# Make sets disjoint without emptying them

Suppose you have a set of sets of integers. It's possible that some of the sets will overlap (i.e. sharing elements). You could get rid of the overlaps by deleting elements from the sets, but then some of them might end up empty; that would be a shame. Can we make all the sets disjoint without emptying any of them?

Note that in this situation, there's never any reason to leave multiple elements in a set, so this problem can always be solved by reducing each set to just one element. That's the version of the problem we're solving here.

Write a program or function, as follows:

Input: A list of sets of integers.

Output: A list of integers, of the same length as the input, for which:

• All integers in the output are distinct; and
• Each integer in the output is an element of the corresponding set of the input.

## Clarifications

• You can represent a set as a list if you wish (or whatever's appropriate for your language), disregarding the order of elements.
• You don't have to handle the case where no solution exists (i.e. there will always be at least one solution).
• There might be more than one solution. Your algorithm must always produce a valid solution, but is allowed to be nondeterministic (i.e. it's OK if it picks a different valid solution each time it runs).
• The number of distinct integers appearing in the input, n, will be equal to the number of sets in the input, and for simplicity, will be the integers from 1 to n inclusive (as their actual values don't matter). It's up to you whether you wish to exploit this fact or not.

## Testcases

[{1,2},{1,3},{1,4},{3,4}] -> [2,3,1,4] or [2,1,4,3]
[{1,3},{1,2,4},{2,3},{3},{2,3,4,5}] -> [1,4,2,3,5]
[{1,3,4},{2,3,5},{1,2},{4,5},{4,5}] -> [1,3,2,4,5] or [3,2,1,4,5] or [1,3,2,5,4] or [3,2,1,5,4]


## Victory condition

A program requires an optimal time complexity to win, i.e. if an algorithm with a better time complexity is found, it disqualifies all slower entries. (You can assume that your language's builtins run as fast as possible, e.g. you can assume that a sorting builtin runs in time O(n log n). Likewise, assume that all integers of comparable size to n can be added, multiplied, etc. in constant time.)

Because an optimal time complexity is likely fairly easy to obtain in most languages, the winner will therefore be the shortest program among those with the winning time complexity, measured in bytes.

• If it makes no sense then why is it that they understood it over here dreamincode.net/forums/topic/… Dec 5, 2017 at 13:43
• @fredrussell maybe, just maybe, it's about how you explain it, and not about, e. g. the notation on the image?
– Maya
Dec 5, 2017 at 13:44
• @fredrussell your explanation of the "challenge" is unclear and not formatted. On this site you'll usually find properly formatted and ordered questions, for example following a layout like "Input; Output; Rules; Testcases", but you aren't providing anything of that. Additionally, you don't have a winning criteria which could determine a winner. And after your insult I don't think anyone is willing to solve the question now. Even on SO, you should always keep in mind that the people answering are doing this in their free time, so you should not be rude like that. Dec 5, 2017 at 13:49
• @Arnauld fastest-code would imply that if we both write O(n) algorithms, but yours is 100 times faster, then you win. If we only require an optimal time complexity, then it's okay if my code is 100 times slower, as long as it's one byte smaller. But this challenge might well count as fastest-algorithm. Dec 5, 2017 at 17:32
• This is exactly the problem of finding a full matching in a bipartite graph. The time complexities of the best known algorithms depend on how large the sets are compared to the number of sets. Related challenge. Dec 5, 2017 at 20:38

# Wolfram Language (Mathematica), 87 bytes and ϴ(n3)

-Range[n=Length@#]/.Rule@@@FindIndependentEdgeSet[Join@@Table[-i->j,{i,n},{j,#[[i]]}]]&


Try it online!

Builds a bipartite graph whose vertices on one side are the sets (indexed by -1,-2,...,-n) and whose vertices on the other side are the elements 1,2,...,n, with an edge from -i to j when j is contained in the i-th set. Finds a perfect matching in this graph using a built-in. Then lists out the elements corresponding to -1,-2,...,-n in that order in the perfect matching.

Mathematica's FindIndependentEdgeSet is the bottleneck here; everything else takes O(n2) operations to do. Mathematica probably uses the Hungarian algorithm, and so I'll assume that it runs in ϴ(n3) time, though it's possible that Mathematica has a naive implementation with O(n4) complexity.

• Well... Mathematica is bad at restricted-complexity, not for the first time. Dec 6, 2017 at 1:22

Œp⁼Q$ÐfḢ  Try it online! # Explanation Œp⁼Q$ÐfḢ  Main Link
Œp        Cartesian Product of the elements; all possible lists
Ðf   Filter; keep elements that are
⁼Q\$     Equal to themselves uniquified
Ḣ   Take the first one


Extremely inefficient. Asymptotic to ϴ(n^(n+1)) according to Misha Lavrov; I think it's right.

