On 04/02/2015 10:40 PM, Duncan Murdoch wrote:
On 02/04/2015 9:31 AM, Feng Li wrote:
Dear all,
I am working with a likelihood function that requires the inverse of
many small covariance matrices for multivariate normal densities. When
the sample size is large, this calculation is really heavy. Those
matrices are independent but unfortunately I can hardly find a way to
vectorize them.
Can anyone give me a hint to speed this up? Thanks in advance!
Are you sure you need the inverses of those matrices? For example, if
you are trying to compute x^t Ainv x,
where Ainv is A inverse, the naive calculation is t(x) %*% solve(A) %*%
x, but that's likely slower and less accurate than other equivalent
ones, such as x %*% solve(A, x), and I wouldn't be surprised if there
are better ones.
Duncan Murdoch
I agree what you suggested is good practice. But my likelihood function
needs to calculate "x %*% solve(A_i, x)" for i=1,...,n, which is the
bottom neck.
Feng
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