A Weight Function Lemma Heuristic for Graph Pebbling

TL;DR

This paper introduces a heuristic for graph pebbling that improves upper bounds on pebbling numbers by strategically distributing weights, especially focusing on the farthest vertices, and demonstrates its effectiveness on specific graph classes.

Contribution

The paper proposes a novel heuristic method for the Weight Function Lemma in graph pebbling, enhancing bounds by prioritizing weight distribution to distant vertices, filling a gap in the formal framework.

Findings

Improved upper bounds on pebbling numbers for Flower and Blanuša snarks.

Heuristic effectively balances weight distribution, reducing surplus weight.

Theoretical analysis highlights the importance of farthest vertices in strategy optimization.

Abstract

Graph pebbling is a problem in which pebbles are distributed across the vertices of a graph and moved according to a specific rule: two pebbles are removed from a vertex to place one on an adjacent vertex. The goal is to determine the minimum number of pebbles required to ensure that any target vertex can be reached, known as the pebbling number. Computing the pebbling number lies beyond NP in the polynomial hierarchy, leading to bounding methods. One of the most prominent techniques for upper bounds is the Weight Function Lemma (WFL), which relies on costly integer linear optimization. To mitigate this cost, an alternative approach is to consider the dual formulation of the problem, which allows solutions to be constructed by hand through the selection of strategies given by subtrees with associated weight functions. To improve the bounds, the weights should be distributed as uniformly as possible among the vertices, balancing their individual contribution. However, despite its simplicity, this approach lacks a formal framework. To fill this gap, we introduce a novel heuristic method that refines the selection of balanced strategies. The method is motivated by our theoretical analysis of the limitations of the dual approach, in which we prove lower bounds on the best bounds achievable. Our theoretical analysis shows that the bottleneck lies in the farthest vertices from the target, forcing surplus weight onto the closer neighborhoods. To minimize surplus weight beyond the theoretical minimum, our proposed heuristic prioritizes weight assignment to the farthest vertices, building the subtrees starting from the shortest paths to them and then filling in the weights for the remaining vertices. Applying our heuristic to Flower snarks and Blanu\v{s}a snarks, we improve the best-known upper bounds, demonstrating the effectiveness of a structured strategy selection when using the WFL.