13 Feb
2020
13 Feb
'20
5:56 a.m.
On Feb 12, 2020, at 3:54 PM, Bakul Shah
<bakul(a)bitblocks.com> wrote:
On Feb 12, 2020, at 3:05 PM, Larry McVoy <lm(a)mcvoy.com> wrote:
Er... not right. Fl. pt. arithmetic is hard!
On Wed, Feb 12, 2020 at 04:45:54PM -0600, Charles H Sauer wrote:
David Goldberg's article "What every computer scientist should know
about floating-point arithmetic" is a good one to read:
https://www.itu.dk/~sestoft/bachelor/IEEE754_article.pdf
I still have Bill Press's Numerical Recipes book though not opened
recently (as in not since '80s)!
It is interesting that older languages such as Lisp & APL have a
builtin concept of tolerance. Here 0.3 < 0.1 + 0.2 is false. But
in most modern languages it is true! This is so since 0.1 + 0.2 is
0.30000000000000004. In Fortran you'd write something like
abs(0.3 - (0.1 + 0.2)) > tolerance. You can do the same in C
etc.but for some reason it seems to be uncommon :-) If I recall correctly:
- all doctoral candidates ended up taking two semesters of numerical
analysis. I still have two volume n.a. text in the attic (orange, but not
"burnt orange", IIRC).
- numerical analysis was covered on the doctoral qualifying exam.
Pretty sure Madison required that stuff for Masters degrees. Or maybe
undergrad, I feel like I took that stuff pretty early on.
I'm very systems oriented so I can't imagine I would have taking that
willingly. I hate the whole idea of floating point, just seems so
error prone.