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/quadratic_impact_control.rst

@@ -0,0 +1,76 @@
+Quadratic-impact control — closed-form Riccati
+==============================================
+
+Closed-form Riccati feedback for a controlled 1-D SDE with quadratic running cost (`quadratic_impact_control_py`).
+
+.. note:: Companion executed notebook: `13_quadratic_impact.ipynb <../../examples/notebooks/13_quadratic_impact.ipynb>`_
+
+13 — Quadratic-impact controlled SDE
+====================================
+
+.. 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
+
+Riccati fixed-point check
+-------------------------
+
+$h'(t) = h(t)^2/γ - φ$ with $h(T) = A$.  When $γ = φ = A = 1$ the right-hand side is $h^2 - 1 = 0$ at $h = 1$, so `h ≡ 1`.
+
+.. code-block:: python
+
+   res = opt.quadratic_impact_control_py(
+       gamma=1.0, phi=1.0, a_terminal=1.0,
+       t_horizon=0.5, n_steps=500,
+   )
+   tg = np.array(res['time_grid'])
+   h  = np.array(res['h']); k = np.array(res['feedback_gain'])
+   print('h drift from 1:', float(np.max(np.abs(h - 1.0))))
+
+.. code-block:: python
+
+   fig, ax = plt.subplots()
+   ax.plot(tg, h, label='h(t)')
+   ax.plot(tg, k, '--', label='k(t) = h(t)/γ')
+   ax.axhline(1.0, color='k', alpha=0.3, ls=':', label='fixed point')
+   ax.set_xlabel('t'); ax.legend(); ax.grid(alpha=0.3)
+   ax.set_title('Riccati fixed point  γ=φ=A=1')
+   fig.tight_layout(); plt.show()
+
+.. image:: ../_static/v2/quadratic_impact_control/plot_01.png
+   :align: center
+   :width: 80%
+
+Sensitivity to the terminal weight
+----------------------------------
+
+Vary $A$, fix $γ = 1$, $φ = 0.25$, $T = 1$.
+
+.. code-block:: python
+
+   fig, ax = plt.subplots()
+   for A in [0.0, 0.25, 0.5, 1.0, 2.0, 5.0]:
+       r = opt.quadratic_impact_control_py(1.0, 0.25, A, 1.0, 1000)
+       ax.plot(r['time_grid'], r['h'], label=f'A = {A:g}')
+   ax.set_xlabel('t'); ax.set_ylabel('h(t)'); ax.legend(); ax.grid(alpha=0.3)
+   ax.set_title('Riccati sensitivity to terminal weight')
+   fig.tight_layout(); plt.show()
+
+.. image:: ../_static/v2/quadratic_impact_control/plot_02.png
+   :align: center
+   :width: 80%
+
+**Verified:** `h ≡ 1` with `max|h - 1| < 1e-9` at the fixed point.
+
+API
+---
+
+.. code-block:: rust
+
+   pub fn solve_quadratic_impact_control(cfg: &QuadraticImpactConfig) -> Result<QuadraticImpactResult>;
+   pub struct QuadraticImpactConfig { pub gamma: f64, pub phi: f64, pub a_terminal: f64, pub t_horizon: f64, pub n_steps: usize }
+   pub struct QuadraticImpactResult { pub time_grid: Array1<f64>, pub h: Array1<f64>, pub feedback_gain: Array1<f64> }