Rust backend optimizations #67
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Bootstrap weight generation (16x average speedup): - Direct Array2 allocation eliminates Vec<Vec<f64>> intermediate - Rayon chunk size tuning (min_len=64) reduces scheduling overhead - Webb distribution uses lookup table instead of 6-way if-else Linear algebra optimizations: - Cholesky factorization for SPD matrix inversion with LU fallback - Vectorized HC1 meat computation: X' @ (X * e²) via BLAS - Vectorized score computation: broadcast multiplication Static BLAS linking options: - Added openblas-static and intel-mkl-static features for distribution Benchmark results (release build, Apple Silicon): - Bootstrap Rademacher: 6-14x speedup - Bootstrap Mammen: 11-24x speedup - Bootstrap Webb: 12-32x speedup - OLS (small <1K obs): 2-3x speedup - OLS (large >10K obs): 0.3-0.7x (Apple Accelerate faster) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Update version to 2.0.3 in pyproject.toml, __init__.py, Cargo.toml - Add CHANGELOG entry documenting Rust backend optimizations - Update TODO.md to mark Rust optimizations as completed in v2.0.3 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Tests verify that when Cholesky factorization fails due to near-singular or high condition number matrices, the Rust backend correctly falls back to LU decomposition: - test_near_singular_matrix_lu_fallback: Near-collinear design matrix - test_high_condition_number_matrix: Extreme column scaling - test_near_singular_with_clusters: Near-singular with cluster-robust SEs Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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