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地基设置成本预算的最大覆盖率问题

Irving van Heuven van Staereling1, Bart de Keijzer2, Guido Schäfer1,3

  • 1Networks & Optimization Group, Centrum Wiskunde & Informatica, Science Park 123, Amsterdam, 1098 XG The Netherlands.

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概括
此摘要是机器生成的。

本研究介绍了一个预算的最大覆盖范围的变种,用于优化投标. 研究人员开发了近似算法,包括针对特定超图结构的全多项式时间近似方案.

关键词:
接近算法的近似算法超图形 (Hypergraphs) 是一个超图形.最大覆盖面的问题是最大覆盖面.赞助搜索 赞助搜索 赞助搜索亚模块化优化的优化

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科学领域:

  • 组合优化的优化.
  • 算法设计 算法设计
  • 计算复杂性 计算复杂性

背景情况:

  • 预算的最大覆盖率问题是各种领域的应用程序的一个基本的NP-hard问题.
  • 引入了一个自然变体,在预算限制范围内考虑顶点成本,而不是超边缘成本.
  • 这个问题是由赞助搜索拍卖中的应用引起的,特别是竞价优化.

研究的目的:

  • 调查预算最大覆盖率问题与顶点成本的新变体.
  • 为这个问题开发高效的近似算法.
  • 分析问题的近似性,并确定准确或近乎准确的解决方案的条件.

主要方法:

  • 该研究采用了近似算法和复杂性理论的技术.
  • 从众所周知的硬问题 (如密度最大的k-subgraph) 进行减小,以确定不可接近性结果.
  • 针对不同类型的超图,开发了特定的算法方法,包括贪策略和动态编程.

主要成果:

  • 这个问题被证明至少和标准预算的最大覆盖率问题一样困难,这意味着[公式:参见文本]-不可接近性.
  • 一个[公式:见文本]-近似算法是为一般图表.
  • 一个完全多项式时间近似方案 (FPTAS) 对于高图,其发生率图是森林 (Berge-acyclic) 衍生,具有固定反集的扩展.
  • 一个[公式:见文本]-近似算法为基于最大顶点度的一般超图提供.

结论:

  • 开发的算法为预算最大覆盖率问题的特定实例提供了显著的改进.
  • 这些发现提供了对问题的复杂性及其对优化投标的实际影响的理论理解.
  • 未来的工作可以探索这些算法技术的进一步扩展和应用.