应用度量正则性计算矩阵博弈平滑算法的条件测度
Applying Metric Regularity to Compute a Condition Measure of a Smoothing Algorithm for Matrix Games
AI总结：
本文通过变分分析和广义微分，建立了Gilpin等人提出的平滑一阶算法的条件测度与相关集值映射的度量正则性精确界之间的关系，并利用初始矩阵博弈数据计算该条件测度。
摘要：
我们发展了一种变分分析和广义微分的方法，用于处理两人零和矩阵博弈的条件问题。我们的主要结果建立了Gilpin等人[第23届AAAI会议论文集(2008)第75-82页]提出的平滑一阶算法的某个条件测度与相关集值映射的度量正则性精确界之间的精确关系。通过这种方式，我们根据初始矩阵博弈数据计算了上述条件测度。
英文摘要：
We develop an approach of variational analysis and generalized differentiation to conditioning issues for two-person zero-sum matrix games. Our major results establish precise relationships between a certain condition measure of the smoothing first-order algorithm proposed by Gilpin et al. [Proceedings of the 23rd AAAI Conference (2008) pp. 75-82] and the exact bound of metric regularity for an associated set-valued mapping. In this way we compute the aforementioned condition measure in terms of the initial matrix game data.
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。
