Dear R users,
I'm trying to find a solution for optimizing my code. I have to run a 50.000
iteration long simulation and it is absolutely necessary to have an
optimized code.
I have to do this operation
*sum_t ( t(X_t) %*% A %*% X_t )*
where X_t is a (d*k) matrix which changes in time and A is a constant in
time (d*d) matrix.
I have put all my X_t in a three dimensional array X of dimension (d,k,T).
At the moment for computing the sum over time I'm doing a for loop and
saving the resulting (k*k) matrix in a list and at the end I sum the T
matrices in this list. I'm wondering if there is a better way to do this.
Here an example of what I have to do:
*d=3
k=2
T=4
X = array(rnorm(d*k*T),dim=c(d,k,T))
A = matrix(rnorm(d*d),d,d)
e1 = list()
for (t in 1:T){ #I would like to avoid this
e1[[t]] = t(X[,,t])%*%A%*%X[,,t]
}
##############################
#Function for doing the sum of matrices in a list
##############################
sumMatrices <- function(matrices){
if (length(matrices) > 2) matrices[[1]] + Recall(matrices[-1])
else matrices[[1]] + matrices[[2]]
}
##############################
result = sumMatrices(e1)
*
Thank you in advance for all your help,
best
Michela
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