Optimizing Inefficiency: the quest for the worst sorting algorithm(medium.freecodecamp.org)
medium.freecodecamp.org
Optimizing Inefficiency: the quest for the worst sorting algorithm
https://medium.freecodecamp.org/optimizing-inefficiency-human-folly-and-the-quest-for-the-worst-sorting-algorithm-c0ba7b32ffd
14 comments
I like sleepsort:
The bead sort is a really smartass solution that could be considered an instructional example of the benefits of parallelization as well as exploitation of situational advantages.
Gravity effectively gives infinite free "threads" for the falling and it engages in accounting tricks with assumptions - normally the starting state set up is taken for granted because either it is already there or would always have to be done the same way (no getting around reading in the N numbers) which is more involved in a physical system.
I liken it to being asked to give the value of pi and giving the answer as a red circle split by a green line in the center and writing length of red divided by the length of green. Technically correct and efficient in calculations but extracting it involves more work not in the solution.
Gravity effectively gives infinite free "threads" for the falling and it engages in accounting tricks with assumptions - normally the starting state set up is taken for granted because either it is already there or would always have to be done the same way (no getting around reading in the N numbers) which is more involved in a physical system.
I liken it to being asked to give the value of pi and giving the answer as a red circle split by a green line in the center and writing length of red divided by the length of green. Technically correct and efficient in calculations but extracting it involves more work not in the solution.
I agree, and wrote a lengthy comment below about why this "algorithm" would actually be considered exponential time using the accepted definition of time complexity in theoretical CS.
Having spent some time learning counterpoint, it interesting to see how complex it is, and how completely baffled I was to see how Bach's works seemed to be effortless compared to my uninspired attempts.
Surely the time complexity of the abacus sort is O(ksqrt(n)+cn)? What with the time taken for the beads to fall being proportional to the square root of the distance under freefall and proportional to n itself when at terminal velocity? (so strictly O(n)). Radix sort on a known range of integers has some similar-ish properties.
I was going to comment on this already, but even that's way too "fast" based on the accepted notion of time complexity in theoretical CS... abacus sort should technically be an exponential-time algorithm! (And yes, I disagree with the wikipedia article on this topic, which lists several "possible" time complexities, all of which I consider incorrect.)
The reason being that the accepted definition of time complexity in formal literature is standardized not based on the number of "things" in the input (numbers in a list, nodes in a graph, etc.), but on the length of the input string. This is a common pitfall when people reason about time complexity. Here's why this applies to abacus sort:
1. Abacus sort contains integers in its input string--and based on the conventions of formal time complexity, these can not be encoded in unary (if unary encodings were allowed, then several famous NP-Complete algorithms, e.g. bin packing, would have polynomial time solutions based on the length of their input)
2. The value of an integer is exponentially related to its length. For example, making a binary integer only one bit longer can double its value, making it two bits longer will quadruple its value, etc.
3. The input to a sorting algorithm like abacus sort is a list of numbers. Even if there are n numbers, the number of "beads" you need to simulate is exponentially proportional to the length of the longest single number.
4. Even if you think that the number of beads can be simulated with an infinite number of threads, or something, you'd still need to decode the (at least) binary input to a unary number of beads, which will take an exponentially-related amount of memory.
Of course, all this is not to say the algorithm isn't cool in practice, for numbers in ranges that reasonable people care about. It's similar to a weakly-NP-complete problem (https://en.wikipedia.org/wiki/Weak_NP-completeness) like integer bin packing, where the time complexity is dependent on the longest number and therefore technically exponential, but in practice solved for any reasonable ranges of values in polynomial time.
Edit: By the way, this is why the naive prime-number-checking algorithm (check divisibility of a value v by all values up to sqrt(v) is not an efficient algorithm even though many people incorrectly think it's O(sqrt(n))!
It technically takes exponential time relative to the length of bits in the number, so it's basically useless for guaranteeing primality of something like a 4096-bit number.
The reason being that the accepted definition of time complexity in formal literature is standardized not based on the number of "things" in the input (numbers in a list, nodes in a graph, etc.), but on the length of the input string. This is a common pitfall when people reason about time complexity. Here's why this applies to abacus sort:
1. Abacus sort contains integers in its input string--and based on the conventions of formal time complexity, these can not be encoded in unary (if unary encodings were allowed, then several famous NP-Complete algorithms, e.g. bin packing, would have polynomial time solutions based on the length of their input)
2. The value of an integer is exponentially related to its length. For example, making a binary integer only one bit longer can double its value, making it two bits longer will quadruple its value, etc.
3. The input to a sorting algorithm like abacus sort is a list of numbers. Even if there are n numbers, the number of "beads" you need to simulate is exponentially proportional to the length of the longest single number.
4. Even if you think that the number of beads can be simulated with an infinite number of threads, or something, you'd still need to decode the (at least) binary input to a unary number of beads, which will take an exponentially-related amount of memory.
Of course, all this is not to say the algorithm isn't cool in practice, for numbers in ranges that reasonable people care about. It's similar to a weakly-NP-complete problem (https://en.wikipedia.org/wiki/Weak_NP-completeness) like integer bin packing, where the time complexity is dependent on the longest number and therefore technically exponential, but in practice solved for any reasonable ranges of values in polynomial time.
Edit: By the way, this is why the naive prime-number-checking algorithm (check divisibility of a value v by all values up to sqrt(v) is not an efficient algorithm even though many people incorrectly think it's O(sqrt(n))!
It technically takes exponential time relative to the length of bits in the number, so it's basically useless for guaranteeing primality of something like a 4096-bit number.
> 3. The input to a sorting algorithm like abacus sort is a list of numbers. Even if there are n numbers, the number of "beads" you need to simulate is exponentially proportional to the length of the longest single number.
I think this is where we'd mismatch: I'd assume baked into the problem space is that you have N numbers to sort, they are each of size K where K is fixed and bounded. As a solution to "sort these N 16-bit ints, for some arbitrary N". It would be same problem space that can be solved in O(n) with bucket/counting sorts.
I think this is where we'd mismatch: I'd assume baked into the problem space is that you have N numbers to sort, they are each of size K where K is fixed and bounded. As a solution to "sort these N 16-bit ints, for some arbitrary N". It would be same problem space that can be solved in O(n) with bucket/counting sorts.
This is true, but note that the problem in general doesn't specify that the ints are only 16-bit. It's already well established that "linear time" sorting algorithms exist (e.g. radix sort) for numbers that are all a bounded size. However, the key is still that the length of each number must be bounded, because that ensures that the length of the input relates to the number of numbers in the list to be sorted, rather than to the length of the largest number. (The Wikipedia article on radix sort gets this right... Just not the one on abacus sort)
Yeah, it's O(N) in air and O(sqrt(N)) in a vacuum. The O(1) thing assumes all the beads move simultaneously in the same time unit, which isn't possible in practice of course.
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An idea: Use Bitcoin’s PoW as the function for Worstsort.
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