• I think this is at least Ω(n^n): the maximum possible size of the Cartesian product. Dec 5, 2017 at 17:58
• More precisely, ϴ(n^(n+1)), since the "equal to itself uniquified" check should be ϴ(n) time. Dec 5, 2017 at 18:20
• @MishaLavrov Ah okay, thanks. Dec 5, 2017 at 18:20
• @HyperNeutrino unique function in Jelly use O(n) containment (x in s) check, each one should take O(n) according to this page, so Q should take O(n^2) worst case / average case time complexity. Therefore the algorithm is O(n^(n+2)). (unique can be O(n) in case all elements are equal, where each containment check runs in O(1)) --- On an unrelated note, it is possible to implement unique in O(n) using Python builtin set data structure which is hash-set. Anyway Jelly is not designed to be efficient. Dec 7, 2017 at 12:10

-1 byte thanks to nimi.

import Data.List


Try it online!

• mapM id instead of sequence
– nimi
Dec 5, 2017 at 19:58

## Mathematica 39 Bytes

Last@Union[DeleteDuplicates/@Tuples@#]&


On the issue of complexity, I think it is very dependent upon the length of each sublist, as well as a measure of how disjoint the sublists are.

So, I think this algorithm is O(n Log n + n^2 Log m) where m is roughly the average length of each sublist.

Something like this would have complexity O(a^n) where a>1 is a measure of the overlap in sublists:

(For[x={1,1},!DuplicateFreeQ@x,x=RandomChoice/@#];x)&


Hard to say which is really faster without knowing the properties of possible inputs.

• The DeleteDuplicates /@ Tuples@# step takes ϴ(n^(n+1)) time by the same arguments as in the other solutions. Then Union has a list of length n^n to sort, which takes O(n^(n+1) log(n)) time - but maybe it's faster since at most 2^n n! elements in that list are distinct. Either way, the complexity is ϴ(n^(n+1)) up to a log(n) factor. Dec 5, 2017 at 20:33
• I think this problem requires a multivariate definition of big O notation to have any practical meaning. Obviously the length of the sublists is incredibly important. Dec 5, 2017 at 21:46
• My interpretation of the problem statement is that only the dependence on n (the number of sublists) matters, and we assume the worst case about the length of the sublists. In any case, even if every sublist has length 2, Tuples@# has size 2^n, so your first asymptotic estimate can't possibly be true. Dec 5, 2017 at 21:57
• Huh? Why is multivariate BigO problematic? It's done all the time. Dec 6, 2017 at 1:21

# 05AB1E, 13 bytes, O(n!*n)

āœʒ‚øεQO}P}н


Try it online! Explanation:

ā               Make a range 1..n
œ              Generate all permutations
ʒ        }    Filter
‚ø           Zip with input
ε   }      Loop over sets
QO       Check each element for membership
P     All sets must match
н   Take the first result


# Husk, 5 bytes

►oLuΠ


Try it online!

### Explanation

Just saw the thing about complexity: As so often it is the case with golfing languages' solution they are not very efficient - this one has complexity O(n·nⁿ).

►(Lu)Π  -- input as a list of lists, for example: [[1,2],[1,3],[1,4],[3,4]]
Π  -- cartesian product: [[1,1,1,3],...,[2,3,4,4]]
►(  )   -- maximum by the following function (eg. on [1,1,1,3]):
u    --   deduplicate: [1,3]
L     --   length: 2


# Pyth, 9 bytes (ϴ(nn+1))

Since this works exactly like the Jelly solution, it most likely has the same complexity.

h{I#.nM*F


Try it here!

## How?

h{I#.nM*F | Full program.

*F | Fold Cartesian product.
.nM   | Flatten each.
#      | Keep those:
{I         That are invariant over duplicate elements removal.
h         | Take the first element.


# JavaScript (ES6), 74 73 bytes

1 byte saved, thanks to @Neil.

f=([a,...A],s=[],S)=>a?a.map(c=>s.includes(c)||(S=S||f(A,[...s,c])))&&S:s


Recursively iterates through the array looking for a solution.

Ungolfed:

f=(
[a, ...A],                        //a is the first array, A is the rest
s = [],                           //s is the current trial solution
S                                 //S holds the solution (if one exists) at this branch
)=>
a ? a.map(                      //if we're not done, iterate through a
c => s.includes(c) ||     //  if this element already exists, short-circuit
(S = S ||            //  else if S has a solution, keep it
f(A, [...s, c])     //  else look for a solution down this branch
)
) && S                      //return S


Test Cases:

f=([a,...A],s=[],S)=>a?a.map(c=>s.includes(c)||(S=S||f(A,[...s,c])))&&S:s

console.log(f([[1,2],[1,3],[1,4],[3,4]])) // [2,3,1,4] or [2,1,4,3]
console.log(f([[1,3],[1,2,4],[2,3],,[2,3,4,5]])) // [1,4,2,3,5]
console.log(f([[1,3,4],[2,3,5],[1,2],[4,5],[4,5]])) // [1,3,2,4,5] or [3,2,1,4,5] or [1,3,2,5,4] or [3,2,1,5,4],4]

• Why not a?a.map(...)&&S:s?
– Neil
Dec 6, 2017 at 15:44
• @Neil, because Duh. Thanks! Dec 6, 2017 at 16:01

# Python3, 93 84 bytes

-9 bytes thanks to caird coinheringaahing

lambda I:next(filter(lambda x:len(x)==len({*x}),product(*I)))
from itertools import*


Try it online!

• 84 bytes Dec 6, 2017 at 15:04