计算机软件新技术国家重点实验室       

摘 要：

The singular value decomposition (SVD) is one of the core computations of today‘s scientific applications and data analysis tools. The main goal is to compute a compact representation of a high dimensional operator, a matrix, or a set of data that best resembles the original in its most important features. Thus, the SVD is widely used in scientific computing and machine learning, including low rank factorizations, graph learning, unsupervised learning, compression and analysis of images and text. The popularity of the SVD has resulted in an increased diversity of methods and implementations that exploit specific features of the input data (e.g., dense/sparse matrix, data distributed among the computing devices, data from queries or batch access, spectral decay) and certain constraints on the computed solutions (e.g., few/many number of singular values and singular vectors computed, targeted part of the spectrum, accuracy). The use of the proper method and the customization of the settings can significantly reduce the cost. In this talk, we’ll overview the most relevant methods in terms of computing cost and accuracy (direct methods, iterative methods, online methods), including the most recent advances in randomized and online SVD solvers. We present what parameters have the biggest impact on the computational cost and the quality of the solution, and some intuition for their tuning. Finally, we discuss the current state of the software on widely used platforms (MATLAB, Python‘s numpy/scipy and R) as well as high-performance solvers with support for multicore, GPU, and distributed memory。

 报告人简介:

Andreas Stathopoulos is a Professor of Computer Science at the College of William and Mary in Virginia. He was awarded an NSF CISE Postdoctoral Fellowship after receiving his Ph.D. and M.S. in Computer Science from Vanderbilt University; he also completed a B.S. in Mathematics from the University of Athens in Greece. Dr. Stathopoulos’ research interests include numerical analysis and high performance computing; methods for large eigenvalue problems and linear systems of equations; and related applications from materials science and quantum chromodynamics. He co-developed PRIMME (Preconditioned Iterative MultiMethod Eigensolver), one of the foremost eigenvalue packages, several other significant software tools, and has published numerous journal articles and conference papers in computational sciences and applications.  He is a member of IEEE, IEEE Computer, and SIAM, and the Section Editor for Software and HPC in SIAM Journal on Scientific Computing.

时间：6月11日  10:50-11:30

地点：计算机科学技术楼229室