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Support of extremal doubly stochastic arrays

Mark Mordechai Etkind, Nir Lev

TL;DR

This work completely determines the minimal support size of $n\times m$ doubly stochastic arrays as $n+m-\gcd(n,m)$ and shows that, when $\gcd(n,m)=1$, all extremal arrays have the minimal possible support $n+m-1$. For the case $m=kn+1$, it provides a complete, checkable structure theorem: a subset $\Omega$ is the support of an extremal array iff each row has exactly $k+1$ nonzeros and $\Omega$ contains no cycles, with a constructive proof via a tree-based weight assignment. The paper also characterizes extremal arrays with non-minimal support sizes (showing all sizes $n+m-s$ with $1\le s\le \gcd(n,m)$ occur) and, in the $m=n+1$ case, yields a precise enumeration of extremal arrays and a correspondence with labeled trees, leading to the existence of non-equivalent extremals sharing the same multiset of entries for $n\ge 6$. These results bridge combinatorial graph methods, matrix extremality, and tiling interpretations in abelian groups, with implications for joint distributions and transportation-type problems.

Abstract

An $n \times m$ array with nonnegative entries is called doubly stochastic if the sum of its entries at each row is $m$ and at each column is $n$. The set of all $n \times m$ doubly stochastic arrays is a convex polytope with finitely many extremal points. The main result of this paper characterizes the possible sizes of the supports of all extremal $n \times m$ doubly stochastic arrays. In particular we prove that the minimal size of the support of an $n \times m$ doubly stochastic array is $n + m - \gcd(n,m)$. Moreover, for $m=kn+1$ we also characterize the structure of the support of the extremal arrays.

Support of extremal doubly stochastic arrays

TL;DR

This work completely determines the minimal support size of doubly stochastic arrays as and shows that, when , all extremal arrays have the minimal possible support . For the case , it provides a complete, checkable structure theorem: a subset is the support of an extremal array iff each row has exactly nonzeros and contains no cycles, with a constructive proof via a tree-based weight assignment. The paper also characterizes extremal arrays with non-minimal support sizes (showing all sizes with occur) and, in the case, yields a precise enumeration of extremal arrays and a correspondence with labeled trees, leading to the existence of non-equivalent extremals sharing the same multiset of entries for . These results bridge combinatorial graph methods, matrix extremality, and tiling interpretations in abelian groups, with implications for joint distributions and transportation-type problems.

Abstract

An array with nonnegative entries is called doubly stochastic if the sum of its entries at each row is and at each column is . The set of all doubly stochastic arrays is a convex polytope with finitely many extremal points. The main result of this paper characterizes the possible sizes of the supports of all extremal doubly stochastic arrays. In particular we prove that the minimal size of the support of an doubly stochastic array is . Moreover, for we also characterize the structure of the support of the extremal arrays.
Paper Structure (23 sections, 20 theorems, 43 equations, 6 figures)

This paper contains 23 sections, 20 theorems, 43 equations, 6 figures.

Key Result

Theorem 1.1

For all $n,m$ the minimal size of the support of an array $A \in \mathcal{S}_{n,m}$ is equal to $n + m - \gcd(n,m)$.

Figures (6)

  • Figure 2.1: The graphs associated to the two matrices in \ref{['eqDS1.3']}.
  • Figure 3.1: The discrete trapezoid function for $n=5$, $m=7$.
  • Figure 4.1: Graph prototypes of type I (left) and type II (right).
  • Figure 4.2: Graph prototypes of type III (left) and type IV (right).
  • Figure 6.1: An example of two trees on $7$ vertices which correspond to two non-equivalent extremal $6 \times 7$ arrays with the same multiset of entries.
  • ...and 1 more figures

Theorems & Definitions (31)

  • Theorem 1.1
  • Theorem 1.2
  • Theorem 1.3
  • Theorem 1.4
  • Theorem 1.5
  • Theorem 1.6
  • Proposition 2.1: see Li96
  • Proposition 2.2: see Li96
  • Proposition 2.3: see Car96
  • Proposition 2.4
  • ...and 21 more