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Main Authors: Ostrovsky, Rafail, Rabani, Yuval, Tov, Yoav Siman
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2507.19178
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author Ostrovsky, Rafail
Rabani, Yuval
Tov, Yoav Siman
author_facet Ostrovsky, Rafail
Rabani, Yuval
Tov, Yoav Siman
contents We give polynomial time logarithmic approximation guarantees for the budget minimization, as well as for the profit maximization versions of minimum spanning tree interdiction. In this problem, the goal is to remove some edges of an undirected graph with edge weights and edge costs, so as to increase the weight of a minimum spanning tree. In the budget minimization version, the goal is to minimize the total cost of the removed edges, while achieving a desired increase $Δ$ in the weight of the minimum spanning tree. An alternative objective within the same framework is to maximize the profit of interdiction, namely the increase in the weight of the minimum spanning tree, subject to a budget constraint. There are known polynomial time $O(1)$ approximation guarantees for a similar objective (maximizing the total cost of the tree, rather than the increase). However, the guarantee does not seem to apply to the increase in cost. Moreover, the same techniques do not seem to apply to the budget version. Our approximation guarantees are motivated by studying the question of minimizing the cost of increasing the minimum spanning tree by any amount. We show that in contrast to the budget and profit problems, this version of interdiction is polynomial time-solvable, and we give an efficient algorithm for solving it. The solution motivates a graph-theoretic relaxation of the NP-hard interdiction problem. The gain in minimum spanning tree weight, as a function of the set of removed edges, is super-modular. Thus, the budget problem is an instance of minimizing a linear function subject to a super-modular covering constraint. We use the graph-theoretic relaxation to design and analyze a batch greedy-based algorithm.
format Preprint
id arxiv_https___arxiv_org_abs_2507_19178
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Budget and Profit Approximations for Spanning Tree Interdiction
Ostrovsky, Rafail
Rabani, Yuval
Tov, Yoav Siman
Data Structures and Algorithms
We give polynomial time logarithmic approximation guarantees for the budget minimization, as well as for the profit maximization versions of minimum spanning tree interdiction. In this problem, the goal is to remove some edges of an undirected graph with edge weights and edge costs, so as to increase the weight of a minimum spanning tree. In the budget minimization version, the goal is to minimize the total cost of the removed edges, while achieving a desired increase $Δ$ in the weight of the minimum spanning tree. An alternative objective within the same framework is to maximize the profit of interdiction, namely the increase in the weight of the minimum spanning tree, subject to a budget constraint. There are known polynomial time $O(1)$ approximation guarantees for a similar objective (maximizing the total cost of the tree, rather than the increase). However, the guarantee does not seem to apply to the increase in cost. Moreover, the same techniques do not seem to apply to the budget version. Our approximation guarantees are motivated by studying the question of minimizing the cost of increasing the minimum spanning tree by any amount. We show that in contrast to the budget and profit problems, this version of interdiction is polynomial time-solvable, and we give an efficient algorithm for solving it. The solution motivates a graph-theoretic relaxation of the NP-hard interdiction problem. The gain in minimum spanning tree weight, as a function of the set of removed edges, is super-modular. Thus, the budget problem is an instance of minimizing a linear function subject to a super-modular covering constraint. We use the graph-theoretic relaxation to design and analyze a batch greedy-based algorithm.
title Budget and Profit Approximations for Spanning Tree Interdiction
topic Data Structures and Algorithms
url https://arxiv.org/abs/2507.19178