TL;DR
This paper introduces new methods for cut sparsification and succinct representation of submodular hypergraphs, improving size bounds and encoding efficiency by leveraging the concept of deformation and a new parameter called spread.
Contribution
It proves polynomial-size sparsifiers for all submodular hypergraphs and introduces the spread parameter to optimize sparsifier size, also offering a more compact encoding for certain splitting functions.
Findings
Polynomial-size sparsifiers for submodular hypergraphs.
Introduction of the spread parameter for smaller sparsifiers.
More efficient encoding of cuts for specific splitting functions.
Abstract
In cut sparsification, all cuts of a hypergraph are approximated within factor by a small hypergraph . This widely applied method was generalized recently to a setting where the cost of cutting each hyperedge is provided by a splitting function . This generalization is called a submodular hypergraph when the functions are submodular, and it arises in machine learning, combinatorial optimization, and algorithmic game theory. Previous work studied the setting where is a reweighted sub-hypergraph of , and measured the size of by the number of hyperedges in it. In this setting, we present two results: (i) all submodular hypergraphs admit sparsifiers of size polynomial in and ; (ii) we propose a new parameter, called spread, and use it to obtain smaller sparsifiers in some…
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Videos
Cut Sparsification and Succinct Representation of Submodular Hypergraphs· youtube
