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/bsde.rst

@@ -0,0 +1,99 @@
+BSDE — θ-scheme and deep-BSDE bridge
+====================================
+
+This notebook exercises `optimizr.linear_bsde_constant_coeffs`, the Crank–Nicolson θ-scheme for the BSDE
+`-dY = (a Y + b Z + c) dt - Z dW` with constant coefficients, and verifies the discrete trajectory against the analytic solution `Y_t = exp(-ρ (T - t))`.
+
+.. note:: Companion executed notebook: `10_bsde.ipynb <../../examples/notebooks/10_bsde.ipynb>`_
+
+10 — BSDE θ-scheme
+==================
+
+Generic CPU-only Crank–Nicolson scheme for linear backward stochastic differential equations.  Reference doc page: [bsde.rst](../../docs/source/algorithms/bsde.rst).
+
+.. 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
+
+Exponential ground-truth check
+------------------------------
+
+With $a(t) \equiv -\rho$, $b = c = 0$ and $Y_T = 1$ the analytic deterministic solution is $Y_t = e^{-\rho (T-t)}$.
+
+.. code-block:: python
+
+   rho = 0.3
+   T   = 1.0
+   res = opt.linear_bsde_constant_coeffs(
+       a_const=-rho, b_const=0.0, c_const=0.0,
+       terminal=1.0, n_steps=200, t_horizon=T, theta=0.5,
+   )
+   tg = np.array(res['time_grid'])
+   yg = np.array(res['y'])
+   analytic = np.exp(-rho * (T - tg))
+   print('Y0 =', yg[0], '   exp(-rho T) =', analytic[0])
+   print('max abs error =', float(np.max(np.abs(yg - analytic))))
+
+.. code-block:: python
+
+   fig, ax = plt.subplots()
+   ax.plot(tg, yg, label='θ-scheme', lw=2)
+   ax.plot(tg, analytic, '--', label='analytic exp(-ρ(T-t))')
+   ax.set_xlabel('t'); ax.set_ylabel('Y_t')
+   ax.set_title('Linear BSDE — Crank–Nicolson vs analytic')
+   ax.legend(); ax.grid(alpha=0.3)
+   fig.tight_layout(); plt.show()
+
+.. image:: ../_static/v2/bsde/plot_01.png
+   :align: center
+   :width: 80%
+
+Convergence rate study
+----------------------
+
+Crank–Nicolson is second-order in `Δt`.
+
+.. code-block:: python
+
+   errs = []
+   ns = [25, 50, 100, 200, 400, 800]
+   for n in ns:
+       r = opt.linear_bsde_constant_coeffs(-rho, 0.0, 0.0, 1.0, n, T, 0.5)
+       errs.append(abs(r['y'][0] - np.exp(-rho * T)))
+   print(list(zip(ns, errs)))
+
+.. code-block:: python
+
+   fig, ax = plt.subplots()
+   ax.loglog(ns, errs, 'o-')
+   ax.loglog(ns, [errs[0] * (ns[0] / n) ** 2 for n in ns],
+             ':', label='O(Δt²) reference')
+   ax.set_xlabel('n_steps'); ax.set_ylabel('|Y0 − analytic|')
+   ax.set_title('Crank–Nicolson convergence'); ax.grid(which='both', alpha=0.3); ax.legend()
+   fig.tight_layout(); plt.show()
+
+.. image:: ../_static/v2/bsde/plot_02.png
+   :align: center
+   :width: 80%
+
+**Verified against analytic ground truth:** `Y_t = exp(-ρ (T - t))` — relative error at `t = 0` below `1e-3` for `n_steps = 200`.
+
+API
+---
+
+.. code-block:: rust
+
+   pub fn solve_linear_bsde<A, B, C>(
+       a: A, b: B, c: C, terminal: f64, cfg: &ThetaSchemeConfig
+   ) -> Result<ThetaSchemeResult>
+   where A: Fn(f64) -> f64, B: Fn(f64) -> f64, C: Fn(f64) -> f64;
+
+   pub struct ThetaSchemeConfig { pub n_steps: usize, pub t_horizon: f64, pub theta: f64 }
+   pub struct ThetaSchemeResult { pub y: Array1<f64>, pub z: Array1<f64>, pub time_grid: Array1<f64> }
+
+   pub trait ConditionalExpectation { /* deep-BSDE bridge */ }
+   pub struct DeepBsdeBridge { /* ... */ }