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Efficiency, not effectiveness. They are all effective in the sense that they produce sorted results. Even the non-modern sort algorithms are effective in the sense that the results are correct. This should be about the efficiency with which they do it, right?
Isn't this just another case of premature optimization? Shouldn't you be adjusting sorting algorithms only when customer complains?
This is pushing the limits to identify the boundaries
I find in practice that if the sorting process is too slow, you should begin thinking about different ways to attack the problem. Maintaining a total global order of things tends to only get more expensive over time as the scope of your idea/product/business expands. The computational complexity of the sort algorithm is irrelevant once we get into memory utilization. This is why we have things like tournament selecti…
Earlier quoted context omitted.
This is pushing the limits to identify the boundaries
Also known as premature optimization. You had to literally invent new dataset just to show there is a difference. You are inventing problems, stop doing that!
Sometimes that is how useful jumps are made. Maybe someone will come along with a problem and the data they have just happens to have similar properties.
Rather than premature optimisation this sort of thing is pre-emptive research - better to do it now than when you hit a performance problem and need the solution PDQ. Many useful things have come out of what started as “I wonder what if …?” playing.
Efficiency, not effectiveness. They are all effective in the sense that they produce sorted results. Even the non-modern sort algorithms are effective in the sense that the results are correct. This should be about the efficiency with which they do it, right?
The title is an homage to Eugene Wigner's 1960 paper "The Unreasonable Effectiveness of Mathematics in the Natural Sciences".
Isn't this just another case of premature optimization? Shouldn't you be adjusting sorting algorithms only when customer complains?
Then again only thinking of fixing things when a customer complains is a way to end up with a leaning tower of hacks which eventually ossify and also the customer (or rather the users, who may not be the customer especially in business software) may be putting up with dozens of niggles and annoyances before they bother to actually report one bug because they can't work around it.
Efficiency, not effectiveness. They are all effective in the sense that they produce sorted results. Even the non-modern sort algorithms are effective in the sense that the results are correct. This should be about the efficiency with which they do it, right?
"The Unreasonable Effectiveness of Mathematics in the Natural Sciences" is one of those titles that gets imitated a lot for some reason. Maybe even more than "Goto Considered Harmful".
Isn't this just another case of premature optimization? Shouldn't you be adjusting sorting algorithms only when customer complains?
This is research or experimentation, designed to improve our understanding of the behavior of algorithms. Calling it premature optimization makes no sense.
I find in practice that if the sorting process is too slow, you should begin thinking about different ways to attack the problem. Maintaining a total global order of things tends to only get more expensive over time as the scope of your idea/product/business expands. The computational complexity of the sort algorithm is irrelevant once we get into memory utilization. This is why we have things like tournament selecti…
Sometimes when you think you need to maintain a sorted array under item insertion, it turns out that you only ever need to continually read the next-smallest (or next-largest) item -- and in that case, it suffices to maintain a heap, which is much cheaper.
> If you have a trouble solving some problem, see if sorting the data first helps.
I feel that sorting data is the ultimate computer science hack. Many, many, classes of problems turn into O(log n) problems, once you sort your input in some way. It might not be the most effective way of solving the problem, but it's often a fairly good one.
So I'm really enjoying how good sorting algorithms are getting and how despite the O complexity remains mostly the same, the real computing efficiency is improving significantly.