Sparsification of Binary CSPs

01/03/2019
by   Silvia Butti, et al.
0

A cut ε-sparsifier of a weighted graph G is a re-weighted subgraph of G of (quasi)linear size that preserves the size of all cuts up to a multiplicative factor of ε. Since their introduction by Benczúr and Karger [STOC'96], cut sparsifiers have proved extremely influential and found various applications. Going beyond cut sparsifiers, Filtser and Krauthgamer [SIDMA'17] gave a precise classification of which binary Boolean CSPs are sparsifiable. In this paper, we extend their result to binary CSPs on arbitrary finite domains.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/28/2021

Additive Sparsification of CSPs

Multiplicative cut sparsifiers, introduced by Benczúr and Karger [STOC'9...
research
09/10/2020

Near-linear Size Hypergraph Cut Sparsifiers

Cuts in graphs are a fundamental object of study, and play a central rol...
research
10/24/2022

Edge-Cuts and Rooted Spanning Trees

We give a closed form formula to determine the size of a k-respecting cu...
research
07/31/2020

Generalized Cut Polytopes for Binary Hierarchical Models

Marginal polytopes are important geometric objects that arise in statist...
research
04/21/2022

Motif Cut Sparsifiers

A motif is a frequently occurring subgraph of a given directed or undire...
research
12/06/2021

Faster Cut Sparsification of Weighted Graphs

A cut sparsifier is a reweighted subgraph that maintains the weights of ...
research
11/14/2018

Cutting resilient networks -- complete binary trees

In our previous work, we introduced the random k-cut number for rooted g...

Please sign up or login with your details

Forgot password? Click here to reset