This is based on an article I read about puzzles and interview questions asked by large software companies, but it has a twist...
General question:
What is an algorithm to seat people in a movie theater so that they sit directly beside their friends but not beside their enemies.
Technical question:
Given an N by M grid, fill the grid with N * M - 1 items. Each item has an association Boolean value for each of the other N * M - 2 items. In each row of N, items directly beside other items should have a positive association value for the other. Columns however do not matter, i.e. an item can be "enemies" with the item in front of it. Note: If item A has a positive association value for B, then that means B also has a positive association value for A. It works the same for negative association values. An item is guarenteed to have a positive association with atleast one other item. Also, you have access to all of the items and their association values before you start placing them in the grid.
Comments:
I have been researching this problem and thinking about it since yesterday, from what I have found it reminds me of the bin packing problem with some added requirements. In some free time I attempted to implement it, but large groups of "enemies" were sitting next to each other. I am sure that most situations will have to have atleast one pair of enemies sitting next to each other, but my solution was far from optimal. It actually looked as if I had just randomized it.
As far as my implementation went, I made N = 10, M = 10, the number of items = 99, and had an array of size 99 for EACH item that had a randomized Boolean value that referred to the friendship of the corresponding item number. This means that each item had a friendship value that corresponded with their self as well, I just ignored that value.
I plan on trying to reimplement this again later and I will post the code. Can anyone figure out a "good" way to do this to minimize seating clashes between enemies?
Theater seating is a style of commonly used event layout, comprised of chairs aligned in consecutive straight rows, generally facing a single direction. It is sometimes called stadium seating or auditorium seating.
Seats in a theatre are typically numbered from aisle to wall, or, in the case of the center sections, from left to right. Seat numbering is also typically Odd numbers on the left, Even numbers on the Right, and sequentially in the 100's in the center.
While the comfort-wise, the middle row may be better equipped, as experts say its best to rest on the front. Groupon recommends a theater row near the center that seats only four people, or about one-third back of the other row.
Some movie theaters have more than one screen, which means they can show more than one movie at a time. In most auditoriums, the seating capacity is 150 people, with large ones and medium ones having even more. A large auditorium in a modern multiplex in a city typically seats between 150 and 250 people.
Most Broadway theaters have three sections, namely, orchestra, front mezzanine, and rear mezzanine. There are some smaller theaters, like the August Wilson, that have only two, orchestra and mezzanine. There are box seats on either side of most theaters too for patrons looking for a private theater viewing experience.
This problem is NP-Hard.
Define L={(G,n,m)|there is a legal seating for G in m×m matrix,(u,v) in E if u is friend of v}
L is a formal definition of this problem as a language.
Proof:
We will show Hamiltonian-Problem ≤ (p) 2-path ≤ (p) This-problem in 2 steps [Hamiltonian and 2-path defined below], and thus we conclude this problem is NP-Hard.
(1) We will show that finding two paths covering all vertices without using any vertex twice is NP-Hard [let's call such a path: 2-path and this problem as 2-path problem]
A reduction from Hamiltonian Path problem:
input: a graph G=(V,E)
Output: a graph G'=(V',E) where V' = V U {u₀}.
Correctness:
Thus: G has Hamiltonian path 1 ⇔ G' has 2-path, and thus: 2-path problem is NP-Hard.
(2)We will now show that our problem [L] is also NP-Hard:
We will show a reduction from the 2-path problem, defined above.
input: a graph G=(V,E)
output: (G,|V|+1,1) [a long row with |V|+1 sits].
Correctness:
Conclusion: This problem is NP-Hard, so there is not known polynomial solution for it.
Exponential solution: You might want to use backtracking solution: which is basically: create all subsets of E with size |V|-2 or less, check which is best.
static best <- infinity
least_enemies(G,used):
if |used| <= |V|-2:
val <- evaluate(used)
best <- min(best,val)
if |used| == |V|-2:
return
for each edge e in E-used: //E without used
least_enemies(G,used + e)
in here we assume evaluate(used) gives the 'score' for this solution. if this solution is completely illegal [i.e. a vertex appear twice], evaluate(used)=infinity
. an optimization can of course be made, trimming these cases. to get the actual sitting we can store the currently best solution.
(*)There are probably better solutions, this is just a simple possible solution to start with, the main aim in this answer is proving this problem is NP-Hard.
EDIT: simpler solution:
Create a graph G'=(V U { u₀ } ,E U {(u₀,v),(v,u₀) | for each v in V})
[u₀ is a junk vertex for the buffer] and a weight function for edges:
w((u,v)) = 1 u is friend of v
w((u,v)) = 2 u is an enemy v
w((u0,v)) = w ((v,u0)) = 0
Now you got yourself a classic TSP, which can be solved in O(|V|^2 * 2^|V|)
using dynamic programming.
Note that this solution [using TSP] is for one lined theatre, but it might be a good lead to find a solution for the general case.
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