Computational geometry: an introduction
Computational geometry: an introduction
A faster approximation algorithm for the Steiner problem in graphs
Information Processing Letters
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Steiner's problem in graphs: heuristic methods
Discrete Applied Mathematics - Special issue: combinatorial methods in VLSI
Improved algorithms for the Steiner problem in networks
Discrete Applied Mathematics - Special issue on the combinatorial optimization symposium
A Hybrid GRASP with Perturbations for the Steiner Problem in Graphs
INFORMS Journal on Computing
Applicability of group communication for increased scalability in MMOGs
NetGames '06 Proceedings of 5th ACM SIGCOMM workshop on Network and system support for games
Approaches to the Steiner Problem in Networks
Algorithmics of Large and Complex Networks
A distributed primal-dual heuristic for steiner problems in networks
WEA'07 Proceedings of the 6th international conference on Experimental algorithms
Algorithm engineering: bridging the gap between algorithm theory and practice
Algorithm engineering: bridging the gap between algorithm theory and practice
Efficient congestion mitigation using congestion-aware steiner trees and network coding topologies
VLSI Design - Special issue on CAD for Gigascale SoC Design and Verification Solutions
Dealing with large hidden constants: engineering a planar steiner tree PTAS
Journal of Experimental Algorithmics (JEA)
Fast local search for the steiner problem in graphs
Journal of Experimental Algorithmics (JEA)
Contraction-based steiner tree approximations in practice
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
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Some of the most widely used constructive heuristics for the Steiner Problem in Graphs are based on algorithms for the Minimum Spanning Tree problem. In this paper, we examine efficient implementations of heuristics based on the classic algorithms by Prim, Kruskal, and Bor驴vka. An extensive experimental study indicates that the theoretical worst-case complexity of the algorithms give little information about their behavior in practice. Careful implementation improves average computation times not only significantly, but asymptotically. Running times for our implementations are within a small constant factor from that of Prim's algorithm for the Minimum Spanning Tree problem, suggesting that there is little room for improvement.