Collaborative Research: Randomized Numerical Linear Algebra for Large Scale Inversion, Sparse Principal Component Analysis, and Applications

合作研究：大规模反演的随机数值线性代数、稀疏主成分分析及应用

基本信息

批准号：
2152661
负责人：
Matthias Chung
金额：
$ 10万
依托单位：
Emory University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152661&HistoricalAwards=false
关键词：
Collaborative Research Randomized Numerical Linear

项目摘要

In many scientific applications such as genetics, geophysics, bioinformatics, and medicine, data are being generated at ever-increasing rates. For these, and other data-intensive applications, the massive size of the data sets, as well as the growing model complexities, present fundamental computational challenges. State-of-the-art inference methods have exceeded their limits of applicability and advanced mathematical, computational, and statistical tools are urgently needed to extract relevant information. This research addresses the urgent need to advance efficient methods for computing solutions of large-scale inverse problems. This project will advance tools from inverse problems which will be merged with novel approaches from randomized numerical linear algebra, and sparse principal component analysis. The expanded tools produced by this project will have the ability to transform the field of large-scale inverse problems and subsequently benefit a wide variety of applications.The development of novel approaches for large-scale inversion will significantly advance current solutions in a wide range of applications, such as machine learning, geophysics, and genetics. This project will investigate advanced iterative methods for inverse problems, randomization, sketching schemes, as well as methods for sparse principal component analysis. By accelerating numerical methods, providing theoretical convergence analysis, and producing a user-friendly software package, the broader scientific community will be able to integrate these advanced tools within their application areas. The project offers training opportunities for students in computational and applied mathematics. These include the implementation of a novel cross-institutional graduate course, merging the expertise of the three PIs in the topics of inverse problems, randomized linear algebra, and numerical optimization, to provide a broader opportunity for graduate students to engage in timely research projects; connect with their peers across the US; and expand the diversity pool of students in our programs. Collaborations between the project team and domain experts guarantee that the proposed algorithms and software will have an impact on real data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在遗传学、地球物理学、生物信息学和医学等许多科学应用中，数据的生成速度不断加快。对于这些以及其他数据密集型应用程序，数据集的巨大规模以及不断增长的模型复杂性提出了基本的计算挑战。最先进的推理方法已经超出了其适用范围，迫切需要先进的数学、计算和统计工具来提取相关信息。这项研究解决了对大规模逆问题的计算解决方案提出有效方法的迫切需要。该项目将改进逆问题的工具，这些工具将与随机数值线性代数和稀疏主成分分析的新方法相结合。该项目产生的扩展工具将能够改变大规模反演问题领域，并随后使各种应用受益。大规模反演新方法的开发将在广泛的领域显着推进当前的解决方案应用，例如机器学习、地球物理学和遗传学。该项目将研究反问题、随机化、草图方案的先进迭代方法，以及稀疏主成分分析方法。通过加速数值方法、提供理论收敛分析以及生成用户友好的软件包，更广泛的科学界将能够将这些先进的工具集成到其应用领域中。该项目为学生提供计算和应用数学方面的培训机会。其中包括实施新颖的跨机构研究生课程，融合三位PI在反问题、随机线性代数和数值优化等主题上的专业知识，为研究生及时参与研究项目提供更广泛的机会；与美国各地的同行联系；并扩大我们项目中学生的多样性。项目团队和领域专家之间的合作保证了所提出的算法和软件将对真实数据产生影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。