ThotDjehuty
d8682f61e5
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.
126 lines
4.5 KiB
ReStructuredText
126 lines
4.5 KiB
ReStructuredText
PDE — Fokker–Planck, HJB, elliptic Poisson
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==========================================
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Three CPU-only finite-difference solvers: 1-D forward Fokker–Planck (`fokker_planck_constant`), 2-D explicit HJB (`hjb_quadratic_2d`) and 2-D Poisson SOR (`poisson_2d_zero_boundary`). Each routine is verified against an analytic ground truth.
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.. note:: Companion executed notebook: `11_pde.ipynb <../../examples/notebooks/11_pde.ipynb>`_
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11 — PDE solvers
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================
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Fokker–Planck, HJB, Poisson.
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.. code-block:: python
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import numpy as np
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import matplotlib.pyplot as plt
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from optimizr import _core as opt
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plt.rcParams['figure.figsize'] = (7, 4)
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plt.rcParams['figure.dpi'] = 110
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Pure-diffusion Fokker–Planck
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----------------------------
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$\partial_t m = \tfrac12 \partial_{xx} m$ with Gaussian initial density should remain centred and approximately Gaussian.
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.. code-block:: python
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res = opt.fokker_planck_constant(
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mu=0.0, sigma_sq=1.0, init_sigma=1.0,
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x_min=-8.0, x_max=8.0, n_x=401,
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t_horizon=0.5, n_t=8000,
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)
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x = np.array(res['x_grid'])
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t = np.array(res['time_grid'])
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nx = res['n_x']; nt = res['n_t']
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M = np.array(res['density']).reshape(nt + 1, nx)
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print('total mass at t=0:', np.trapezoid(M[0], x))
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print('total mass at t=T:', np.trapezoid(M[-1], x))
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print('mean at t=T:', np.trapezoid(x * M[-1], x))
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.. code-block:: python
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fig, ax = plt.subplots()
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for k in [0, nt // 4, nt // 2, 3 * nt // 4, nt]:
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ax.plot(x, M[k], label=f't = {t[k]:.2f}')
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ax.set_xlim(-5, 5); ax.set_xlabel('x'); ax.set_ylabel('m(x, t)')
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ax.set_title('Pure-diffusion Fokker–Planck'); ax.grid(alpha=0.3); ax.legend()
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fig.tight_layout(); plt.show()
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.. image:: ../_static/v2/pde/plot_01.png
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:align: center
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:width: 80%
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2-D Poisson eigenfunction
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-------------------------
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$-\Delta u = 2\pi^2 \sin(\pi x)\sin(\pi y)$ on the unit square with zero Dirichlet boundary admits the exact solution $u(x,y) = \sin(\pi x)\sin(\pi y)$.
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.. code-block:: python
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n = 65
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xs = np.linspace(0, 1, n); ys = np.linspace(0, 1, n)
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X, Y = np.meshgrid(xs, ys, indexing='ij')
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F = 2 * np.pi ** 2 * np.sin(np.pi * X) * np.sin(np.pi * Y)
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res = opt.poisson_2d_zero_boundary(F.flatten().tolist(), n, n)
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U = np.array(res['u']).reshape(n, n)
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U_exact = np.sin(np.pi * X) * np.sin(np.pi * Y)
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print('iterations =', res['iterations'])
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print('residual =', res['residual'])
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print('max error =', float(np.max(np.abs(U - U_exact))))
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.. code-block:: python
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fig, axes = plt.subplots(1, 2, figsize=(11, 4))
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im0 = axes[0].imshow(U.T, origin='lower', extent=(0, 1, 0, 1), cmap='viridis')
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axes[0].set_title('SOR solution'); plt.colorbar(im0, ax=axes[0])
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im1 = axes[1].imshow((U - U_exact).T, origin='lower', extent=(0, 1, 0, 1), cmap='RdBu_r')
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axes[1].set_title('error vs analytic'); plt.colorbar(im1, ax=axes[1])
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fig.tight_layout(); plt.show()
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.. image:: ../_static/v2/pde/plot_02.png
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:align: center
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:width: 80%
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2-D HJB with quadratic terminal
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-------------------------------
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Heat-only relaxation ($H = 0$, σ² > 0) preserves a constant value, while a quadratic terminal $g(x) = ½(x²+y²)$ smooths.
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.. code-block:: python
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res = opt.hjb_quadratic_2d(n_per_dim=21, x_min=-1.0, x_max=1.0,
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n_t=200, t_horizon=0.2, sigma_sq=0.1)
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ax_x = np.array(res['axis']); npd = res['n_per_dim']
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V = np.array(res['value']).reshape(npd, npd)
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print('V(0,0) =', V[npd // 2, npd // 2])
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print('V(±1,±1) =', V[0, 0], V[-1, -1])
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.. code-block:: python
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fig, ax = plt.subplots()
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im = ax.imshow(V.T, origin='lower', extent=(-1, 1, -1, 1), cmap='magma')
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ax.set_title('HJB value V(0, x, y) — quadratic terminal')
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plt.colorbar(im, ax=ax)
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fig.tight_layout(); plt.show()
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.. image:: ../_static/v2/pde/plot_03.png
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:align: center
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:width: 80%
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**Verified:** Poisson max-error vs analytic eigenfunction below `5e-3`; Fokker–Planck mean stays at 0 within `0.05`.
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API
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---
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.. code-block:: rust
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pub fn solve_fokker_planck_1d<F, G, H>(drift: F, diffusion_sq: G, initial_density: H, cfg: &FokkerPlanckConfig) -> Result<FokkerPlanckResult>
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where F: Fn(f64) -> f64, G: Fn(f64) -> f64, H: Fn(f64) -> f64;
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pub fn solve_hjb_multid<H, G>(hamiltonian: H, terminal: G, cfg: &HjbMultidConfig) -> Result<HjbMultidResult>
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where H: Fn(&[f64], &[f64]) -> f64, G: Fn(&[f64]) -> f64;
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pub fn solve_poisson_2d<F, G>(rhs: F, boundary: G, cfg: &EllipticFdConfig) -> Result<EllipticFdResult>
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where F: Fn(f64, f64) -> f64, G: Fn(f64, f64) -> f64;
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