Near-linear Size Hypergraph Cut Sparsifiers
Yu Chen, Sanjeev Khanna, Ansh Nagda

TL;DR
This paper presents a new polynomial-time algorithm for hypergraph cut sparsifiers that match the size bounds known for graph sparsifiers, significantly improving previous results.
Contribution
It introduces a novel algorithm that constructs hypergraph cut sparsifiers of near-linear size, matching the bounds previously known only for graphs.
Findings
Hypergraph cut sparsifiers of size O(n/^2) are achievable.
The new algorithm runs in polynomial time.
This resolves an open question about hypergraph sparsifier size bounds.
Abstract
Cuts in graphs are a fundamental object of study, and play a central role in the study of graph algorithms. The problem of sparsifying a graph while approximately preserving its cut structure has been extensively studied and has many applications. In a seminal work, Bencz\'ur and Karger (1996) showed that given any -vertex undirected weighted graph and a parameter , there is a near-linear time algorithm that outputs a weighted subgraph of of size such that the weight of every cut in is preserved to within a -factor in . The graph is referred to as a {\em -approximate cut sparsifier} of . A natural question is if such cut-preserving sparsifiers also exist for hypergraphs. Kogan and Krauthgamer (2015) initiated a study of this question and showed that given any…
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Taxonomy
TopicsVLSI and FPGA Design Techniques · Low-power high-performance VLSI design · Interconnection Networks and Systems
