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.

examples/compare.rs

@@ -1003,6 +1003,7 @@ fn rastrigin_cma_es(seed: u64) -> SoRun {
         generations: gens,
         initial_sigma: 1.0,
         eigen_decomposition_period: 1,
+        initial_mean: None,
         seed,
     };
     let mut opt = CmaEs::new(config, bounds);
@@ -1058,6 +1059,7 @@ macro_rules! so_run_cma {
             generations: $budget / pop,
             initial_sigma: 1.0,
             eigen_decomposition_period: 1,
+        initial_mean: None,
             seed: $seed,
         };
         let mut opt = CmaEs::new(config, bounds);