⚡ Bolt: Optimize array allocations during objective gradient calculation

[seonghobae]

💡 What: The optimization implements a memory-efficient vectorized operation for calculating grad_alpha during model fitting. It replaces element-wise multiplication on broadcast arrays (e * params.theta[:, factors]) that requires allocating an intermediate $(N, J)$ array with a dense matrix multiplication e.T @ params.theta and fast advanced integer indexing.
🎯 Why: In Item Response Theory models with large sample sizes ($N$) and many items ($J$), creating an $N \times J$ intermediate float array during every step of gradient calculation creates a massive bottleneck due to memory allocation overhead and poor CPU cache utilization.
📊 Impact: Reduces memory allocation operations during gradient steps. For large datasets, this is expected to yield measurable speedups in the model fitting pipeline by leveraging BLAS.
🔬 Measurement: Run the internal simulation loops with a profiler (cProfile) or check execution time for dense matrix calculations; time spent in objective.neg_loglik_and_grad operations decreases.

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PR created automatically by Jules for task 14296781845038760425 started by @seonghobae

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[Copilot]

Pull request overview

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This PR optimizes gradient calculations in the IRT objective to reduce large intermediate array allocations by switching from broadcasted element-wise ops to BLAS-backed matrix multiplications and indexing.

Changes:

• Compute grad_alpha via e.T @ theta + advanced indexing to avoid allocating an (N, J) intermediate.
• Replace np.dot(...) with @ for grad_xi / grad_zeta computations (same semantics, consistent style).
• Document the optimization learning in .jules/bolt.md.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.

File	Description
python/fast_mlsirm/objective.py	Reworks gradient math to reduce allocations and leverage matrix multiplication/indexing.
.jules/bolt.md	Adds internal notes about preferring matmul over broadcasted reductions for performance.

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📥 Commits

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📒 Files selected for processing (2)

• .jules/bolt.md
• python/fast_mlsirm/objective.py

✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Commit unit tests in branch perf/vectorize-gradient-allocations-14296781845038760425

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[Copilot]

Pull request overview

Copilot reviewed 2 out of 2 changed files in this pull request and generated no new comments.

[seonghobae]

중복 정리: objective.py의 grad_alpha 행렬곱 최적화로, 리뷰 대기 중인 #177 과 동일 대상·동일 취지입니다. 그룹당 대표 1건만 남기는 백로그 정리 방침(유지보수자 승인, #161–#206 정리와 동일)에 따라 닫습니다. 필요 시 재오픈 가능합니다.

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Generated by Claude Code

[google-labs-jules]

중복 정리: objective.py의 grad_alpha 행렬곱 최적화로, 리뷰 대기 중인 #177 과 동일 대상·동일 취지입니다. 그룹당 대표 1건만 남기는 백로그 정리 방침(유지보수자 승인, #161–#206 정리와 동일)에 따라 닫습니다. 필요 시 재오픈 가능합니다.

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Generated by Claude Code

Understood. Acknowledging that this work is now obsolete and stopping work on this task.