Daniel Hayes, University of Delaware
Event Date
2026-03-25
Event Time
04:00 pm ~ 05:00 pm
Event Location
617 Wachman Hall
In many areas of scientific computing, statistical analysis, and machine learning, the ability to efficiently and accurately handle large scale and high dimensional data is a rapidly growing necessity. Many powerful techniques have been developed to approach this task, one being Tensor-Train (TT) and variants such as Tensor-Train Cross (TT-Cross). TT-Cross provides a framework suited for working with extremely large datasets while maintaining low memory and computational complexity. In this talk, I will introduce the foundations at the matrix level and the base TT-Cross algorithm. I will then discuss a post-processing oversampling algorithm designed to enhance approximation accuracy. Numerical experiments demonstrate improved computational efficiency and systematic error reduction via oversampling.