The code of this text could be found on this GitHub folder.
One of my favorite professors throughout my studies told me this:
“Simply because your algorithm is inefficient, it doesn’t mean that the issue is hard”
Which means if you wish to solve a whatever problem (easy or hard), there’ll all the time be an approach that’s naive enough to be extremely inefficient. For instance, let’s say you will have to go to work in a brand new workplace. As an alternative of using Google Maps, you begin from your home’s alley and check out all of the possible mixtures of the streets (north, south, west, and east). By the point you’ll arrive to work your organization is likely to be filing bankruptcy or having you fired.
Let’s attempt to be a bit of more formal. Let’s say that in whatever business or engineering environment, you will have to seek out the minimum or maximum of a function. For instance, your organization has to maximise revenue from the sales of a given department. We call this function f. The “strings” you pull, meaning the decisions which you could take to maximise the revenue is a vector x. You may’t obviously…
