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.
82 KiB
82 KiB
In [1]:
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
In [2]:
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))))
h drift from 1: 0.0
In [3]:
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()
In [4]:
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()
