School of CSE Seminar Series: Andreas Stathopoulos

Speaker: Andreas Stathopoulos, professor at College of William & Mary
Date and Time: October 24, 2:00-3:00 p.m.
Location: Coda, Room 114
Host: Edmond Chow

Title: Improving and Evaluating Streaming SVD for Large Scale Problems

Abstract: We study streaming singular value decomposition (SVD) for approximating large-scale SVD problems where data matrices are too large to store or recompute. Our focus is on Incremental SVD (iSVD), which applies hard-thresholding truncation after each streamed window. In contrast, Frequent Directions employs soft thresholding with provable but often loose bounds, and randomized methods such as SketchySVD construct one-pass sketches.  We propose algorithmic and theoretical improvements to iSVD and evaluate them experimentally.  

A “large-window” variant processes as many rows as memory allows, improving both accuracy and efficiency. Implemented in Skema, a high-performance C++ framework using Kokkos and PRIMME, this approach scales to massive datasets. Algorithmic contributions include warm-start iterative solvers reducing iterations by 30–50% and a reservoir-sampling–based convergence criterion for early stopping.

Theoretically, we outline a proof roadmap that when streaming rows in a random order (random shuffle), iSVD avoids pathological cases and achieves higher expected accuracy than Frequent Directions. Complexity analysis clarifies accuracy–cost trade-offs with SketchySVD, while experiments demonstrate that iSVD achieves several orders of magnitude higher accuracy at competitive runtime. As an application, we use iSVD to obtain better quality multigrid prolongators in Lattice Quantum Chromodynamics.

Bio: Andreas Stathopoulos is a Professor of Computer Science at William & Mary in Virginia, USA. 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. Since 2010 he is an Associate Editor in SIAM Journal on Scientific Computing and from 2016-2022 served as its Section Editor for Software and HPC.