Class EsauWilliamsCapacitatedMinimumSpanningTree<V,E>
- java.lang.Object
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- org.jgrapht.alg.spanning.AbstractCapacitatedMinimumSpanningTree<V,E>
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- org.jgrapht.alg.spanning.EsauWilliamsCapacitatedMinimumSpanningTree<V,E>
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- Type Parameters:
V
- the vertex typeE
- the edge type
- All Implemented Interfaces:
CapacitatedSpanningTreeAlgorithm<V,E>
public class EsauWilliamsCapacitatedMinimumSpanningTree<V,E> extends AbstractCapacitatedMinimumSpanningTree<V,E>
Implementation of a randomized version of the Esau-Williams heuristic, a greedy randomized adaptive search heuristic (GRASP) for the capacitated minimum spanning tree (CMST) problem. It calculates a suboptimal CMST. The original version can be found in L. R. Esau and K. C. Williams. 1966. On teleprocessing system design: part II a method for approximating the optimal network. IBM Syst. J. 5, 3 (September 1966), 142-147. DOI=http://dx.doi.org/10.1147/sj.53.0142 This implementation runs in polynomial time O(|V|^3).This implementation is a randomized version described in Ahuja, Ravindra K., Orlin, James B., and Sharma, Dushyant, (1998). New neighborhood search structures for the capacitated minimum spanning tree problem, No WP 4040-98. Working papers, Massachusetts Institute of Technology (MIT), Sloan School of Management.
This version runs in polynomial time dependent on the number of considered operations per iteration
numberOfOperationsParameter
(denoted by p), such that runs is in $O(|V|^3 + p|V|) = O(|V|^3)$ since $p \leq |V|$.A Capacitated Minimum Spanning Tree (CMST) is a rooted minimal cost spanning tree that satisfies the capacity constrained on all trees that are connected to the designated root. The problem is NP-hard.
- Since:
- July 12, 2018
- Author:
- Christoph GrĂ¼ne
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Nested Class Summary
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Nested classes/interfaces inherited from class org.jgrapht.alg.spanning.AbstractCapacitatedMinimumSpanningTree
AbstractCapacitatedMinimumSpanningTree.CapacitatedSpanningTreeSolutionRepresentation
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Nested classes/interfaces inherited from interface org.jgrapht.alg.interfaces.CapacitatedSpanningTreeAlgorithm
CapacitatedSpanningTreeAlgorithm.CapacitatedSpanningTree<V,E>, CapacitatedSpanningTreeAlgorithm.CapacitatedSpanningTreeImpl<V,E>
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Field Summary
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Fields inherited from class org.jgrapht.alg.spanning.AbstractCapacitatedMinimumSpanningTree
bestSolution, capacity, demands, graph, root
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description CapacitatedSpanningTreeAlgorithm.CapacitatedSpanningTree<V,E>
getCapacitatedSpanningTree()
Computes a capacitated spanning tree.protected AbstractCapacitatedMinimumSpanningTree.CapacitatedSpanningTreeSolutionRepresentation
getSolution()
Calculates a partition representation of the capacitated spanning tree.
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Constructor Detail
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EsauWilliamsCapacitatedMinimumSpanningTree
public EsauWilliamsCapacitatedMinimumSpanningTree(Graph<V,E> graph, V root, double capacity, java.util.Map<V,java.lang.Double> weights, int numberOfOperationsParameter)
Constructs an Esau-Williams GRASP algorithm instance.- Parameters:
graph
- the graphroot
- the root of the CMSTcapacity
- the capacity constraint of the CMSTweights
- the weights of the verticesnumberOfOperationsParameter
- the parameter how many best vertices are considered in the procedure
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Method Detail
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getCapacitatedSpanningTree
public CapacitatedSpanningTreeAlgorithm.CapacitatedSpanningTree<V,E> getCapacitatedSpanningTree()
Computes a capacitated spanning tree.Returns a capacitated spanning tree computed by the Esau-Williams algorithm.
- Specified by:
getCapacitatedSpanningTree
in interfaceCapacitatedSpanningTreeAlgorithm<V,E>
- Specified by:
getCapacitatedSpanningTree
in classAbstractCapacitatedMinimumSpanningTree<V,E>
- Returns:
- a capacitated spanning tree
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getSolution
protected AbstractCapacitatedMinimumSpanningTree.CapacitatedSpanningTreeSolutionRepresentation getSolution()
Calculates a partition representation of the capacitated spanning tree. With that, it is possible to calculate the to the partition corresponding capacitated spanning tree in polynomial time by calculating the MSTs. The labels of the partition that are returned are non-negative.- Returns:
- a representation of the partition of the capacitated spanning tree that has non-negative labels.
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