Large-Scale and Big Data Optimization

大规模、大数据优化

• 批准号: RGPIN-2017-06715
• 负责人: Jaumard, Brigitte
• 金额: $ 2.99万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636905
• 关键词: Large; Scale; Big; Data; Optimization

In today’s digital world, with ever increasing amounts of data comes the need to solve optimization problems of unprecedented sizes. Machine learning, communication and social networks, logistics systems are some of the many prominent application domains where optimization problems arise with tens of thousands or millions of variables. Many optimization models and algorithms, while exhibiting great efficiency in modest dimensions, have great difficulties to scale for instances of this size and do not offer satisfactory solution. The primary and long-term objective of my research is to contribute to the design of novel optimization algorithms capable of working in very large-scale setting. I plan to investigate both exact and heuristic methods, and validate the findings on some particular applications in communication, logistics and social networks.

在当今的数字世界中，随着数据量的不断增加，需要解决前所未有的规模的优化问题，机器学习、通信和社交网络、物流系统是许多突出的应用领域中的一些，其中出现了数万或数百万的优化问题。许多优化模型和算法虽然在适度的尺寸下表现出很高的效率，但对于这种尺寸的实例来说很难扩展，并且不能提供令人满意的解决方案。能够在超大规模环境中工作的新颖优化算法。我计划研究精确方法和启发式方法，并验证通信、物流和社交网络中某些特定应用的研究结果。