Algorithms


What and where precision matters

It is well known in all programming languages the floating point math loses some accuracy. Therefore we have something like the following statement 0.1 + 0.2 != 0.3 This is explained in detail in here, but basically, is because some fractional numbers aren’t well represented on binary, and the carryover loses accuracy, but it keeps precise. There are several ways to mitigate this effect, the most known use double numbers or big decimal representations, the problem with those is the operations tend to use more computing resources, especially with BigNumbers, that usually are objects with large strings and complicated math behind.

Divide and... conquer?

It has been taught “divide and conquer” is a great technique to solve any problem. Well, sometimes in practice it is harder than we, though, here is a case study of how our team apply this concept and still wasn’t enough, we need to divide even more and tweak some other things. The problem In the beginning, a the developer in charge to create an algorithm for the mean value for some data points did not have in mind the massive data and the required memory to process that piece of code in the future.