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⚡ Bolt: Cache redundant matrix inversions in vuongtest.R #12

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⚡ Bolt: Cache redundant matrix inversions in vuongtest.R #12

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3 changes: 3 additions & 0 deletions .jules/bolt.md
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Original file line number Diff line number Diff line change
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## 2024-05-24 - Avoid Redundant Matrix Inversions in Formulaic Translations
**Learning:** Formulaic translation of statistical math into R code often leads to redundant matrix inversions (like computing `chol2inv(chol(A))` twice). This is an O(n^3) operation that causes unnecessary overhead.
**Action:** Always check for repeated identical calculations, especially expensive ones like matrix inversions or decompositions, and cache their results in variables to be reused.
14 changes: 10 additions & 4 deletions R/vuongtest.R
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Expand Up @@ -271,10 +271,16 @@ calcLambda <- function(object1, object2, n, score1, score2, vc1, vc2) {
AB2 <- calcAB(object2, n, score2, vc2)
Bc <- calcBcross(AB1$sc, AB2$sc, n)

W <- cbind(rbind(-AB1$B %*% chol2inv(chol(AB1$A)),
t(Bc) %*% chol2inv(chol(AB1$A))),
rbind(-Bc %*% chol2inv(chol(AB2$A)),
AB2$B %*% chol2inv(chol(AB2$A))))
## Performance optimization: Cache redundant matrix inversions
## Computing chol2inv(chol(A)) is an O(n^3) operation. Doing it once per
## block instead of twice saves significant computation time for large matrices.
invA1 <- chol2inv(chol(AB1$A))
invA2 <- chol2inv(chol(AB2$A))

W <- cbind(rbind(-AB1$B %*% invA1,
t(Bc) %*% invA1),
rbind(-Bc %*% invA2,
AB2$B %*% invA2))

lamstar <- eigen(W, only.values=TRUE)$values
## Discard imaginary part, as it only occurs for tiny eigenvalues?
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