Sorting in Computer Science vs. sorting in the 'real' world
I was thinking about sorting algorithms in software, and possible ways one could surmount the O(nlogn) roadblock. I don't think it IS possible to sort faster in a practical sense, so please don't think that I do.
With that said, it seems with almost all sorting algorithms, the software must know the position of each element. Which makes sense, otherwise, how would it know where to place each element according to some sorting criteria?
But when I crossed this thinking with the real world, a centrifuge has no idea what position each molecule is in when it 'sorts' the molecules by density. In fact, it doesn't care about the position of each molecule. However it can sort trillions upon trillions of items in a relatively short period of time, due to the fact that each molecule follows density and gravitational laws - which got me thinking.
Would it be possible with some overhead on each node (some value or method tacked on to each of the nodes) to 'force' the order of the list? Something like a centrifuge, where only each element cares about its relative position in space (in relation to other nodes). Or, does this violate some rule in computation?
I think one of the big points brought up here is the quantum mechanical effects of nature and how they apply in parallel to all particles simultaneously.
Perhaps classical computers inherently restrict sorting to the domain of O(nlogn), where as quantum computers may be able to cross that threshold into O(logn) algorithms that act in parallel.
The point that a centrifuge being basically a parallel bubble sort seems to be correct, which has a time complexity of O(n).
I guess the next thought is that if nature can sort in O(n), why can't computers?
EDIT: I had misunderstood the mechanism of a centrifuge and it appears that it does a comparison, a massively-parallel one at that. However there are physical processes that operate on a property of the entity being sorted rather than comparing two properties. This answer covers algorithms that are of that nature.
A centrifuge applies a sorting mechanism that doesn't really work by means of comparisons between elements, but actually by a property ('centrifugal force') on each individual element in isolation.Some sorting algorithms fall into this theme, especially Radix Sort. When this sorting algorithm is parallelized it should approach the example of a centrifuge.
Some other non-comparative sorting algorithms are Bucket sort and Counting Sort. You may find that Bucket sort also fits into the general idea of a centrifuge (the radius could correspond to a bin).
Another so-called 'sorting algorithm' where each element is considered in isolation is the Sleep Sort. Here time rather than the centrifugal force acts as the magnitude used for sorting.
Computational complexity is always defined with respect to some computational model. For example, an algorithm that's O(n) on a typical computer might be O(2n) if implemented in Brainfuck.
The centrifuge computational model has some interesting properties; for example:
- it supports arbitrary parallelism; no matter how many particles are in the solution, they can all be sorted simultaneously.
- it doesn't give a strict linear sort of particles by mass, but rather a very close (low-energy) approximation.
- it's not feasible to examine the individual particles in the result.
- it's not possible to sort particles by different properties; only mass is supported.
Given that we don't have the ability to implement something like this in general-purpose computing hardware, the model may not have practical relevance; but it can still be worth examining, to see if there's anything to be learned from it. Nondeterministic algorithms and quantum algorithms have both been active areas of research, for example, even though neither is actually implementable today.
The trick is there, that you only have a probability of sorting your list using a centrifuge. As with other real-world sorts [citation needed], you can change the probability that your have sorted your list, but never be certain without checking all the values (atoms).
Consider the question: "How long should you run your centrifuge for?"
If you only ran it for a picosecond, your sample may be less sorted than the initial state.. or if you ran it for a few days, it may be completely sorted. However, you wouldn't know without actually checking the contents.
A real world example of a computer based "ordering" would be autonomous drones that cooperatively work with each other, known as "drone swarms". The drones act and communicate both as individuals and as a group, and can track multiple targets. The drones collectively decide which drones will follow which targets and the obvious need to avoid collisions between drones. The early versions of this were drones that moved through way points while staying in formation, but the formation could change.
For a "sort", the drones could be programmed to form a line or pattern in a specific order, initially released in any permutation or shape, and collectively and in parallel they would quickly form the ordered line or pattern.
Getting back to a computer based sort, one issue is that there's one main memory bus, and there's no way for a large number of objects to move about in memory in parallel.
know the position of each element
In the case of a tape sort, the position of each element (record) is only "known" to the "tape", not to the computer. A tape based sort only needs to work with two elements at a time, and a way to denote run boundaries on a tape (file mark, or a record of different size).
IMHO, people overthink log(n). O(nlog(n)) IS practically O(n). And you need O(n) just to read the data.
Many algorithms such as quicksort do provide a very fast way to sort elements. You could implement variations of quicksort that would be very fast in practice.
Inherently all physical systems are infinitely parallel. You might have a buttload of atoms in a grain of sand, nature has enough computational power to figure out where each electron in each atom should be. So if you had enough computational resources (O(n) processors) you could sort n numbers in log(n) time.
From comments:
Given a physical processor that has k number of elements, it can achieve a parallelness of at most O(k). If you process n numbers arbitrarily, it would still process it at a rate related to k. Also, you could formulate this problem physically. You could create n steel balls with weights proportional to the number you want to encode, which could be solved by a centrifuge in a theory. But here the amount of atoms you are using is proportional to n. Whereas in a standard case you have a limited number of atoms in a processor.
Another way to think about this is, say you have a small processor attached to each number and each processor can communicate with its neighbors, you could sort all those numbers in O(log(n)) time.