I have a set M which consists of three subsets A,B and C.
Problem: I would like to calculate all possible subsets S(1)...S(N) of M which contain all possible pairs between elements of A, B and C in such manner that:
elements of A and B can happen in a pair only once for each of two positions in a pair (that is {a1,a2} and {b1,a1} can be in one subset S, but no more elements {a1,_} and {_,a1} are allowed in this subset S);
elements of C can happen 1-N times in a subset S (that is {a,c}, {b,c}, {x,c} can happen in one subset S), but I would like to get subsets S for all possible numbers of elements of C in a subset S.
For example, if we have A = [a1,a2], B = [b1,b2], C = [c1,c2], then some of the resulting subsets S would be (remember, they should contain pairs of elements):
- {a1,b1}, {b1,a2}, {a2,b2}, {b2,c1};
- {a1,b1}, {b1,a2}, {a2,b2}, {b2,c1}, {c1,c2};
- {a1,c1}, {c1,a2}, {c1,b2}, {b1,c1};
- etc.
I tend to think that first I need to find all possible subsets of M, which contain only one element of A, one element of B and 1..N elements of C (1). And after that I should somehow generate sets of pairs (2) from that. But I am not sure that this is the right strategy.
So, the more elaborated question would be:
what is the best way to create sets and find subsets in Erlang if the elements of the set M a integers?
are there any ready-made tools to find subsets of a set in Erlang?
are there any ready-made tools to generate all possible pairs of elements of a set in Erlang?
How can I solve the aforementioned problem in Erlang?
There is a sets module*, but I suspect you're better off thinking up an algorithm first -- its implementation in Erlang is the problem (or not) that comes after this. (Maybe you notice its actually a graph algorithm (like, bipartite matching something something), and you'll get happy with Erlang's digraph module.)
Long story short, when you come up with an algorithm, Erlang can very probably be used to implement it. Yes, there is a certain support for sets. But solutions to a problem requiring "all possible subsets" tend to be exponential (i.e., given n elements, there are 2^n subsets; for every element you either have it in your subset or not) and thus bad.
(* there are some modules concerning sets)
Related
I'm trying to find an algorithm to the following problem.
Say I have a number of objects A, B, C,...
I have a list of valid combinations of these objects. Each combination is of length 2 or 4.
For eg. AF, CE, CEGH, ADFG,... and so on.
For combinations of two objects, eg. AF, the length of the combination is 2. For combination of four objects, eg CEGH, the length of the combination.
I can only pick non-overlapping combinations, i.e. I cannot pick AF and ADFG because both require objects 'A' and 'F'. I can pick combinations AF and CEGH because they do not require common objects.
If my solution consists of only the two combinations AF and CEGH, then my objective is the sum of the length of the combinations, which is 2 + 4 = 6.
Given a list of objects and their valid combinations, how do I pick the most valid combinations that don't overlap with each other so that I maximize the sum of the lengths of the combinations? I do not want to formulate it as an IP as I am working with a problem instance with 180 objects and 10 million valid combinations and solving an IP using CPLEX is prohibitively slow. Looking for some other elegant way to solve it. Can I perhaps convert this to a network? And solve it using a max-flow algorithm? Or a Dynamic program? Stuck as to how to go about solving this problem.
My first attempt at showing this problem to be NP-hard was wrong, as it did not take into account the fact that only combinations of size 2 or 4 were allowed. However, using Jim D.'s suggestion to reduce from 3-dimensional matching (3DM), we can show that the problem is nevertheless NP-hard.
I'll show that the natural decision problem form of your problem ("Given a set O of objects, and a set C of combinations of either 2 or 4 objects from O, and an integer m, does there exist a subset D of C such that all sets in D are pairwise disjoint, and the union of all sets in D has size at least m?") is NP-hard. Clearly the optimisation problem (i.e., your original problem, where we seek an actual subset of combinations that maximises m above) is at least as hard as this problem. (To see that the optimisation problem is not "much" harder than the decision problem, notice that you could first find the maximum m value for which a solution exists using a binary search on m in which you solve a decision problem at each step, and then, once this maximal m value has been found, solving a series of decision problems in which each combination in turn is removed: if the solution after removing some particular combination is still "YES", then it may also be left out of all future problem instances, while if the solution becomes "NO", then it is necessary to keep this combination in the solution.)
