Structural Optimisation in Python

Looking at the bigger picture.

Structural optimisation takes a big picture approach. It attempts to optimise code by solving the problem in a different way, for example:

These techniques are not a replacement for low-level optimisation, they represent an additional way of optimising code. It isn’t always possible to apply this type of optimisation. For example, if you are already using the most efficient known algorithm then there may be no way to improve it. But where structural changes do apply they can sometimes lead to dramatic improvements. And, of course, having found the most efficient structural solution, it might still be possible to make it even faster (if necessary) using low-level optimisations.

This is a big topic, this article is only really an overview of techniques rather than a comprehensive guide.

There are many standard algorithms in computer science that cover tasks such as:

There are many other algorithms for other tasks, of course. There are often several algorithms that achieve the same result but work in different ways. And sometimes, one algorithm can be much faster than another, depending on the specific requirements of your application.

There are also many data structures. Again there are often several different data structures that could be used for any particular purpose, but some might be more efficient than others for a particular purpose.

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