In this series of posts we’ll cover important concepts from computability theory; techniques for designing efficient algorithms for combinatorial, algebraic, and (if I can learn enough about it), number-theoretic problems. It’ll serve as a compact way to familiarize ourselves with basic concepts such as NP-Completeness from computational complexity theory, through Python.
The only pre-requisite is that you know what Big-O notation is conceptually, even if you don’t have a good intuition for why certain algorithms are one complexity versus another. Basically know that it is a relative representation of the complexity of an algorithm. More specifically, the order of growth in the time of execution depending on the length of the input.