Given an instance (X, Y, Z, T, k) of 3DM, where X, Y and Z are sets that are pairwise disjoint from each other, T is a subset of X*Y*Z (i.e., a set of ordered triples with first, second and third components from X, Y and Z, respectively) and k is an integer, our task is to determine whether there is any subset U of T such that |U| >= k and all triples in U are pairwise disjoint (i.e., to answer the question, "Are there at least k non-overlapping triples in T?"). To turn any such instance of 3DM into an instance of your problem, all we need to do is create a fresh 4-combination from each triple in T, by adding a distinct dummy value to each. The set of objects in the constructed instance of your problem will consist of the union of X, Y, Z, and the |T| dummy values we created. Finally, set m to k.
Suppose that the answer to the original 3DM instance is "YES", i.e., there are at least k non-overlapping triples in T. Then each of the k triples in such a solution corresponds to a 4-combination in the input C to your problem, and no two of these 4-combinations overlap, since by construction, their 4th elements are all distinct, and by assumption of the. Thus there are at least m = k non-overlapping 4-combinations in the instance of your problem, so the solution for that problem must also be "YES".
In the other direction, suppose that the solution to the constructed instance of your problem is "YES", i.e., there are at least m non-overlapping 4-combinations in C. We can simply take the first 3 elements of each of the 4-combinations (throwing away the fourth) to produce a set of k = m non-overlapping triples in T, so the answer to the original 3DM instance must also be "YES".
We have shown that a YES-answer to one problem implies a YES-answer to the other, thus a NO-answer to one problem implies a NO-answer to the other. Thus the problems are equivalent. The instance of your problem can clearly be constructed in polynomial time and space. It follows that your problem is NP-hard.
You can reduce this problem to the maximum weighted clique problem, which is, unfortunately, NP-hard.
Build a graph such that every combination is a vertex with weight equal to the length of the combination, and connect vertices if the corresponding combinations do not share any object (i.e. if you can pick both them at the same time). Then, a solution is valid if and only if it is a clique in that graph.
A simple search on google brings up a lot of approximation algorithms for this problem, such as this one.
Lets say I have N lists which are known. Each list has items, which may repeat (Not a set)
eg:
{A,A,B,C}, {A,B,C}, {B,B,B,C,C}
I need some algorithm (Some machine-learning one maybe?) which answers the following question:
Given a new & unknown partial list of items, for example, {A,B}, what is the probability that C will appear in the list based on the what I know from the previous lists. If possible, I would like a more fine-grained probability of: given some partial list L, what is the probability that C will appear in the list once, probability it will appear twice, etc... Order doesn't matter. The probability of C appearing twice in {A,B} should equal it appearing twice in {B,A}
Any algorithms which can do this?
This is just pure mathematics, no actual "algorithms", simply estimate all the probabilities from your dataset (literally count the occurences). In particular you can do very simple data structure to achieve your goal. Represent each "list" as bag of letters, thus:
{A,A,B,C} -> {A:2, B:1, C:1}
{A,B} -> {A:1, B:1}
etc. and create basic reverse indexing of some sort, for example keep indexes for each letter separately, sorted by their counts.
Now, when a query comes, like {A,B} + C all you do is you search for your data that contains at least 1 A and 1 B (using your indexes), and then estimate probability by computing the fraction of retrived results containing C (or exactly one C) vs. all retrived results (this is a valid probability estimate assuming that your data is a bunch of independent samples from some underlying data-generating distribution).
Alternatively, if your alphabet is very small you can actually precompute all the values P(C|{A,B}) etc. for all combinations of letters.
I'm doing some comp. mathematics work where I'm trying to sort a sequence with a complex mathematical sorting predicate, which isn't always defined between two elements in the sequence. I'm trying to learn more about sorting algorithms that gracefully handle element-wise comparisons that cannot be made, as I've only managed a very rudimentary approach so far.
My apologies if this question is some classical problem and it takes me some time to define it, algorithmic design isn't my strong suit.
Defining the problem
Suppose I have a sequence A = {a, b, c, d, e}. Let's define f(x,y) to be a binary function which returns 0 if x < y and 1 if y <= x, by applying some complex sorting criteria.
Under normal conditions, this would provide enough detail for us to sort A. However, f can also return -1, if the sorting criteria is not well-defined for that particular pair of inputs. The undefined-ness of a pair of inputs is commutative, i.e. f(q,r) is undefined if and only if f(r,q) is undefined.
I want to try to sort the sequence A if possible with the sorting criterion that are well defined.
