Full_Name: Hao Yu
Version: R-1.4.1
OS: Windows and Linux
Submission from: (NULL) (129.100.242.16)
I just report what I find for the solve() function when I was doing benchmark.
A specific function Toeplitz is used as following
"Toeplitz" <- function(x)
{
matrix(x[1 + abs(outer(seq(along = x), seq(along = x), FUN =
"-"))],
byrow = T, ncol = length(x))
}
A. On a dual PIII 1GHz Xeon Linux Redhat 7.2 (R-1.4.1 is recompiled to create
libR.so. No change to other settings. All libraries come with redhat.)
*****************************************************> system.time(solve(matrix(rnorm(1000000),nrow=1000)))
[1] 35.04 0.30 35.33 0.00 0.00> system.time(solve(Toeplitz(.5^seq(0,999))))
[1] 53.28 0.43 53.69 0.00 0.00
*****************************************************
B. On a PIII-M 1.2GHz laptop running Windows Xp Pro (download binary R from
CRAN)
*****************************************************> system.time(solve(matrix(rnorm(1000000),nrow=1000)))
[1] 28.18 0.05 28.24 NA NA> system.time(solve(Toeplitz(.5^seq(0,999))))
[1] 33.80 0.03 34.14 NA NA
*****************************************************
C. On a PIII 1.125GHz desktop Windows 2k (Download binary R from CRAN)
*****************************************************> system.time(solve(matrix(rnorm(1000000),nrow=1000)))
[1] 32.31 0.14 36.37 NA NA> system.time(solve(Toeplitz(.5^seq(0,999))))
[1] 36.45 0.16 40.84 NA NA
*****************************************************
Clearly solve() does a random matrix inverse faster than a positive definite and
symmetric matrix (covariance matrix). I do not think the results (inverses) are
wrong. But it is odd regarding the timing for solve(). Using solve.Matrix() from
the package Matrix, I got almost identical timings (it fails to identify
positive definite and symmetric matrices).
Thanks
Hao Yu
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