Comments by "" (@rumfordc) on "10 weird algorithms" video.

there's not really such a thing as "algorithms for a living" but its never too late to learn an algorithm. most of them are free to learn about or even take a class on. the ones shown in this video are only going to be used in specialized fields though.
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Al Gore-isms, they were originally called
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usually sorting is measured in terms of comparisons, in which case it would be O(1) but that's obviously misleading. its a good example of how there is more to performance than a single runtime complexity.
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its probably because you're not just looking, you're interfering with them. it's like if you made a driver fill out a form every time they come to an intersection in order to track their routes, but this would change how they drive because they'll be incentivized to take routes with fewer intersections so they can save time not filling out forms.
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@danser_theplayer01 you have other faculties besides looking though. if someone is throwing baseballs and you stick your hand out to measure how many baseballs are passing through, you're going to change their trajectory whether you're looking or not. so the look-and-it-changes part really isn't so mysterious. we see that sort of measurement-interference in all facets of life.
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@sean-ih5fp are you able to understand the pseudo code? i don't know which papers you're referring to but I'd start at Wikipedia. Pretty much every algorithm, its pseudo code, and its run time complexities are there. I think you should start with a simple sorting algorithm that is easy to follow, such as bubble-sort. Once you understand the code, what it is doing, when it is fast and when it is slow, then you can make more sense of the notation for things like runtime complexity
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@sean-ih5fp oh ok then start by getting more comfortable with python, then try writing something like a Bubble Sort function. You don't really need any math to do that. You'll still be a long ways from being able to make a living, but it should help a lot when it comes to understanding people when they talk about algorithms.
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