Gradation in Greyscales of Graphs

TL;DR

This paper introduces the concept of graph greyscales and gradation vectors, providing polynomial algorithms to find minimal gradation vectors, with potential applications in engineering, physics, and applied mathematics.

Contribution

It defines the minimum gradation vector as a new graph invariant and offers polynomial algorithms to compute it and all corresponding greyscales, addressing a novel vectorial optimization problem.

Findings

Polynomial algorithms for minimum gradation vector

All greyscales producing the minimum vector are computable

Potential applications in various scientific fields

Abstract

In this work we present the notion of greyscale of a graph as a colouring of its vertices that uses colours from the real interval [0,1]. Any greyscale induces another colouring by assigning to each edge the non-negative difference between the colours of its vertices. These edge colours are ordered in lexicographical decreasing ordering and gives rise to a new element of the graph: the gradation vector. We introduce the notion of minimum gradation vector as a new invariant for the graph and give polynomial algorithms to obtain it. These algorithms also output all greyscales that produce the minimum gradation vector. This way we tackle and solve a novel vectorial optimization problem in graphs that may produce more satisfactory solutions than those ones generated by known scalar optimization approaches. The interest of these new concepts lies in their possible applications for solving problems of engineering, physics and applied mathematics which are modeled according to a network whose nodes have assigned numerical values of a certain parameter delimited by a range of real numbers. The objective is to minimize the differences between each node and its neighbors, ensuring that the extreme values of the interval are assigned.