I need only the diagonal elements from a matrix multiplication:
,
in R. As Z is huge I want to avoid the full out multiplication....
Z <- matrix(c(1,1,1,2,3,4), ncol = 2)
Z
# [,1] [,2]
#[1,] 1 2
#[2,] 1 3
#[3,] 1 4
X <- matrix(c(10,-5,-5,20), ncol = 2)
X
# [,1] [,2]
#[1,] 10 -5
#[2,] -5 20
Z %*% D %*% t(Z)
# [,1] [,2] [,3]
#[1,] 70 105 140
#[2,] 105 160 215
#[3,] 140 215 290
diag(Z %*% D %*% t(Z))
#[1] 70 160 290
X is always a small square matrix (2x2 , 3x3 or 4x4), where Z will have the number of columns equal to the dimension of X. Is there a function available to do this?
I don't think you can avoid the first matrix multiplication (i.e. ZX
), but you can the second one, which is the expensive one:
rowSums((Z %*% X) * Z)
# [1] 70 160 290
The second multiplication is NOT a matrix multiply. This is much faster:
library(microbenchmark)
set.seed(1)
X <- matrix(c(10,-5,-5,20), ncol = 2)
Z <- matrix(sample(1:1000), ncol=2) # made Z a little bigger
microbenchmark(
res.new <- rowSums((Z %*% X) * Z), # this solution
res.old <- diag(Z %*% X %*% t(Z)) # original method
)
# Unit: microseconds
# expr min lq mean median uq max neval
# res.new <- rowSums((Z %*% X) * Z) 20.956 23.233 34.77693 29.6150 44.0025 67.852 100
# res.old <- diag(Z %*% X %*% t(Z)) 571.214 699.247 1761.08885 760.4295 1188.4485 47488.543 100
all.equal(res.new, res.old)
# [1] TRUE
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