<()> {
             .build();
 
         let before = study.n_trials();
-        study.optimize(10, |trial| {
+        study.optimize(10, |trial: &mut optimizer::Trial| {
             let xv = x.suggest(trial)?;
             Ok::<_, optimizer::Error>(xv * xv)
         })?;

examples/multi_objective.rs

@@ -15,7 +15,7 @@ fn main() -> optimizer::Result<()> {
 
     // Classic bi-objective: f1(x) = x², f2(x) = (x-1)²
     // The Pareto front is the curve where improving f1 worsens f2.
-    study.optimize(50, |trial| {
+    study.optimize(50, |trial: &mut optimizer::Trial| {
         let xv = x.suggest(trial)?;
         let f1 = xv * xv;
         let f2 = (xv - 1.0) * (xv - 1.0);

examples/parameter_types.rs

@@ -41,7 +41,7 @@ fn main() {
 
     // --- Run the optimization ---
     study
-        .optimize(30, |trial| {
+        .optimize(30, |trial: &mut optimizer::Trial| {
             let lr_val = lr.suggest(trial)?;
             let layers = n_layers.suggest(trial)?;
             let opt = optimizer.suggest(trial)?;

examples/pruning.rs

@@ -26,7 +26,7 @@ fn main() -> optimizer::Result<()> {
 
     let n_epochs: u64 = 20;
 
-    study.optimize(30, |trial| {
+    study.optimize(30, |trial: &mut optimizer::Trial| {
         let lr_val = lr.suggest(trial)?;
         let mom = momentum.suggest(trial)?;
 

examples/sampler_comparison.rs

@@ -20,7 +20,7 @@ fn run_study(study: Study<f64>, n_trials: usize) -> f64 {
     let y = FloatParam::new(-3.0, 3.0).name("y");
 
     study
-        .optimize(n_trials, |trial| {
+        .optimize(n_trials, |trial: &mut optimizer::Trial| {
             let x_val = x.suggest(trial)?;
             let y_val = y.suggest(trial)?;
             Ok::<_, Error>(sphere(x_val, y_val))