⚡ Bolt: Cache matrix inversions in vuongtest to reduce redundant calculations#15
⚡ Bolt: Cache matrix inversions in vuongtest to reduce redundant calculations#15seonghobae wants to merge 1 commit into
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…3) calculations Added `invA1` and `invA2` to cache the results of `chol2inv(chol())` in `calcLambda` inside `vuongtest.R`, halving the matrix inversion overhead.
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Pull request overview
This PR optimizes vuongtest’s calcLambda() by caching expensive matrix inversions so they are computed once and reused when constructing the W matrix, reducing redundant chol2inv(chol()) calls.
Changes:
- Hoists
chol2inv(chol(AB1$A))andchol2inv(chol(AB2$A))into cached variables (invA1,invA2) to avoid repeated O(N³) work. - Adds a
.jules/bolt.mdnote documenting the optimization rationale.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 1 comment.
| File | Description |
|---|---|
| R/vuongtest.R | Caches matrix inversions inside calcLambda() to avoid recomputation during W assembly. |
| .jules/bolt.md | Adds an internal/automation note describing the optimization and guiding future inspections. |
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| ## 2024-05-18 - Caching Matrix Inversions in Vuong Test | ||
| **Learning:** Found redundant `chol2inv(chol())` calls within the `calcLambda` matrix construction block in `vuongtest.R`. Matrix inversions are $O(N^3)$, so caching them and reusing is an effective and non-intrusive optimization for matrix-heavy statistic R packages. | ||
| **Action:** When inspecting matrix operations inside block initializations or recursive calls, always check if invariant intermediate expressions like inversion or decomposition are evaluated multiple times and hoist them. |
💡 What
Cached the result of matrix inversions (
chol2inv(chol())) inside thecalcLambdafunction inR/vuongtest.R.🎯 Why
Matrix inversions scale with$O(N^3)$ complexity. The previous implementation called
chol2inv(chol(AB1$A))andchol2inv(chol(AB2$A))twice each while assembling theWmatrix block. Hoisting these intoinvA1andinvA2variables prevents these redundant computations, reducing overall runtime for complexvuongtestcalls.📊 Impact
Halves the number of matrix inversions inside
calcLambda. This reduces the computational overhead significantly for model comparisons involving large asymptotic covariance matrices, accelerating the test without any changes to statistical outcomes.🔬 Measurement
Run a profiling trace (
Rprof) overvuongtest(model1, model2)on models with large coefficient matrices, and observe that the cumulative time spent inchol()andchol2inv()drops by up to 50%. Statistical results remain mathematically identical.PR created automatically by Jules for task 15346295909571307141 started by @seonghobae