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An exact algorithm for minimizing vertex guards on art galleries

机译:最小化美术馆顶点保护的精确算法

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We consider the problem [art gallery problem (AGP)] of minimizing the number of vertex guards required to monitor an art gallery whose boundary is an n-vertex simple polygon. In this paper, we compile and extend our research on exact approaches for solving the AGP. In prior works, we proposed and tested an exact algorithm for the case of orthogonal polygons. In that algorithm, a discretization that approximates the polygon is used to formulate an instance of the set cover problem, which is subsequently solved to optimality. Either the set of guards that characterizes this solution solves the original instance of the AGP, and the algorithm halts, or the discretization is refined and a new iteration begins. This procedure always converges to an optimal solution of the AGP and, moreover, the number of iterations executed highly depends on the way we discretize the polygon. Notwithstanding that the best known theoretical bound for convergence is ⊙(n~3) iterations, our experiments show that an optimal solution is always found within a small number of them, even for random polygons of many hundreds of vertices. Herein, we broaden the family of polygon classes to which the algorithm is applied by including non-orthogonal polygons. Furthermore, we propose new discretization strategies leading to additional trade-off analysis of preprocessing vs. processing times and achieving, in the case of the novel Convex Vertices strategy, the most efficient overall performance so far. We report on experiments with both simple and orthogonal polygons of up to 2500 vertices showing that, in all cases, no more than 15 minutes are needed to reach an exact solution, on a standard desktop computer. Ultimately, we more than doubled the size of the largest instances solved to optimality compared with our previous experiments, which were already five times larger than those previously reported in the literature.
机译:我们考虑使监视边界为n个顶点的简单多边形的画廊所需的顶点保护的数量最小化的问题[画廊问题(AGP)]。在本文中,我们汇编并扩展了有关解决AGP的确切方法的研究。在先前的工作中,我们针对正交多边形的情况提出并测试了一种精确的算法。在该算法中,将近似多边形的离散化用于制定集合覆盖问题的实例,然后将其求解为最优。代表此解决方案的一组保护措施可以解决AGP的原始实例,并且算法将停止,或者离散化会得到完善,并且会开始新的迭代。此过程始终会收敛到AGP的最佳解决方案,此外,执行的迭代次数高度取决于我们离散化多边形的方式。尽管最著名的收敛理论边界是⊙(n〜3)次迭代,但我们的实验表明,即使对于具有数百个顶点的随机多边形,也总是在少数迭代中找到最优解。在此,我们通过包括非正交多边形来拓宽应用该算法的多边形类的族。此外,我们提出了新的离散化策略,从而导致对预处理与处理时间进行更多的权衡分析,并且在新颖的凸顶点策略的情况下,实现了迄今为止最有效的整体性能。我们报告了多达2500个顶点的简单和正交多边形的实验,结果表明,在所有情况下,在标准台式计算机上,只需不到15分钟即可获得精确的解决方案。最终,与之前的实验相比,我们将最大实例的大小增加了一倍以上,从而达到了最佳效果,而这些实验已经是文献中先前报道的五倍。

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