Explore cutting-edge research in randomized numerical linear algebra applied to streaming and sliding window models in this 24-minute IEEE conference talk. Delve into the challenges and solutions for near-optimal linear algebra techniques, including reverse online leverage scores, spectral sparsification, and low-rank approximation. Learn about the connections between online and sliding window models, and discover how these advanced algorithms can be applied to real-world data processing scenarios.
Near Optimal Linear Algebra in the Online and Sliding Window Models
Overview
Syllabus
Intro
Streaming / Sliding Window Model
Randomized Numerical Linear Algebra (randNLA) on Sliding Windows
Why randNLA on Sliding Windows?
Results: Sliding Window Model
Challenges
Reverse Online Leverage Scores
Algorithm
Spectral Sparsification (Summary)
Low-Rank Approximation
Template
Reverse Online l1 Sensitivities
Results: Online Model
Results: Connections
Taught by
IEEE FOCS: Foundations of Computer Science
