Linear Sketching Hypergraph Sparsifiers

Explore the concept of linear sketching hypergraph sparsifiers in this 34-minute lecture by Aaron Putterman from Harvard University. Dive into the world of sublinear graph simplification and learn about $(1 \pm \epsilon)$-sparsifiers of hypergraphs. Discover how these sparsifiers preserve cut values within a specific factor and the known bounds for their size. Examine the task of constructing sparsifiers using only linear measurements over hyperedges, and understand the nearly-matching upper and lower bounds for this process. Investigate the randomized linear sketch technique and its applications in dynamic streaming algorithms for hypergraph cut sparsification. Gain insights into new techniques such as improved error accumulation analysis, pre-processing linear sketches, and random fingerprinting for breaking correlations between hyperedges. Enhance your understanding of advanced graph theory concepts and their practical applications in data compression and streaming algorithms.