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FRG: Collaborative Research: Randomized Algorithms for Solving Linear Systems

FRG：协作研究：求解线性系统的随机算法

基本信息

批准号：
1952735
负责人：
Per-Gunnar Martinsson
金额：
$ 67.7万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952735&HistoricalAwards=false
关键词：
FRG Collaborative Research Randomized Algorithms

项目摘要

The objective of this project is to develop faster and more energy-efficient algorithms for one of the most fundamental tasks in computational science: solving large systems of coupled linear equations. Faster algorithms will both accelerate computations that can already be performed, and enable computations that are beyond the reach of existing methods. More energy efficient algorithms will help to reduce the power consumption of data centers, and to extend the battery life of mobile devices such as cell phones and tablet computers. The fundamental innovation behind our approach is to harness mathematical properties of large collections of random numbers to build new stochastic algorithms that dramatically outperform existing deterministic ones. In a nutshell, the idea is to use randomized sampling, and randomized averaging, to reduce the effective dimensionality of the problems to be processed. In addition the project provides research training opportunities for postdoctoral fellows and graduate students.We seek to develop computationally efficient methods for solving linear systems of equations involving large numbers of variables, both in terms of asymptotic complexity, and in terms of practical speed at realistic problem sizes. Such systems of equations arise ubiquitously in science and engineering, and solving them is often the bottleneck in terms of time that decides how large of a problem can be handled. In particular, this is what limits how large of a data set can be analyzed, or how realistic a computational simulation can be when modelling some physical phenomenon. By developing faster and more efficient algorithms, we will accelerate computations that are done today, and enable many others that are outside the reach of currently existing methods. The project is premised on the recent development of new randomized algorithms for solving linear algebraic problems. Such methods have proven to dramatically outperform classical deterministic methods for certain tasks such as computing low rank factorizations to matrices - the crucial computational step in e.g. Principal Component Analysis, the PageRank algorithm by Larry Page and Sergey Brin, numerical coarse graining when modeling complex multiscale systems, and many more. Randomized algorithms have also been used to build faster solvers for linear systems. However, while the theoretical results obtained at this point are extremely encouraging, it remains to develop randomized linear solvers that are decisively faster in practical applications. To achieve this goal, the project will support a research group that brings together four researchers with complementary skills in numerical linear algebra, random matrix theory, computational harmonic analysis, optimization, and high performance computing.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.

该项目的目标是为计算科学中最基本的任务之一开发更快、更节能的算法：求解大型耦合线性方程组。更快的算法将加速已经可以执行的计算，并实现现有方法无法完成的计算。更节能的算法将有助于降低数据中心的功耗，并延长手机和平板电脑等移动设备的电池寿命。我们方法背后的根本创新是利用大量随机数集合的数学特性来构建新的随机算法，其性能显着优于现有的确定性算法。简而言之，其想法是使用随机采样和随机平均来减少要处理的问题的有效维度。此外，该项目还为博士后和研究生提供研究培训机会。我们寻求开发计算有效的方法来求解涉及大量变量的线性方程组，无论是在渐近复杂性方面，还是在实际问题规模的实际速度方面。这种方程组在科学和工程中普遍存在，解决它们通常是决定问题大小的时间瓶颈。特别是，这限制了可以分析的数据集的大小，或者在对某些物理现象进行建模时计算模拟的真实程度。通过开发更快、更高效的算法，我们将加速当前完成的计算，并实现当前现有方法无法实现的许多其他计算。该项目的前提是最近开发了用于解决线性代数问题的新随机算法。事实证明，对于某些任务，例如计算矩阵的低阶因式分解，此类方法的性能显着优于经典的确定性方法 - 例如计算中的关键计算步骤。主成分分析、Larry Page 和 Sergey Brin 的 PageRank 算法、复杂多尺度系统建模时的数值粗粒度等等。随机算法也被用来为线性系统构建更快的求解器。然而，虽然此时获得的理论结果非常令人鼓舞，但仍然需要开发在实际应用中速度更快的随机线性求解器。为了实现这一目标，该项目将支持一个研究小组，该小组汇集了四名在数值线性代数、随机矩阵理论、计算调和分析、优化和高性能计算方面具有互补技能的研究人员。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。