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Title: Stochastic Minimum Vertex Cover in General Graphs: A 3/2-Approximation
We study the stochastic vertex cover problem. In this problem, G = (V, E) is an arbitrary known graph, and G⋆ is an unknown random subgraph of G where each edge e is realized independently with probability p. Edges of G⋆ can only be verified using edge queries. The goal in this problem is to find a minimum vertex cover of G⋆ using a small number of queries. Our main result is designing an algorithm that returns a vertex cover of G⋆ with size at most (3/2+є) times the expected size of the minimum vertex cover, using only O(n/є p) non-adaptive queries. This improves over the best-known 2-approximation algorithm by Behnezhad, Blum and Derakhshan [SODA’22] who also show that Ω(n/p) queries are necessary to achieve any constant approximation. Our guarantees also extend to instances where edge realizations are not fully independent. We complement this upperbound with a tight 3/2-approximation lower bound for stochastic graphs whose edges realizations demonstrate mild correlations.  more » « less
Award ID(s):
2145898
NSF-PAR ID:
10482192
Author(s) / Creator(s):
; ;
Publisher / Repository:
ACM
Date Published:
Page Range / eLocation ID:
242 to 253
Format(s):
Medium: X
Location:
Orlando FL USA
Sponsoring Org:
National Science Foundation
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