Dynamic Sketching for Graph Optimization Problems with Applications to Cut-Preserving Sketches
Sepehr Assadi, Sanjeev Khanna, Yang Li, Val Tannen

TL;DR
This paper introduces a new dynamic sketching model for graph optimization problems, providing tight bounds for maximum matching and applications to cut-preserving sparsifiers, advancing sublinear graph algorithms.
Contribution
It defines the dynamic sketching model, establishes tight bounds for maximum matching sketches, and constructs cut-preserving vertex sparsifiers with improved bounds.
Findings
Existence of a compact $O(k^2)$ size sketch for maximum matching.
Any such matching sketch requires $ ilde{ ext{Omega}}(k^2)$ size.
Construction of cut-preserving vertex sparsifiers with $O(kC^2)$ space.
Abstract
In this paper, we introduce a new model for sublinear algorithms called \emph{dynamic sketching}. In this model, the underlying data is partitioned into a large \emph{static} part and a small \emph{dynamic} part and the goal is to compute a summary of the static part (i.e, a \emph{sketch}) such that given any \emph{update} for the dynamic part, one can combine it with the sketch to compute a given function. We say that a sketch is \emph{compact} if its size is bounded by a polynomial function of the length of the dynamic data, (essentially) independent of the size of the static part. A graph optimization problem in this model is defined as follows. The input is a graph and a set of terminals; the edges between the terminals are the dynamic part and the other edges in are the static part. The goal is to summarize the graph into a compact sketch…
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