一种可扩展的两阶段自适应线性最优化算法
A Scalable Algorithm for Two-Stage Adaptive Linear Optimization
摘要：
提出了一种求解两阶段自适应优化问题的列约束生成方法（CCG），该方法由\citet{Zeng2013}提出。我们发现，CCG方法是相当可扩展的，但有时，在某些应用程序中，往往会产生不可行的第一阶段的解决方案，即使问题是可行的。在这项研究中，我们扩展了CCG方法的方式，（a）保持可扩展性和（b）总是产生可行的第一阶段的决定，如果它们存在。我们将我们的方法与最近提出的几种方法进行比较，发现它更快地达到高精度，并解决了更大的问题。
英文摘要：
The column-and-constraint generation (CCG) method was introduced by \citet{Zeng2013} for solving two-stage adaptive optimization. We found that the CCG method is quite scalable, but sometimes, and in some applications often, produces infeasible first-stage solutions, even though the problem is feasible. In this research, we extend the CCG method in a way that (a) maintains scalability and (b) always produces feasible first-stage decisions if they exist. We compare our method to several recently proposed methods and find that it reaches high accuracies faster and solves significantly larger problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
