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A series of papers provided approximation algorithms for the minimum Steiner tree problem with approximation ratios that improved upon the 2 − 2/t ratio. This sequence culminated with Robins and Zelikovsky's algorithm in 2000 which improved the ratio to 1.55 by iteratively improving upon the minimum cost terminal spanning tree.
A MSTST is fast to compute but is a poor approximation of the MRST. A better approximation, called the refined single trunk tree (RST-T), may be found in O(n log n) time. The idea is to replace some connections to the trunk with connections to previous connections if this is advantageous, following a simple heuristic.
Sparse approximation; Variations of the Steiner tree problem. Specifically, with the discretized Euclidean metric, rectilinear metric. The problem is known to be NP-hard with the (non-discretized) Euclidean metric. [3]: ND13 Three-dimensional Ising model [45]
Subsequently, the ε factor was removed by Rizzi [2] and a 4/3 approximation algorithm was obtained by Chakrabarty et al. [3] The same concept has been used by subsequent authors on the Steiner tree problem, e.g. [4] Robins and Zelikovsky [5] proposed an approximation algorithm for Steiner tree problem which on quasi-bipartite graphs has ...
Example of rectilinear minimum spanning tree from random points. In graph theory, the rectilinear minimum spanning tree (RMST) of a set of n points in the plane (or more generally, in ) is a minimum spanning tree of that set, where the weight of the edge between each pair of points is the rectilinear distance between those two points.
In computational complexity theory, Karp's 21 NP-complete problems are a set of computational problems which are NP-complete.In his 1972 paper, "Reducibility Among Combinatorial Problems", [1] Richard Karp used Stephen Cook's 1971 theorem that the boolean satisfiability problem is NP-complete [2] (also called the Cook-Levin theorem) to show that there is a polynomial time many-one reduction ...
The full potential of parameterized approximation algorithms is utilized when a given optimization problem is shown to admit an α-approximation algorithm running in () time, while in contrast the problem neither has a polynomial-time α-approximation algorithm (under some complexity assumption, e.g., ), nor an FPT algorithm for the given parameter k (i.e., it is at least W[1]-hard).
The optimal solutions to the Steiner tree problem and the minimum Wiener connector can differ. Define the set of query vertices Q by Q = {v 1, ..., v 10}.The unique optimal solution to the Steiner tree problem is Q itself, which has Wiener index 165, whereas the optimal solution for the minimum Wiener connector problem is Q ∪ {r 1, r 2}, which has Wiener index 142.