FAQ 7.31
Robin Hankin <r.hankin at noc.soton.ac.uk> writes:
Jacobi's theta functions crop up here and there in various branches of
mathematics,
such as unimodular and elliptic functions.
They have Taylor expansions, but the powers increase as the square of
n, as in
1 + z + z^4 + z^9 + z^16 + z^25 + . . .
so they converge very quickly, provided |z|<1
The following toy function shows how I'm implementing these objects.
I just add terms until
they make no difference:
f <- function(z, maxiter=10){
out.old <- 1
for(n in 1:maxiter){
out.new <- out.old + z^(n*n)
if(identical(out.new, out.old)){
return(out.new)
}
out.old <- out.new
}
stop("not converged")
}
[NB this is not a real theta function! Real theta functions are more
complicated. f() just shows the issues]
I worry about the use of identical() here, because I am comparing two
floats for equality
as discussed in FAQ 7.31. Perhaps all.equal() would be better:
all.equal(1e99,1e99+1e83, tol=.Machine$double.eps)
Does the List have any comments to make?
Shouldn't matter in this sort of case, since the added term is bound to underflow relative to the partial sum. The things to watch out for is that mathematical equality doesn't imply numerical equality (3 * 0.1 != 0.3), and sometimes granularity effects in correction terms. We've been bitten by code of the type x(n+1) = x(n) + f(x(n)) a couple of times in the numerical code, typically in Newton-type iterations where the correction term alternates by +/- a few units in the last place, and the program goes into an infinite loop. Optimizing compilers can introduce such effects in otherwise functioning code by applying mathematical transformations (see above...) to the code, rearranging terms, moving terms outside of loops, folding constants, etc.
O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907