• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>SIAM/ASA Journal on Uncertainty Quantification >Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification
【24h】

Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification

机译：Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相关主题

摘要

As the scale of problems and data used for experimental design, signal processing, and data assimilation grow, the oft-occurring least squares subproblems are correspondingly growing in size. As the scale of these least squares problems creates prohibitive memory movement costs for the usual incremental QR and Krylov-based algorithms, randomized least squares problems are garnering more attention. However, these randomized least squares solvers are difficult to integrate into application algorithms as their uncertainty limits practical tracking of algorithmic progress and reliable stopping. Accordingly, in this work, we develop theoretically rigorous, practical tools for quantifying the uncertainty of an important class of iterative randomized least squares algorithms, which we then use to track algorithmic progress and create a stopping condition. We demonstrate the effectiveness of our algorithm by solving a 0.78 TB least squares subproblem from the inner loop of incremental 4D-Var using only 195 MB of memory.

著录项

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号