Innovative Techniques for Constructing Branch Decompositions

构建分支分解的创新技术

• 批准号: 0611723
• 负责人: Illya Hicks
• 金额: $ 9.51万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-15 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0611723&HistoricalAwards=false
• 关键词: Innovative; Techniques; Constructing; Branch; Decompositions

This grant provides funding for the development of novel numerical techniques to solve computationally hard mathematical problems in combinatorial optimization that have relevant applications in network design, sensor networks, and biology. The developed techniques will be used to increase the scalability of problems solved in these research areas. Specifically, the research will develop innovative techniques to produce a specific but vital decomposition structure, called a branch decomposition, for mathematical structures called graphs that are used to mathematically model these problems. To this end, the developed techniques will be compared with other exact algorithms in the literature to validate and assess the computational efficacy of the techniques. If successful, the results will increase the scalability and efficiency of solving the problems of interest. Applications of these problems are in fields seeing increased demand for services. In addition, the results can be used to solve other related computationally hard problems in combinatorial optimization which have a wide range of applications apart from the aforementioned applications. Thus, the results will advance the knowledge base in combinatorial optimization and offer new effective techniques for solving large-scale problems in the areas.

这笔赠款为开发新的数值技术提供资金，以解决组合优化中计算困难的数学问题，这些问题在网络设计、传感器网络和生物学中有相关应用。所开发的技术将用于增加这些研究领域所解决问题的可扩展性。具体地说，这项研究将开发创新技术，以产生一种特定但至关重要的分解结构，称为分支分解，用于对这些问题进行数学建模的称为图的数学结构。为此，开发的技术将与文献中的其他精确算法进行比较，以验证和评估这些技术的计算效率。如果成功，结果将增加可扩展性和解决感兴趣的问题的效率。这些问题的应用领域看到了对服务的需求增加。此外，所得结果还可用于解决组合优化中除上述应用外具有广泛应用的其他相关计算困难问题。因此，该结果将推进组合优化领域的知识库，并为解决该领域的大规模问题提供新的有效技术。