Improved Decision Rule Approximations for Multistage Robust Optimization via Copositive Programming

Improved decision rule approximations for multistage robust optimization via copositive programming Previous research in the field has proposed several approaches to tackle multistage robust optimization problems, but they are often limited in their applicability. These existing methods either fail to handle cases where recourse matrices are uncertain or struggle to handle large-scale problems effectively. In their paper titled “Improved decision rule approximations for multistage robust optimization via copositive programming,” Guanglin Xu and Grani A. Hanasusanto contribute to the robust optimization literature by presenting a novel solution method. Their approach utilizes convex conic techniques and aims to address the general case of multistage robust optimization, where uncertainty exists in the recourse matrices. One significant advantage of their proposed method is its ability to scale well with large-sized instances, overcoming a common limitation faced by previous methods. Through numerical experiments on various simulated applications, Xu and Hanasusanto demonstrate the superiority of their algorithm over existing state-of-the-art methods.

通过余正规划改进多阶段鲁棒优化的决策规则近似 该领域先前的研究已经提出了几种解决多阶段鲁棒优化问题的方法，但它们的适用性往往有限。这些现有方法要么无法处理补偿矩阵不确定的情况，要么难以有效处理大规模问题。在他们题为《通过余正规划改进多阶段鲁棒优化的决策规则近似》的论文中，徐广林和格拉尼·A·哈纳苏桑托通过提出一种新的解决方法，为鲁棒优化文献做出了贡献。他们的方法利用凸锥技术，旨在解决多阶段鲁棒优化的一般情况，即补偿矩阵中存在不确定性。他们所提出方法的一个显著优势是它能够很好地处理大规模实例，克服了先前方法面临的一个常见限制。通过在各种模拟应用上进行数值实验，徐和哈纳苏桑托证明了他们的算法相对于现有最先进方法的优越性。