AF: Small: Fast and Efficient Randomized Algorithms for Solving Laplacian Systems of Linear Equations and Sparse Least Squares Problems

AF：小型：用于解决线性方程拉普拉斯系统和稀疏最小二乘问题的快速高效随机算法

• 批准号: 1016501
• 负责人: Petros Drineas
• 金额: $ 32.27万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016501&HistoricalAwards=false
• 关键词: AF; Small; Fast; Efficient; Randomized

Randomization in the context of linear-algebraic algorithms is an exciting and innovative idea. In recent years, a large body of work has focused on provably accurate randomized algorithms for regression problems, with a particular emphasis on least-squares regression. Fast algorithms for such problems are of continuous interest due to their broad applicability in scientific computing and statistical data analysis, where increasingly larger input matrices appear. The PI seeks to theoretically and numerically investigate provably accurate and practically useful randomized algorithms for such problems when (i) the constraint matrix of the regression problem is Laplacian, or (ii) the regression problem is under- or over-constrained and sparse. Thus, the PI seeks to address the alarming gap between recent breakthrough theoretical results of Spielman, Teng, and collaborators and their practical applicability, as well as the lack of efficient algorithms dealing with over- or under-constrained regression problems with sparse input matrices. In order to bridge the gap between theory and applications in this line of research, a number of novel theoretical results are necessary and will be investigated. The practical usefulness of the proposed research will be numerically evaluated using data matrices from scientific applications.Efficiently solving large systems of linear equations is perhaps the most fundamental question in numerical analysis and linear algebra, mainly because such systems are ubiquitous in scientific computing applications. The proposed work seeks to bring the theoretical breakthroughs of the recent work of Spielman, Teng, and collaborators on solving systems of linear equations with Laplacian input matrices closer to practice. Towards that end, both theoretical as well as numerical results will be derived. This research paradigm can subsequently be used as a starting point in order to spark further research efforts on broader classes of massive systems of linear equations. A second aspect of the impact of the proposed work has to do with the considerable overlap between Theoretical Computer Science and Numerical Linear Algebra approaches that will be explored. As randomization becomes increasingly useful in the context of linear algebra, the PI expects that the next generation of researchers in this domain will need solid training in both areas, which is exactly what the proposed work will provide to graduate students. Finally, a third aspect of the impact of the proposed work will emerge from the dissemination of our results via workshops, tutorials, and mini-symposia in high-profile relevant conferences.

线性代数算法中的随机化是一个令人兴奋和创新的想法。近年来，大量的工作集中在可证明的精确随机算法的回归问题，特别强调最小二乘回归。此类问题的快速算法由于其在科学计算和统计数据分析中的广泛适用性而一直受到关注，在这些领域中出现越来越大的输入矩阵。当(i)回归问题的约束矩阵是拉普拉斯的，或（ii）回归问题是约束不足或过度约束和稀疏的，PI寻求在理论上和数值上研究可证明准确和实际有用的随机算法。因此，PI试图解决Spielman， Teng和合作者最近的突破性理论结果与其实际适用性之间的惊人差距，以及缺乏处理稀疏输入矩阵的过度或欠约束回归问题的有效算法。为了在这方面的研究中弥合理论与应用之间的差距，一些新的理论结果是必要的，并且将被研究。拟议研究的实际用途将使用科学应用中的数据矩阵进行数值评估。有效地求解大型线性方程组可能是数值分析和线性代数中最基本的问题，主要是因为这些系统在科学计算应用中无处不在。提出的工作旨在将Spielman， Teng和合作者最近在解决具有拉普拉斯输入矩阵的线性方程组方面的理论突破更接近实践。为此，将推导出理论和数值结果。这一研究范式随后可以作为一个起点，以激发对更广泛的大规模线性方程系统的进一步研究。所提议的工作影响的第二个方面与理论计算机科学和数值线性代数方法之间的相当大的重叠有关，这些方法将被探索。随着随机化在线性代数的背景下变得越来越有用，PI期望该领域的下一代研究人员在这两个领域都需要扎实的训练，这正是提议的工作将为研究生提供的。最后，建议工作的影响的第三个方面将通过研讨会、教程和在高知名度的相关会议上的小型专题讨论会来传播我们的结果。