Finding a second Hamiltonian decomposition of a 4-regular multigraph by integer linear programming

IF 0.9 4区 数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Andrei V. Nikolaev, Egor V. Klimov
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引用次数: 0

Abstract

A Hamiltonian decomposition of a regular graph is a partition of its edge set into Hamiltonian cycles. We consider the second Hamiltonian decomposition problem: for a 4-regular multigraph, find 2 edge-disjoint Hamiltonian cycles different from the given ones. This problem arises in polyhedral combinatorics as a sufficient condition for non-adjacency in the 1-skeleton of the traveling salesperson polytope. We introduce two integer linear programming models for the problem based on the classical Dantzig-Fulkerson-Johnson and Miller-Tucker-Zemlin formulations for the traveling salesperson problem. To enhance the performance on feasible problems, we supplement the algorithm with a variable neighborhood descent heuristic w.r.t. two neighborhood structures and a chain edge fixing procedure. Based on the computational experiments, the Dantzig-Fulkerson-Johnson formulation showed the best results on directed multigraphs, while on undirected multigraphs, the variable neighborhood descent heuristic was especially effective.

Abstract Image

通过整数线性规划寻找 4 规则多图的第二哈密顿分解
正则图的哈密顿分解是将其边集分割成哈密顿循环。我们考虑的是第二个哈密顿分解问题:对于一个 4 不规则的多图,找出 2 个与给定哈密顿循环不同的边相交的哈密顿循环。这个问题出现在多面体组合学中,是旅行推销员多面体 1 骨架中不相接的充分条件。我们根据旅行推销员问题的经典 Dantzig-Fulkerson-Johnson 公式和 Miller-Tucker-Zemlin 公式,为该问题引入了两个整数线性规划模型。为了提高在可行问题上的性能,我们在算法中增加了一个可变邻域下降启发式,其中包含两个邻域结构和一个链边固定程序。根据计算实验,Dantzig-Fulkerson-Johnson 公式在有向多图上显示出最佳结果,而在无向多图上,可变邻域下降启发式特别有效。
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来源期刊
Journal of Combinatorial Optimization
Journal of Combinatorial Optimization 数学-计算机:跨学科应用
CiteScore
2.00
自引率
10.00%
发文量
83
审稿时长
6 months
期刊介绍: The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering. The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.
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