Computing the fully optimal spanning tree of an ordered bipolar directed graph

TL;DR

This paper presents two methods for computing the unique fully optimal spanning tree of an ordered bipolar directed graph, including a polynomial-time algorithm based on linear programming adaptation.

Contribution

It introduces a novel polynomial-time algorithm for directly computing the fully optimal spanning tree in ordered bipolar digraphs, advancing previous recursive methods.

Findings

First method: deletion/contraction recursion, efficient for full bijection construction.

Second method: linear programming-based algorithm, polynomial time complexity.

Provides a practical approach for computing canonical spanning trees in ordered bipolar graphs.

Abstract

It has been previously shown by the authors that a directed graph on a linearly ordered set of edges (ordered graph) with adjacent unique source and sink (bipolar digraph) has a unique fully optimal spanning tree, that satisfies a simple criterion on fundamental cycle/cocycle directions. This result yields, for any ordered graph, a canonical bijection between bipolar orientations and spanning trees with internal activity 1 and external activity 0 in the sense of the Tutte polynomial. This bijection can be extended to all orientations and all spanning trees, yielding the active bijection, presented for graphs in a companion paper. In this paper, we specifically address the problem of the computation of the fully optimal spanning tree of an ordered bipolar digraph. In contrast with the inverse mapping, built by a straightforward single pass over the edge set, the direct computation is not easy and had previously been left aside. We give two independent constructions. The first one is a deletion/contraction recursion, involving an exponential number of minors. It is structurally significant but it is efficient only for building the whole bijection (i.e. all images) at once. The second one is more complicated and is the main contribution of the paper. It involves just one minor for each edge of the resulting spanning tree, and it is a translation and an adaptation in the case of graphs, in terms of weighted cocycles, of a general geometrical linear programming type algorithm, which allows for a polynomial time complexity.