feat(algorithms): add IpopCmaEs (CMA-ES with restart) for multimodal problems

Auger & Hansen 2005 IPOP-CMA-ES: wraps the existing CmaEs in a restart
loop that doubles the population size and re-randomizes the mean
whenever a restart trigger fires. Specifically addresses the failure
mode we observed on Rastrigin (vanilla CMA-ES = 2.3 vs DE = 0).

Restart triggers:
- The whole budget for one inner CmaEs run finishes without improvement
- (More sophisticated triggers — eigenvalue collapse, condition-number
  blow-up, sigma stagnation — are left for future versions; the
  per-run budget trigger captures the bulk of the practical benefit)

Each restart:
- Doubles the population_size (Auger & Hansen 2005)
- Re-randomizes the initial mean to a fresh point in the bounds box
- Resets sigma to the user's initial value

Same Vec<f64> + single-objective constraints as CmaEs. The total
budget is divided across restarts; restart budget grows with
population. Tests verify it beats vanilla CMA-ES on Rastrigin.

src/algorithms/nelder_mead.rs

@@ -177,15 +177,16 @@ where
                         if idx == best_idx {
                             continue;
                         }
+                        #[allow(clippy::needless_range_loop)] // body indexes both vertices and best_pt.
                         for j in 0..n {
                             vertices[idx][j] = best_pt[j]
                                 + self.config.shrinkage
                                     * (vertices[idx][j] - best_pt[j]);
                         }
                         // Clamp to bounds.
-                        for j in 0..n {
+                        for (j, x) in vertices[idx].iter_mut().enumerate() {
                             let (lo, hi) = self.bounds.bounds[j];
-                            vertices[idx][j] = vertices[idx][j].clamp(lo, hi);
+                            *x = x.clamp(lo, hi);
                         }
                         evals[idx] = problem.evaluate(&vertices[idx]);
                         evaluations += 1;