首页> 外文期刊>Computers & operations research >Minimum-weight spanning tree algorithms A survey and empirical study
【24h】

Minimum-weight spanning tree algorithms A survey and empirical study

机译:最小权重生成树算法调查与实证研究

获取原文
获取原文并翻译 | 示例

摘要

The minimum-weight spanning tree problem is one of the most typical and well-known problems of combinatorial optimisation. Efficient solution techniques had been known for many years. However, in the last two decades asymptotically faster algorithms have been invented. Each new algorithm brought the time bound one step closer to linearity and finally Karger, Klein and Tarjan proposed the only known expected linear-time method. Modern algorithms make use of more advanced data structures and appear to be more complicated to implement. Most authors and practitioners refer to these but still use the classical ones, which are considerably simpler but asymptotically slower. The paper first presents a survey of the classical methods and the more recent algorithmic developments. Modern algorithms are then compared with the classical ones and their relative performance is evaluated through extensive empirical tests, using reasonably large-size problem instances. Randomly generated problem instances used in the tests range from small networks having 512 nodes and 1024 edges to quite large ones with 16 384 nodes and 524288 edges. The purpose of the comparative study is to investigate the conjecture that modern algorithms are also easy to apply and have constants of proportionality small enough to make them competitive in practice with the older ones.
机译:最小权重生成树问题是组合优化的最典型和众所周知的问题之一。高效的解决方案技术已为人所知多年。然而,在过去的二十年中,发明了渐近更快的算法。每种新算法使时间界限都更接近线性,最后Karger,Klein和Tarjan提出了唯一已知的期望线性时间方法。现代算法使用更高级的数据结构,并且实现起来似乎更加复杂。大多数作者和从业人员都参考了这些,但是仍然使用经典方法,这些方法虽然简单得多,但渐近地变慢。本文首先介绍了经典方法和最新算法的发展概况。然后将现代算法与经典算法进行比较,并使用合理的大型问题实例,通过广泛的经验测试对它们的相对性能进行评估。测试中使用的随机生成问题实例的范围从具有512个节点和1024个边缘的小型网络到具有16 384个节点和524288个边缘的相当大的网络。比较研究的目的是调查这样一个猜想,即现代算法也易于应用并且比例常数足够小,以使其在实践中与较旧的算法竞争。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号