The vertex k-center problem is a classical NP-hard problem in computer science. It has application in facility location and clustering. Basically, the vertex k-center problem models the following real problem: given a city with facilities, find the best facilities where to build fire stations. Since firemen must attend any emergency as quickly as possible, the distance from the farthests facility to its nearest fire station has to be as small as possible. In other words, the position of the fire stations must be such that every possible fire is attended as quickly as possible.
Formal definition
The vertex k-center problem is a classical NP-Hard problem in computer science. It was first proposed by Hakimi in 1964. Formally, the vertex k-center problem consists in: given a complete undirected graph in a metric space, and a positive integer, find a subset such that and the objective function is minimized. The distance is defined as the distance from the vertex to its nearest center in.
If, the vertex k-center problem can not be solved in polynomial time. However, there are some polynomial time algorithms that get near optimal solutions. Specifically, 2-approximated solutions. Actually, if the best possible solution that can be achieved by a polynomial time algorithm is a 2-approximated one. In the context of a minimization problem, such as the vertex k-center problem, a 2-approximated solution is any solution such that, where is the size of an optimal solution. An algorithm that guarantees to generate 2-approximated solutions is known as a 2-approximation algorithm. The main 2-approximated algorithms for the vertex k-center problem reported in the literature are the Sh algorithm, the HS algorithm, and the Gon algorithm. Even though these algorithms are the best possible ones, their performance on most benchmark datasets is very deficient. Because of this, many heuristics and metaheuristics have been developed through the time. Contrary to common sense, one of the most practical heuristics for the vertex k-center problem is based on the CDS algorithm, which is a 3-approximation algorithm
The Sh algorithm
Formally characterized by David Shmoys in 1995, the Sh algorithm takes as input a complete undirected graph, a positive integer, and an assumption on what the optimal solution size is. The Sh algorithm works as follows: selects the first center at random. So far, the solution consists of only one vertex,. Next, selects center at random from the set containing all the vertices whose distance from is greater than. At this point,. Finally, selects the remaining centers the same way was selected. The complexity of the Sh algorithm is, where is the number of vertices.
The HS algorithm
Proposed by Dorit Hochbaum and David Shmoys in 1985, the HS algorithm takes the Sh algorithm as basis. By noticing that the value of must equals the cost of some edge in, and since there are edges in, the HS algorithm basically repeats the Sh algorithm with every edge cost. The complexity of the HS algorithm is. However, by running a binary search over the ordered set of edge costs, its complexity is reduced to.
The Gon algorithm
Proposed independently by Teofilo Gonzalez, and by Martin Dyer and Alan Frieze in 1985, the Gon algorithm is basically a more powerful version of the Sh algorithm. While the Sh algorithm requires a guess on, the Gon algorithm prescinds from such guess by noticing that if any set of vertices at distance greater than exists, then the farthest vertex must be inside such set. Therefore, instead of computing at each iteration the set of vertices at distance greater than and then selecting a random vertex, the Gon algorithm simply selects the farthest vertex from every partial solution. The complexity of the Gon algorithm is, where is the number of vertices.
The CDS algorithm
Proposed by García Díaz et al. in 2017, the CDS algorithm is a 3-approximation algorithm that takes ideas from the Gon algorithm, the HS algorithm, and the relationship between the vertex k-center problem and the Dominating Set problem. The CDS algorithm has a complexity of. However, by performing a binary search over the ordered set of edge costs, a more efficiente heuristic named CDSh is proposed. The CDSh algorithm complexity is. Despite the suboptimal performance of the CDS algorithm, and the heuristic performance of CDSh, both present a much better performance than the Sh, HS, and Gon algorithms.
Experimental comparison
Some of the most widely used benchmark datasets for the vertex k-center problem are the pmed instances from OR-Lib., and some instances from TSP-Lib. Table 1 shows the mean and standard deviation of the experimental approximation factors of the solutions generated by each algorithm over the 40 pmed instances from OR-Lib
Algorithm
Complexity
HS
1.532
0.175
Gon
1.503
0.122
CDSh
1.035
0.031
CDS
1.020
0.027
Polynomial heuristics
Greedy pure algorithm
The greedy pure algorithm follows the core idea of greedy algorithms: to take optimal local decisions. In the case of the vertex k-center problem, the optimal local decision consists in selecting each center in such a way that the size of the solution is minimum at each iteration. In other words, the first center selected is the one that solves the 1-center problem. The second center selected is the one that, along with the previous center, generates a solution with minimum covering radius. The remaining centers are selected the same way. The complexity of the Gr algorithm is. The empirical performance of the Gr algorithm is poor on most benchmark instances.
The Scoring algorithm was introduced by Jurij Mihelič and Borut Robič in 2005. This algorithm takes advantage of the reduction from the vertex k-center problem to the minimum dominating set problem. The problem is solved by pruning the input graph with every possible value of the optimal solution size and then solving the minimum dominating set problem heuristically. This heuristic follows the lazy principle, which takes every decision as slow as possible. The complexity of the Scr algorithm is. The empirical performance of the Scr algorithm is very good on most benchmark instances. However, its running time rapidly becomes unpractical as the input grows. So, it seems to be a good algorithm only for small instances.
