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

稳健优化是在不确定环境中进行决策的一项强大技术。然而，开发数字可证明的优化原则和数据驱动的方法，可以很容易地由常见的计算机算法实现，仍然是多阶段稳健优化的一个难以实现的目标。但这对多阶段优化的实际应用至关重要。这个项目旨在通过扩展研究人员最近获奖的进展来发展这一新的数学理论和方法，包括冯·诺伊曼获奖的拉瑟尔层次方法。结果将为在大数据不确定性普遍存在的情况下做出卓越决策提供基础和技术，加强澳大利亚的数据驱动创新

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