For instance let's suppose that
f(a,d) = f(d,a) is undefined.
All other input pairs to f are well defined.
Then despite not knowing the inequality relation between a and d, we will be able to sort A based on the well-defined sorting criteria as long as a and d are not adjacent to one another in the resulting "sorted" sequence.
For instance, suppose we first determined the relative sorting of A - {d} to be {c, a, b, e}, as all of those pairs to fare well-defined. This could invoke any sorting algorithm, really.
Then we might call f(d,c), and
if d < c we are done - the sorted sequence is indeed {d, c, a, b, e}.
Else, we move to the next element in the sequence, and try to call f(a, d). This is undefined, so we cannot establish d's position from this angle.
We then call f(d, e), and move from right to left element-wise.
If we find some element x where d > x, we are done.
If we end up back at comparing f(a, d) once again, we have established that we cannot sort our sequence based on the well-defined sorting criterion we have.
The question
Is there a classification for these kinds of sorting algorithms, which handle undefined comparison pairs?
Better yet although not expected, is there a well-known "efficient" approach? I have defined my own extremely rudimentary brute-force algorithm which solves this problem, but I am certain it is not ideal.
It effectively just throws out all sequence elements which cannot be compared when encountered, and sorts the remaining subsequence if any elements remain, before exhaustively attempting to place all of the sequence elements which are not comparable to all other elements into the sorted subsequence.
Simply a path on which to do further research into this topic would be great - I lack experience with algorithms and consequently have struggled to find out where I should be looking for some more background on these sorts of problems.
This is very close to topological sorting, with your binary relation being edges. In particular, this is just extending a partial order into a total order. Naively if you consider all pairs using toposort (which is O(V+E)) you have a worst case O(n^2) algorithm (actually O(n+p) with n being the number of elements and p the number of comparable pairs).
Suppose we have a finite set S and a list of subsets of S. Then, the set packing problem asks if some k subsets in the list are pairwise disjoint .
The optimization version of the problem, maximum set packing, asks for the maximum number of pairwise disjoint sets in the list.
http://en.wikipedia.org/wiki/Set_packing
So, Let S = {1,2,3,4,5,6,7,8,9,10}
and `Sa = {1,2,3,4}`
and `Sb = {4,5,6}`
and `Sc = {5,6,7,8}`
and `Sd = {9,10}`
Then the maximum number of pairwise disjoint sets are 3 ( Sa, Sc, Sd )
I could not find any articles about the algorithm involved. Can you shed some light on the same?
My approach:
Sort the sets according to the size. Start from the set of the smallest size. If no element of the next set intersects with the current set, then we unite the set and increase the count of maximum sets. Does this sound good to you? Any better ideas?
As hivert pointed out, this problem is NP-hard, so there's no efficient way to do this. However, if your input is relatively small, you can still pull it off. Exponential doesn't mean impossible, after all. It's just that exponential problems become impractical very quickly, as the input size grows. But for something like 25 sets, you can easily brute force it.
Here's one approach. Let's say you have n subsets, called S0, S1, ..., etc. We can try every combination of subsets, and pick the one with maximum cardinality. There are only 2^25 = 33554432 choices, so this is probably reasonable enough.
An easy way to do this is to notice that any non-negative number strictly below 2^N represents a particular choice of subsets. Look at the binary representation of the number, and choose the sets whose indices correspond to the bits that are on. So if the number is 11, the 0th, 1st and 3rd bits are on, and this corresponds to the combination [S0, S1, S3]. Then you just verify that these three sets are in fact disjoint.
Your procedure is as follows:
Iterate i from 0 to 2^N - 1
For each value of i, use the bits that are on to figure out the corresponding combination of subsets.
If those subsets are pairwise disjoint, update your best answer with this combination (i.e., use this if it is bigger than your current best).
Alternatively, use backtracking to generate your subsets. The two approaches are equivalent, modulo implementation tradeoffs. Backtracking will have some stack overhead, but can cut off entire lines of computation if you check disjointness as you go. For example, if S1 and S2 are not disjoint, then it will never bother with any bigger combinations containing those two, saving some time. The iterative method can't optimize itself in this way, but is fast and efficient because of the bitwise operations and tight loop.
The only nontrivial matter here is how to check if the subsets are pairwise disjoint. There are all sorts of tricks you can pull here as well, depending on the constraints.
