Difference between neighbors in a matrix (Solved)
Only neighbors in the same row or column are considered. Given a matrix , let be the maximum difference between two neighboring entries of . Given integers , let be the smallest possible value of among all matrices with different integer entries. Thus the conjecture asserts that .
Consider the matrix defined by . The entries of is and . Thus . Consequently the conjecture is equivalent to the assertion .
It can be easily seen that for any matrix with different integer entries, there exists a matrix with the entries such that . Therefore, in the definition of , we may assume that the entries of each matrix is . Consequently the conjecture reduces to the case when the entries of the matrix is .
I have proved the conjecture for . The proof is separate for each of the 4 cases and pretty elementary. However, I am not so sure at the moment whether the conjecture is true for all . If it turns out to be generally false, it would be an interesting problem to evaluate . I wonder if there are any related results.
Bibliography
* indicates original appearance(s) of problem.
Related questions
Dear Daniel, thank you for the very nice proof! Theorem 11 from the lecture notes you refer to suggests some answers to other general questions related to the conjecture (let us now refer to it as Theorem 0). In particular I have the following 2 questions in mind.
1. I wonder how the generalization of Theorem 0 to higher dimensional matrices looks like. More specifically, given integers , what is the smallest value, denoted , of among all -matrices with distinct integer entries? Theorem 0 states that . Theorem 11 implies that where and . My feeling is that . What do you think? Note that is the number of all ordered partitions of into exactly parts not exceeding .
bandwidth
Dear Dr. Lioubimov!
At first sight your feeling seemed plausible, but since then i managed to find the more general problem: The graph bandwidth problem. (see wiki page)
At the wikipedia page, you can see that a classical result of Harper is the bandwidth of the hypercube. Well, if your feeling were true, the bandwidth of the cube would be the middle binomial coefficient, which is not true.
We can see this in a different way too, i'll give some heuristics. Take a look at the proof of the 2 dimensional case: We had a single vertex which had a big label (at least n(n-1)/2+n). And we concluded that since it has at least one vertex with "small" label, we found a big difference. If we only have 2 dimensions, this bound is sharp, because the degrees of the vertices are not too big. In higher dimensionan grids there are more neighbours of this vertex with "small" labell. Thus a better lower bound can be obtained.
Sincerely, Daniel Soltész
Solution
Dear Dr. Lioubimov
Your problem is equivalent to the following: We label the vertces of the nxn grid graph with distinct elements from the set [n^2]. Then there are adjacent vertices such that their labels differ by at least n. I think i found a solution. The key related result is the vertex isoperimetric inequality in the grid. For our problem it is enough to consider the corollary 12 in the following .pdf:
https://www.dpmms.cam.ac.uk/~par31/notes/extcomb.pdf
With parameters: n=2 since we are in a 2 dimensional grid. Let A be the set which have the labels: 1,2,..., n(n-1)/2. Clearly then we can set r=n-1 and with t=1 we obtain that there are at least n neighbors of this set. This finishes our proof since one of these neighbors will have a label of at least n(n-1)/2+n.
Sincerely Daniel Soltész
Related questions: continued
2. What is the smallest number, denoted , to guarantee that a -matrix with the entries has 2 neighboring entries such that and ? Obviously, , where is the smallest number such that any subset of of size has at least neighbors. Due to Theorem 11, is the smallest number such that the initial segment of length in the simplicial order on has neighbors. In your proof you used the fact that , which implies by Theorem 11 that . However this bound is not tight. Consider the hamming ball . It is easy to see that and . Thus . What about higher dimensions?