Its not. Very simple bcoz using just a single mehod that returs 0 if negative or an addition result. Very logical and concise solution.

A quick rundown of my solution versurs geoffp's:

• The base idea is the same: solving a linear system using all the given points
• Instead of a dispersion matrix, I derived a custom one (no idea if they're the same)
• Whenever sums of many elements are involved, the Kahan-Babushka-Neumaier algorithm was used to minimise sum error
  • In hindsight, I think the precision gained/error avoided was minuscule. But the algorithm is very easy!
• The given points are normalised against a bounding box (centre and scale), not centroid
  • I felt this was more balanced than centroid when too many points are clustered together, and scaling protects against under/overflow
• The system is solved by Gauss-Jordan elimination with complete pivoting, not Cholesky decomposition
  • I don't really know Cholesky decomposition, but Gauss-Jordan is both easy and numerically stable

Please elaborate? I wanna know what you mean

Yes, but the code will never attempt to read it. That statement is in the if-condition, preceded by the statement "i < roman.size() - 1 && (...)". If the previous statement is false, the if-condition is "short-circuited", meaning the second statement is ignored.

In the last i , roman[i+1] is out of index. isn't it?

This is amazing solution

This is a great solution.

The performance difference is negligible between the two