release(v2.0.0-alpha.2): PyO3 bindings + executed companion notebooks + Sphinx RST with inline plots

PyO3 abi3 bindings for the 13 v2.0.0 functions across 8 module groups:
  bsde, pde, stochastic_control, optimal_control::quadratic_impact_control,
  mean_field::mckean_vlasov, agent_based, inference, optimization.

8 executed companion notebooks under examples/notebooks/10_bsde.ipynb …
17_generative_calibration.ipynb (cell outputs and matplotlib figures
preserved as proof-of-work; verified against analytic ground truths).

8 Sphinx RST pages under docs/source/algorithms/{bsde,pde,stochastic_control,
quadratic_impact_control,mckean_vlasov,agent_based,robust_drift,
generative_calibration_hooks}.rst with .. math:: derivations and inline
.. image:: directives placed immediately after each .. code-block:: python
so each plot appears directly under the code that produced it.

18 PNG plot assets under docs/source/_static/v2/<group>/.

index.rst extended with a new 'v2.0 Generic Stochastic Control & PDE'
toctree caption.

Forbidden-vocabulary audit on new src/, docs/source/algorithms/ and
binding files: zero matches.

All previously stable APIs untouched; v2.0.0 is additive at the binding
level — no v1.x function signature was changed.

docs/source/algorithms/mckean_vlasov.rst

@@ -0,0 +1,72 @@
+McKean–Vlasov — propagation of chaos
+====================================
+
+Interacting-particle Euler scheme for $dX_t = θ(\bar X_t - X_t) dt + σ dW_t$ (`mean_reverting_mckean_vlasov`).  The empirical mean is preserved; the empirical variance approaches the diffusion-only equilibrium.
+
+.. note:: Companion executed notebook: `14_mckean_vlasov.ipynb <../../examples/notebooks/14_mckean_vlasov.ipynb>`_
+
+14 — McKean–Vlasov mean-reverting dynamics
+==========================================
+
+.. code-block:: python
+
+   import numpy as np
+   import matplotlib.pyplot as plt
+   from optimizr import _core as opt
+   plt.rcParams['figure.figsize'] = (7, 4)
+   plt.rcParams['figure.dpi'] = 110
+
+.. code-block:: python
+
+   init = np.linspace(-2.0, 2.0, 200).tolist()
+   init_mean = float(np.mean(init))
+   res = opt.mean_reverting_mckean_vlasov(
+       initial=init, theta=1.0, sigma=0.1,
+       n_steps=1000, t_horizon=1.0, seed=42,
+   )
+   n_t = res['n_steps']; n_p = res['n_particles']
+   X   = np.array(res['paths_flat']).reshape(n_t, n_p)
+   tg  = np.array(res['time_grid'])
+   print('initial mean =', init_mean)
+   print('final  mean  =', float(X[-1].mean()))
+   print('final  std   =', float(X[-1].std()))
+
+.. code-block:: python
+
+   fig, ax = plt.subplots()
+   ax.plot(tg, X[:, ::20], color='tab:blue', alpha=0.2, lw=0.6)
+   ax.plot(tg, X.mean(axis=1), color='red', lw=2, label='empirical mean')
+   ax.axhline(init_mean, color='k', ls=':', label='initial mean')
+   ax.set_xlabel('t'); ax.set_ylabel('X^i_t'); ax.legend(); ax.grid(alpha=0.3)
+   ax.set_title('Mean-reverting McKean–Vlasov — 200 particles')
+   fig.tight_layout(); plt.show()
+
+.. image:: ../_static/v2/mckean_vlasov/plot_01.png
+   :align: center
+   :width: 80%
+
+.. code-block:: python
+
+   fig, ax = plt.subplots()
+   ax.hist(X[0],  bins=30, alpha=0.5, label='t = 0',  density=True)
+   ax.hist(X[-1], bins=30, alpha=0.5, label='t = T',  density=True)
+   ax.set_xlabel('x'); ax.set_ylabel('empirical density'); ax.legend(); ax.grid(alpha=0.3)
+   ax.set_title('Marginal density at t = 0 and t = T')
+   fig.tight_layout(); plt.show()
+
+.. image:: ../_static/v2/mckean_vlasov/plot_02.png
+   :align: center
+   :width: 80%
+
+**Verified:** empirical mean stays within `0.05` of the initial mean.
+
+API
+---
+
+.. code-block:: rust
+
+   pub fn simulate_mckean_vlasov<B>(initial: &[f64], drift: B, cfg: &McKeanVlasovConfig) -> Result<McKeanVlasovResult>
+   where B: Fn(f64, &[f64]) -> f64;
+
+   pub struct McKeanVlasovConfig { pub n_particles: usize, pub n_steps: usize, pub t_horizon: f64, pub sigma: f64, pub seed: u64 }
+   pub struct McKeanVlasovResult { pub paths: Array2<f64>, pub time_grid: Array1<f64> }