Tag Archives: Kth Largest m-Tuple

Kth Largest m-Tuple

I think I’m going to move the posts to Wednesday this semester since I teach two 2-hour classes on Tuesday/Thursday.

SP19 is Minimum Sum of Squares

SP20 is Kth Largest Subset and is very similar to our next problem.

The problem: Kth Largest m-Tuple.  This is problem SP21 in the appendix.

The description: Given sets X1 through Xm that all contain positive integers, and integers K and B, are there at least K m-tuples (x1, .., xm) from X1 x X2 x … x Xm for which the sum of the elements in each tuple is at least B?

Example: Let X1 = {1,2,3,4}, X2 = {5,10}, X3 = {1,2}. Notice that the X’s can be different sizes, and can repeat elements.

If B = 15, then the only tuples that sum to at least 15 are {3.10,2), (4,10,1), and  (4,10,2).

Reduction: The paper by Johnson and Mizoguchi presents the reduction pretty densely, but here is what I think they are saying:  We’re going to use a Turing Reduction to solve the Partition problem, which means we’re given an instance of Partition, and assume we can call a subroutine to solve the Kth Largest m-tuple problem multiple times to solve the Partition instance.  Recall that we can use Binary Search on multiple calls of the subroutine to determine (for example) how many tuples sum to some B or more.  (We need multiple calls because the subroutine is a boolean one, just saying yes or no to a given instance).

Updated reduction:

This idea came from Said D. in the comments, and I wanted to make sure it got posted here because it’s so simple and elegant.  If we are given a partition instance S = {s1..sn} and need to know if a subset sums to B (= half the sum of all of the elements in S), then the sets we create for the Kth Largest m-tuple instance are:

{s1+1, 1}, {s2+1, 1}, …(sn+1, 1}

And we number the K-th Largest m-tuple is looking for is B+n.  Each set will contribute 1 to the tuple sum no matter which element is chosen.  It may also contribute si as well, corresponding to whether we “take” that element in the partition instance or not.

That’s way better than what I was trying to do.  Thanks, Said!

The rest of my not very good reduction:

I’ll leave the rest up here for posterity, but this is way worse than Said’s idea above, and glosses over the fact that you can’t just use any elements you want as the sets- you need to set them up in a way that makes sure you don’t repeat elements when you choose the tuples.

So, in a manner similar to that used in the Kth Largest Subset reduction, we can find the number of tuples that sum to any B we want (including half the sum of all of the elements in the partition instance).  The only tricky part is that our “subsets” are tuples of different sizes.  So we need to run this algorithm multiple times:

  • Are there are 1-tuples that solve the partition problem?  (There are O(n)  1-tuples, so a binary search requires O(log n) calls)
  • Are there any 2 tuples that solve the partition problem?  (There are O(N^2) 2-tuples, so a binary search requires O(2* log n) calls)
  • Are there any m-tuples that solve the partition problem? (There are O(N!) n-tuples, so a binary search requires O(n * log n) calls)

Thus, we can do it in a polynomial number of calls to the K-th Largest m-tuple solver.

Difficulty: This is a little harder than the Kth largest subset reduction, which I gave a 5, so I’ll make this a 6.