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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.
The RSMT is an NP-hard problem, and as with other NP-hard problems, common approaches to tackle it are approximate algorithms, heuristic algorithms, and separation of efficiently solvable special cases. An overview of the approaches to the problem may be found in the 1992 book by Hwang, Richards and Winter, The Steiner Tree Problem. [3]
Steiner tree, or Minimum spanning tree for a subset of the vertices of a graph. [2] (The minimum spanning tree for an entire graph is solvable in polynomial time.) Modularity maximization [5] Monochromatic triangle [3]: GT6 Pathwidth, [6] or, equivalently, interval thickness, and vertex separation number [7] Rank coloring; k-Chinese postman
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.
A minimum spanning tree of a weighted planar graph.Finding a minimum spanning tree is a common problem involving combinatorial optimization. Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, [1] where the set of feasible solutions is discrete or can be reduced to a discrete set.
When k is a fixed constant, the k-minimum spanning tree problem can be solved in polynomial time by a brute-force search algorithm that tries all k-tuples of vertices. However, for variable k, the k-minimum spanning tree problem has been shown to be NP-hard by a reduction from the Steiner tree problem. [1] [2]
He is known for an approximation algorithm for the minimum Steiner tree problem with an approximation ratio 1.55, [1] widely cited by his peers [2] and also widely held in libraries. [ 3 ] References
If the input points alone are used as endpoints of the network edges, then the shortest network is their minimum spanning tree. However, shorter networks can often be obtained by adding Steiner points, and using both the new points and the input points as edge endpoints. [1] Another problem that uses Steiner points is Steiner triangulation. The ...