About Computational Patterns

Definitely I understand most of structural patterns, but when it comes to computational patters it becomes fuzzy. Maybe because I don’t like much that field (although I recognize its value and importance) or maybe because I have no solid base on that field.

If I must deal with Linear Algebra when solving a problem (linear operations on vectors or matrices), I will totally use a linear algebra library rather than trying to write functions and algorithms myself. That because is pointless to reinvent the wheel when I can save a lot of time using the standard mathematical operations and routines already packed in libraries where people have put a lot effort on.

Computation can help a lot when solving a problem. Graphs are a very good approach if the problem fits with a certain algorithm or known data structure. Experiments as in the Monte Carlo pattern is a technique that might be implemented more easily today using parallelism, since it requires the generation of thousands of experiments, it execution and to aggregate the results into relevant statistical solutions.

Computational patterns and structural patterns are clearly different to me. In structural patterns you are dealing with entities and its relationships and how to come up with good and efficient designs, while in computation patterns you are pushed to come up with mathematical models.

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