Data-Driven Multistage Robust Optimization—the New Frontier in Optimization

数据驱动的多级鲁棒优化——优化的新领域

• 批准号: DP210101025
• 负责人: Prof Vaithilingam Jeyakumar
• 金额: $ 28.15万
• 依托单位: The University of New South Wales
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP210101025
• 关键词: Data; Driven; Multistage; Robust; Optimization

Robust optimisation is a powerful technology for decision-making in uncertain environments. Yet, developing numerically certifiable optimisation principles and data-driven methods that can be readily implemented by common computer algorithms remains an elusive goal for multistage robust optimisation. But it is crucial for the practical use of multistage optimisation. This project aims to develop this novel mathematical theory and methods by extending the investigators' recent award winning advances, including the von Neumann-prizewinning Lasserre-hierarchy approach. Results will provide a foundation and technologies for making superior decisions in the pervasive presence of big data uncertainty, enhancing data-driven innovation in Australia

鲁棒优化是一种在不确定环境中进行决策的强大技术。然而，开发数字可验证的优化原则和数据驱动的方法，可以很容易地实现常见的计算机算法仍然是一个难以捉摸的目标多级鲁棒优化。但这对于多阶段优化的实际应用是至关重要的。该项目旨在通过扩展研究人员最近的获奖进展来开发这种新颖的数学理论和方法，包括冯诺依曼获奖的Lasserre层次方法。研究结果将为在大数据不确定性普遍存在的情况下做出上级决策提供基础和技术，增强澳大利亚的数据驱动型创新