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引用次数: 0
摘要
本文研究任意自然倍率 k > 1 的无向多重图。图中有三种类型的边:普通边、多重边和多重边。后两种类型的每条边都是 k 条连接边的联合,它们分别连接 2 个或 (k + 1) 个顶点。链接边应同时使用。如果一个顶点与一条多重边相连,那么它也可以与其他多重边相连,而且它可以是某条多重边的 k 条链接边的公共端。如果一个顶点是某多条边的公共端,则它不能成为另一条多条边的公共端。可分割多重图的特点是可以将图分割成 k 个部分,这些部分在链接边上进行调整,并且没有公共边。每个部分都是一个普通图。与普通图一样,我们可以为多重图定义边长的整数函数,并设置连接两个顶点的最短路径问题。任何多重路径都是 k 条普通路径的联合,而普通路径是在所有多重路径和多重路径的链接边上进行调整的。在本文中,我们证明了对于可分割多重图,最短路径问题是多项式问题。并提出了相应的多项式算法。我们还提出了针对任意多重图的算法修改。这种修改在参数 k 上具有指数级的复杂性。
The Polynomial Algorithm of Finding the Shortest Path in a Divisible Multiple Graph
In this paper, we study undirected multiple graphs of any natural multiplicity k > 1. There are edges of three types: ordinary edges, multiple edges, and multiedges. Each edge of the last two types is a union of k linked edges, which connect 2 or (k + 1) vertices, correspondingly. The linked edges should be used simultaneously. If a vertex is incident to a multiple edge, it can also be incident to other multiple edges and it can be the common end of k linked edges of some multiedge. If a vertex is the common end of some multiedge, it cannot be the common end of another multiedge. Divisible multiple graphs are characterized by the possibility to divide the graph into k parts, which are adjusted on the linked edges and which have no common edges. Each part is an ordinary graph. As for an ordinary graph, we can define the integer function of the length of an edge for a multiple graph and set the problem of the shortest path joining two vertices. Any multiple path is a union of k ordinary paths, which are adjusted on the linked edges of all multiple and multiedges. In this article, we show that the problem of the shortest path is polynomial for a divisible multiple graph. The corresponding polynomial algorithm is formulated. We also propose the modification of the algorithm for the case of an arbitrary multiple graph. This modification has an exponential complexity in the parameter k.
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