Algorithm - find the minimal subtraction between sum of two arrays

Question

I am hunting job now and doing many algorithm exercises. Here is my problem:

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Given two arrays: a and b with same length, the subject is to make |sum(a)-sum(b)| minimal, by swapping elements between a and b.

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Here is my though:

assume we swap a[i] and b[j], set Delt = sum(a) - sum(b), x = a[i]-b[j]
then Delt2 = sum(a)-a[i]+b[j] - (sum(b)-b[j]+a[i]) = Delt - 2*x,
then the change = |Delt| - |Delt2|, which is proportional to |Delt|^2 - |Delt2|^2 = 4*x*(Delt-x),

Based on the thought above I got the following code:

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Delt = sum(a) - sum(b);
done = false;
while(!done)
{
    done = true;
    for i = [0, n)
    { 
        for j = [0,n)
        {
            x = a[i]-b[j];
            change = x*(Delt-x);
            if(change >0)
            {
                 swap(a[i], b[j]);
                 Delt = Delt - 2*x;
                 done = false;
            }
        }
    }
}

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However, does anybody have a much better solution ? If you got, please tell me and I would be very grateful of you!

Answer 1

This problem is basically the optimization problem for Partition Problem with an extra constraint of equal parts. I'll prove that adding this constraint doesn't make the problem easier.

NP-Hardness proof:
Assume there was an algorithm A that solves this problem in polynomial time, we can solve the Partition-Problem in polynomial time.

Partition(S):
for i in range(|S|):
   S += {0}
   result <- A(S\2,S\2) //arbitrary split S into 2 parts
   if result is a partition: //simple to check, since partition is NP.
     return true.
return false //no partition

Correctness:
If there is a partition denote as (S1,S2) [assume S2 has more elements], on iteration |S2|-|S1| [i.e. when adding |S2|-|S1| zeros]. The input to A will contatin enough zeros so we can return two equal length arrays: S2,S1+{0,0,...,0}, which will be a partition to S, and the algorithm will yield true.
If the algorithm yields true, and iteration k, we had two arrays: S2,S1, with same number of elements, and equal values. by removing k zeros from the arrays, we get a partition to the original S, so S had a partition.

Polynomial:
assume A takes P(n) time, the algorithm we produced will take n*P(n) time, which is also polynomial.

Conclusion:
If this problem is solveable in polynomial time, so does the Partion-Problem, and thus P=NP. based on this: this problem is NP-Hard.

Because this problem is NP-Hard, for an exact solution you will probably need an exponential algorith. One of those is simple backtracking [I leave it as an exercise to the reader to implement a backtracking solution]

EDIT: as mentioned by @jpalecek: by simply creating a reduction: S->S+(0,0,...,0) [k times 0], one can directly prove NP-Hardness by reduction. polynomial is trivial and correctness is very similar to the above partion's correctness proof: [if there is a partition, adding 'balancing' zeros is possible; the other direction is simply trimming those zeros]