Melvin Alvarez
29 lines
876 B
Markdown
29 lines
876 B
Markdown
# Optimal Control
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High-level bindings for HJB-style optimal control and Kalman filtering utilities.
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## Kalman Filter (sensor fusion)
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```python
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import numpy as np
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from optimizr import maths_toolkit
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# maths_toolkit is provided by the Rust extension (_core)
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F = np.eye(2) # state transition
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H = np.eye(2) # observation
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Q = 0.01 * np.eye(2)
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R = 0.1 * np.eye(2)
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if maths_toolkit is not None:
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kf = maths_toolkit.init_kalman_filter(F.tolist(), H.tolist(), Q.tolist(), R.tolist())
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state = maths_toolkit.kalman_predict(kf, [0.0, 0.0])
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print(state)
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else:
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print("Rust backend not available; install with `pip install .`.")
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```
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## Notes
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- Rust backend (`optimizr._core`) must be present for control utilities.
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- The API is thin and intentionally low-level; matrices are passed as lists.
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- For 1D Mean Field Games, use the dedicated guide in `mean_field_games.md`.
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