Randomized Least Squares Optimization and its Incredible Utility for Large-Scale Tensor Decomposition

Explore randomized least squares optimization and its powerful applications in large-scale tensor decomposition in this 33-minute talk by Tamara Kolda from MathSci.ai. Delivered as part of the Extroverted Sublinear Algorithms series at the Simons Institute, delve into the innovative techniques and methodologies that are revolutionizing the field of tensor decomposition. Gain insights into how these advanced optimization methods can be leveraged to tackle complex computational challenges in data analysis and machine learning.