On optimal approximability results for computing the strong metric dimension
Bhaskar DasGupta, Nasim Mobasheri

TL;DR
This paper investigates the computational complexity of the strong metric dimension problem, providing approximation algorithms and establishing hardness results under various complexity assumptions.
Contribution
It introduces a polynomial-time 2-approximation, exact algorithms with specific time bounds, and proves hardness of better approximations assuming standard complexity conjectures.
Findings
Provides a polynomial-time 2-approximation algorithm.
Develops an $O^*(2^{0.287n})$-time exact algorithm.
Shows hardness results under UGC, P≠NP, and ETH assumptions.
Abstract
The strong metric dimension of a graph was first introduced by Seb\"{o} and Tannier (Mathematics of Operations Research, 29(2), 383-393, 2004) as an alternative to the (weak) metric dimension of graphs previously introduced independently by Slater (Proc. 6th Southeastern Conference on Combinatorics, Graph Theory, and Computing, 549-559, 1975) and by Harary and Melter (Ars Combinatoria, 2, 191-195, 1976), and has since been investigated in several research papers. However, the exact worst-case computational complexity of computing the strong metric dimension has remained open beyond being NP-complete. In this communication, we show that the problem of computing the strong metric dimension of a graph of nodes admits a polynomial-time -approximation, admits a -time exact computation algorithm, admits a -time exact computation…
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