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Main Authors: Cong, Yu, Tian, Kangyi
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2604.23334
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author Cong, Yu
Tian, Kangyi
author_facet Cong, Yu
Tian, Kangyi
contents Motivated by the FPTAS for connectivity interdiction of Huang et al. (IPCO'24), we isolate the part of the argument that does not use cuts. The setting is a minimization problem over a feasible-set family $\mathcal F$ with a linear objective $w(S)=\sum_{e\in S}w(e)$. After dualizing the interdiction budget, deletion can be absorbed into truncated weights $w_λ(e)=\min\{w(e),λc(e)\}$. At an optimal Lagrange multiplier $λ^*$, the unknown optimal interdiction witness is a strict $2$-approximate minimizer of the reweighted problem. Thus an exact algorithm can be obtained whenever one can optimize $w_{λ^*}$ over $\mathcal F$, enumerate all its $2$-approximate minimizers, and solve the remaining knapsack problem.
format Preprint
id arxiv_https___arxiv_org_abs_2604_23334
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A Note on Interdiction of Linear Minimization Problems
Cong, Yu
Tian, Kangyi
Data Structures and Algorithms
Motivated by the FPTAS for connectivity interdiction of Huang et al. (IPCO'24), we isolate the part of the argument that does not use cuts. The setting is a minimization problem over a feasible-set family $\mathcal F$ with a linear objective $w(S)=\sum_{e\in S}w(e)$. After dualizing the interdiction budget, deletion can be absorbed into truncated weights $w_λ(e)=\min\{w(e),λc(e)\}$. At an optimal Lagrange multiplier $λ^*$, the unknown optimal interdiction witness is a strict $2$-approximate minimizer of the reweighted problem. Thus an exact algorithm can be obtained whenever one can optimize $w_{λ^*}$ over $\mathcal F$, enumerate all its $2$-approximate minimizers, and solve the remaining knapsack problem.
title A Note on Interdiction of Linear Minimization Problems
topic Data Structures and Algorithms
url https://arxiv.org/abs/2604.23334