Kevin Bealer wrote:
1. To solve the basic problem the original poster was asking -- if you are working with simple decimals and arithmetic you can get completely accurate representations this way. For some cases like simple financial work this might work really well. e.g. where float would not be because of the slow leak of information with each operation. (I assume real professional financial work is already done using a (better) representation.)
A reasonable way to do financial work is to use longs to represent pennies. After all, you don't have fractional cents in your accounts.
Using floating point to represent money is a disaster in the making.
2. To explain why the 'simple' task of representing something like .1 wasn't as easy as it looks. In other words, why the people who designed float weren't just brain dead. I think they really knew what they were doing but it shocks most people at first that a modern computer can't do what they see as grade school arithmetic.
There's just no getting around needing to understand how computer arithmetic works.
I think for some purposes though, lossless domain specific representations can be a good tool -- if you can represent a problem in a way that is lossless you can maybe do better calculations over long series than working with 'double' and taking the accuracy hit. This is necessarily an application specific technique though.
I.e. you have to know what you're doing :-)
