Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 165 tok/s
Gemini 2.5 Pro 47 tok/s Pro
GPT-5 Medium 28 tok/s Pro
GPT-5 High 24 tok/s Pro
GPT-4o 112 tok/s Pro
Kimi K2 208 tok/s Pro
GPT OSS 120B 466 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Approximating the Weighted Minimum Label $s$-$t$ Cut Problem (2011.06204v1)

Published 12 Nov 2020 in cs.DS

Abstract: In the weighted (minimum) {\sf Label $s$-$t$ Cut} problem, we are given a (directed or undirected) graph $G=(V,E)$, a label set $L = {\ell_1, \ell_2, \dots, \ell_q }$ with positive label weights ${w_\ell}$, a source $s \in V$ and a sink $t \in V$. Each edge edge $e$ of $G$ has a label $\ell(e)$ from $L$. Different edges may have the same label. The problem asks to find a minimum weight label subset $L'$ such that the removal of all edges with labels in $L'$ disconnects $s$ and $t$. The unweighted {\sf Label $s$-$t$ Cut} problem (i.e., every label has a unit weight) can be approximated within $O(n{2/3})$, where $n$ is the number of vertices of graph $G$. However, it is unknown for a long time how to approximate the weighted {\sf Label $s$-$t$ Cut} problem within $o(n)$. In this paper, we provide an approximation algorithm for the weighted {\sf Label $s$-$t$ Cut} problem with ratio $O(n{2/3})$. The key point of the algorithm is a mechanism to interpret label weight on an edge as both its length and capacity.

Citations (2)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.