不同类型网格图的两步着色法

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS
A. V. Smirnov
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引用次数: 0

摘要

摘要 在本文中,我们考虑了图的两步着色这一 NP 难问题。要求在给定颜色数的情况下给图着色,如果没有一对顶点具有相同的颜色,则这些顶点之间的距离为 1 或 2。最佳的两步着色是使用尽可能少的颜色数。我们将两步着色问题应用到网格图中进行研究。我们考虑了四种网格:三角形、正方形、六边形和八边形。我们证明,在一般情况下,六边形和八边形网格图的最佳两步着色需要四种颜色。我们提出了这种着色的多项式算法。最大顶点度等于 3 的正方形网格图需要四种或五种颜色进行两步着色。本文针对这种情况提出了回溯算法。此外,我们还提出了对三角形网格图进行七色两步着色的算法,相对于顶点数,该算法的工作时间是线性的,并证明这种着色总是正确的。如果最大顶点度等于 6,那么解决方案就是最优的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Two-Step Coloring of Grid Graphs of Different Types

Two-Step Coloring of Grid Graphs of Different Types

Two-Step Coloring of Grid Graphs of Different Types

In this article, we consider the NP-hard problem of the two-step coloring of a graph. It is required to color the graph in the given number of colors in a way, when no pair of vertices has the same color, if these vertices are at a distance of one or two between each other. The optimum two-step coloring is one that uses the minimum possible number of colors. The two-step coloring problem is studied in application to grid graphs. We consider four types of grids: triangular, square, hexagonal, and octagonal. We show that the optimum two-step coloring of hexagonal and octagonal grid graphs requires four colors in the general case. We formulate the polynomial algorithms for such a coloring. A square grid graph with the maximum vertex degree equal to 3 requires four or five colors for a two-step coloring. In this paper, we propose the backtracking algorithm for this case. Also, we present the algorithm, which works in linear time relative to the number of vertices, for the two-step coloring in seven colors of a triangular grid graph and show that this coloring is always correct. If the maximum vertex degree equals six, the solution is optimum.

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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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