C W <tmrsg11 <at> gmail.com> writes:
>
> Hi list,
>
> I am running the following R code, the answer should be zero. But R gives
> a very small negative number, what should I do?
>
> ##R code
>
library(numDeriv)
h_x <- function(x){
a = x[1]
b = x[2]
c = x[3]
d = x[4]
(a^2 + c^2 + d^2) * (b^2 + c^2 + d^2)
}
x1 = 10
x2 = 1
x3 = 0
x4 = 10
x = c(x1, x2, x3, x4)
hess.h <- hessian(func = h_x, x)
mat <- h_x(x)*hess.h - grad(h_x, x) %o% grad(h_x, x)
m <- zapsmall(mat)
sum(eigen(m)$val[2:4])
I get a much smaller answer than you do (several orders of magnitude):
[1] 2.421439e-08
This is with:
R Under development (unstable) (2015-02-11 r67792)
Platform: i686-pc-linux-gnu (32-bit)
Running under: Ubuntu precise (12.04.5 LTS)
numDeriv_2012.9-1
However, you mostly have to learn to deal with the
inherent imprecision of floating-point calculations.
It's hard to tell without any further context, but there
may be a more numerically stable way to do what you want.
> [1] 12160648 8103268 1665782 -9769050
>
> > sum(eigen(m)$val[2:4])
>
> [1] -0.0005430793
>
> ## End of R code
>
> The last answer above should be zero, but it's appearing as a very small
> negative number. How should I deal with it?
>
> Thanks lots,
>
> Mike
>
> [[alternative HTML version deleted]]
>
>
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