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Related papers: Additive Sparsification of CSPs

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A cut $\varepsilon$-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 $\varepsilon$. Since their introduction by Bencz\'ur and…

Data Structures and Algorithms · Computer Science 2020-03-25 Silvia Butti , Stanislav Zivny

The seminal work of Bencz\'ur and Karger demonstrated cut sparsifiers of near-linear size. Subsequent extensions have yielded sparsifiers for hypergraph cuts and more recently linear codes over Abelian groups. A decade ago, Kogan and…

Data Structures and Algorithms · Computer Science 2026-05-19 Joshua Brakensiek , Venkatesan Guruswami

CSP sparsification, introduced by Kogan and Krauthgamer (ITCS 2015), considers the following question: how much can an instance of a constraint satisfaction problem be sparsified (by retaining a reweighted subset of the constraints) while…

Data Structures and Algorithms · Computer Science 2024-11-07 Sanjeev Khanna , Aaron L. Putterman , Madhu Sudan

A sparsifier of a graph $G$ (Bencz\'ur and Karger; Spielman and Teng) is a sparse weighted subgraph $\tilde G$ that approximately retains the cut structure of $G$. For general graphs, non-trivial sparsification is possible only by using…

Data Structures and Algorithms · Computer Science 2019-05-07 Nikhil Bansal , Ola Svensson , Luca Trevisan

We initiate the study of spectral sparsification for instances of Constraint Satisfaction Problems (CSPs). In particular, we introduce a notion of the \emph{spectral energy} of a fractional assignment for a Boolean CSP instance, and define…

Data Structures and Algorithms · Computer Science 2025-04-24 Sanjeev Khanna , Aaron Putterman , Madhu Sudan

Pioneered by Benczur and Karger for cuts in graphs [STOC'96], sparsification is a fundamental topic with wide-ranging applications that has been studied, e.g., for graphs and hypergraphs, in a combinatorial and a spectral setting, and with…

Data Structures and Algorithms · Computer Science 2023-02-08 Jannik Kudla , Stanislav Živný

We introduce a notion of code sparsification that generalizes the notion of cut sparsification in graphs. For a (linear) code $\mathcal{C} \subseteq \mathbb{F}_q^n$ of dimension $k$ a $(1 \pm \epsilon)$-sparsification of size $s$ is given…

Data Structures and Algorithms · Computer Science 2023-11-03 Sanjeev Khanna , Aaron L Putterman , Madhu Sudan

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…

Data Structures and Algorithms · Computer Science 2020-09-11 Yu Chen , Sanjeev Khanna , Ansh Nagda

A valued constraint satisfaction problem (VCSP) instance $(V,\Pi,w)$ is a set of variables $V$ with a set of constraints $\Pi$ weighted by $w$. Given a VCSP instance, we are interested in a re-weighted sub-instance $(V,\Pi'\subset \Pi,w')$…

Data Structures and Algorithms · Computer Science 2015-09-08 Arnold Filtser , Robert Krauthgamer

In this paper, we consider the problem of designing cut sparsifiers and sketches for directed graphs. To bypass known lower bounds, we allow the sparsifier/sketch to depend on the balance of the input graph, which smoothly interpolates…

Data Structures and Algorithms · Computer Science 2021-05-13 Ruoxu Cen , Yu Cheng , Debmalya Panigrahi , Kevin Sun

A classic result in graph theory, due to Batson, Spielman, and Srivastava (STOC 2009) shows that every graph admits a $(1 \pm \varepsilon)$ cut (or spectral) sparsifier which preserves only $O(n / \varepsilon^2)$ reweighted edges. However,…

Data Structures and Algorithms · Computer Science 2025-08-12 Jun-Ting Hsieh , Daniel Z. Lee , Sidhanth Mohanty , Aaron Putterman , Rachel Yun Zhang

Graph sparsification has been studied extensively over the past two decades, culminating in spectral sparsifiers of optimal size (up to constant factors). Spectral hypergraph sparsification is a natural analogue of this problem, for which…

Data Structures and Algorithms · Computer Science 2021-06-07 Michael Kapralov , Robert Krauthgamer , Jakab Tardos , Yuichi Yoshida

The problem of sparsifying a graph or a hypergraph 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 $n$-vertex…

Data Structures and Algorithms · Computer Science 2021-06-22 Yu Chen , Sanjeev Khanna , Ansh Nagda

We consider effective preconditioners for solving Laplacians of general weighted graphs. Theoretically, spectral sparsifiers (SSs) provide preconditioners of optimal computational complexity. However, they are not easy to use for real-world…

Numerical Analysis · Mathematics 2022-08-31 Xiaozhe Hu , Junyuan Lin

We study resistance sparsification of graphs, in which the goal is to find a sparse subgraph (with reweighted edges) that approximately preserves the effective resistances between every pair of nodes. We show that every dense regular…

Data Structures and Algorithms · Computer Science 2015-06-26 Michael Dinitz , Robert Krauthgamer , Tal Wagner

Spectral hypergraph sparsification, a natural generalization of the well-studied spectral sparsification notion on graphs, has been the subject of intensive research in recent years. In this work, we consider spectral hypergraph…

Data Structures and Algorithms · Computer Science 2025-02-04 Gramoz Goranci , Ali Momeni

The problem of CSP sparsification asks: for a given CSP instance, what is the sparsest possible reweighting such that for every possible assignment to the instance, the number of satisfied constraints is preserved up to a factor of $1 \pm…

Data Structures and Algorithms · Computer Science 2026-02-12 Joshua Brakensiek , Venkatesan Guruswami , Aaron Putterman

Cut and spectral sparsification of graphs have numerous applications, including e.g. speeding up algorithms for cuts and Laplacian solvers. These powerful notions have recently been extended to hypergraphs, which are much richer and may…

Data Structures and Algorithms · Computer Science 2021-04-13 Michael Kapralov , Robert Krauthgamer , Jakab Tardos , Yuichi Yoshida

In this paper, we present a construction of a `matching sparsifier', that is, a sparse subgraph of the given graph that preserves large matchings approximately and is robust to modifications of the graph. We use this matching sparsifier to…

Data Structures and Algorithms · Computer Science 2018-11-08 Sepehr Assadi , Aaron Bernstein

The Kadison-Singer Conjecture, as proved by Marcus, Spielman, and Srivastava (MSS) [Ann. Math. 182, 327-350 (2015)], has been informally thought of as a strengthening of Batson, Spielman, and Srivastava's theorem that every undirected graph…

Data Structures and Algorithms · Computer Science 2024-01-09 Phevos Paschalidis , Ashley Zhuang
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