Always false float comparisons

[jmh530 via Digitalmars-d]

On Wednesday, 18 May 2016 at 21:49:34 UTC, Joseph Rushton 
Wakeling wrote:
> On Wednesday, 18 May 2016 at 20:29:27 UTC, Walter Bright wrote:
>> I do not understand the tolerance for bad results in 
>> scientific, engineering, medical, or finance applications.
>
> I don't think anyone has suggested tolerance for bad results in 
> any of those applications.
>

I don't think its about tolerance for bad results, so much as the 
ability to make the trade-off between speed and precision when 
you need to.

Just thinking of finance: a market maker has to provide quotes on 
potentially thousands of instruments in real-time. This might 
involve some heavy calculations for options pricing. When dealing 
with real-time tick data (the highest frequency of financial 
data), sometimes you take shortcuts that you wouldn't be willing 
to do if you were working with lower frequency data. It's not 
that you don't care about precision. It's just that sometimes 
it's more important to be fast than accurate.

I'm not a market maker and don't work with high frequency data. I 
usually look at low enough frequency data so that I actually do 
generally care more about accurate results than speed. 
Nevertheless, sometimes with hefty simulations that take several 
hours or days to run, I might be willing to take some short cuts 
to get a general idea of the results. Then, when I implement the 
strategy, I might do something different.