A simple approach is to start with an empty set structure (pick whatever you want from the language of your choice) and add elements from each subset one by one. If you ever hit an element that's already in the set, then it occurs in at least two subsets, and you can give up on this combination.
If the original set S has m elements, and m is relatively small, you can map each of them to the range [0, m-1] and use bitmasks for each set. So if m <= 64, you can use a Java long to represent each subset. Turn on all the bits that correspond to the elements in the subset. This allows blazing fast set operation, because of the speed of bitwise operations. Bitwise AND corresponds to set intersection, and bitwise OR is a union. You can check if two subsets are disjoint by seeing if the intersection is empty (i.e., ANDing the two bitmasks gives you 0).
If you don't have so few elements, you can still avoid repeating the set intersections multiple times. You have very few sets, so precompute which ones are disjoint at the start. You can just store a boolean matrix D, such that D[i][j] = true iff i and j are disjoint. Then you just look up all pairs in a combination to verify pairwise disjointness, rather than doing real set operations.
You can solve the set packing problem searching a Maximum independent set. You encode your problem as follows:
for each set you put a vertex
you put an edge between two vertex if they share a common number.
Then you wan't a maximum set of vertex without two having two related vertex. Unfortunately this is a NP-Hard problem. Any know algorithm is exponential.
Let's say I have a number of 'known' sets:
1 {a, b, c, d, e}
2 {b, c, d, e}
3 {a, c, d}
4 {c, d}
I'd like a function which takes a set as an input, (for example {a, c, d, e}) and finds the set that has the highest number of elements, and no more other items in common. In other words, the subset with the greatest cardinality. The answer doesn't have to be a proper subset. The answer in this case would be {a, c, d}.
EDIT: the above example was wrong, now fixed.
I'm trying to find the absolute most efficient way of doing this.
(In the below, I am assuming that the cost of comparing two sets is O(1) for the sake of simplicity. That operation is outside my control so there's no point thinking about it. In truth it would be a function of the cardinality of the two sets being compared.)
Candiate 1:
Generate all subsets of the input, then iterate over the known sets and return the largest one that is a subset. The downside to this is that the complexity will be something like O(n! × m), where n is the cardinality of the input set and m is the number of 'known' subsets.
Candidate 1a (thanks #bratbrat):
Iterate over all 'known' sets and calculate the cardinatlity of the intersection, and take the one with the highest value. This would be O(n) where n is the number of subsets.
Candidate 2:
Create an inverse table and calculate the euclidean distance between the input and the known sets. This could be quite quick. I'm not clear how I could limit this to include only subsets without a subsequent O(n) filter.
Candidate 3:
Iterate over all known sets and compare against the input. The complexity would be O(n) where n is the number of known sets.
I have at my disposal the set functions built into Python and Redis.
None of these seems particularly great. Ideas? The number of sets may get large (around 100,000 at a guess).
There's no possible way to do this in less than O(n) time... just reading the input is O(n).
A couple ideas:
Sort the sets by size (biggest first), and search for the first set which is a subset of the input set. Once you find one, you don't have to examine the rest.
If the number of possible items which could be in the sets is limited, you could represent them by bit-vectors. Then you could calculate a lookup table to tell you whether a given set is a subset of the input set. (Walk down the bits for each input set under consideration, word by word, indexing each word into the appropriate table. If you find an entry telling you that it's not a subset, again, you can move on directly to the next input set.) Whether this would actually buy you performance, depends on the implementation language. I imagine it would be most effective in a language with primitive integral types, like C or Java.
Take the union of the known sets. This becomes a dictionary of known elements.
Sort the known elements by their value (they're integers, right). This defines a given integer's position in a bit string.
Use the above to define bit strings for each of the known sets. This is a one time operation - the results should be stored to avoid recomputation.
For an input set, run it through the same transform to obtain its bit string.
To get the largest subset, run through the list of known bit strings, taking the intersection (logical and) with the input bit string. Count the '1' elements. Remember the largest one.
http://packages.python.org/bitstring
As mentioned in the comments, this can be paralleled up by subdividing the known sets and giving each thread its own subset to work on. Each thread serves up its best match and then the parent thread picks the best from the threads.
How many searches are you making? In case you are searching multiple input sets you should be able to pre-process all the known sets (perhaps as a tree structure) and your search time for each query would be in the order of your query set size.
Eg: Create a Trie structure with all the known sets. Make sure to sort each set before inserting them. For the query, follow the links that are in the set.