Melvin Alvarez
28 lines
680 B
Markdown
28 lines
680 B
Markdown
# Grid Search
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Deterministic hyper-parameter sweeps with optional Rust acceleration.
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## Usage
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```python
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from optimizr import grid_search
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# Objective returns a scalar score (lower is better)
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def objective(params):
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lr, dropout = params["lr"], params["dropout"]
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return (lr - 0.02)**2 + (dropout - 0.1)**2
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best_params, best_score = grid_search(
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objective_fn=objective,
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param_grid={"lr": [0.005, 0.02, 0.05], "dropout": [0.05, 0.1, 0.2]},
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)
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print(best_params)
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print(best_score)
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```
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## Notes
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- The objective receives a dict of parameters.
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- Exhaustive search is deterministic; keep grids small for large models.
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- Combine with DE for warm-starting a local region.
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