When dealing with geometric algorithms, it is crucial to craft code that is not only highly robust but also efficient. Geometric algorithms tend to be error-prone, relying heavily on numerical precision. They often involve numerous edge cases that must be addressed separately, incorporating computations with numerical optimization and approximation. These complexities often demand solutions with a global context, involving resource-intensive operations, which can contribute to slow algorithm performance in practice.

​

While a naive and straightforward implementation might work for small-scale problems, it would likely fail for larger-scale issues and less-than-ideal input data. Since real-world data is often complex and imperfect, employing a simplistic approach could lead to the breakdown of the geometric pipeline. Ad-hoc solutions would also fail in the long-run. It is therefore essential to implement the pipeline adhering to the best coding standards, ensuring the algorithm supports all edge cases and optimally uses available resources.

​

I usually use the best coding practices and modern C++ to write an efficient and robust code but not constrained to it. It might be a combination of languages or another language. But with C++ I guarantee the best quality of the algorithm in query. I always refactor and keep my code clean and modular. That makes it possible to add new features, modify, tune, and improve this code locally without affecting all the rest of the code base. That also helps newcomers to contribute and support my implementations later.