论文标题
迭代合奏Kalman方法：具有一些新变体的统一视角
Iterative Ensemble Kalman Methods: A Unified Perspective with Some New Variants
论文作者
论文摘要
本文提供了迭代迭代集合Kalman方法的统一观点，这是一个无衍生算法的家庭，用于参数重建和其他相关任务。我们确定，比较和开发三个集合方法的亚家族，这些子集合方法在他们试图最小化的目标和通过整体近似的基于衍生的优化方案中有所不同。我们的工作强调了迭代集合方法的推导和分析的两个原则：统计线性化和连续限制。遵循这些指导原则，我们介绍了新的迭代集合卡尔曼方法，这些方法在贝叶斯反问题，数据同化和机器学习任务中显示出有希望的数值性能。
This paper provides a unified perspective of iterative ensemble Kalman methods, a family of derivative-free algorithms for parameter reconstruction and other related tasks. We identify, compare and develop three subfamilies of ensemble methods that differ in the objective they seek to minimize and the derivative-based optimization scheme they approximate through the ensemble. Our work emphasizes two principles for the derivation and analysis of iterative ensemble Kalman methods: statistical linearization and continuum limits. Following these guiding principles, we introduce new iterative ensemble Kalman methods that show promising numerical performance in Bayesian inverse problems, data assimilation and machine learning tasks.
