Metric k-center

In graph theory, the metric k-center or metric facility location problem is a combinatorial optimization problem studied in theoretical computer science. Given n cities with specified distances, one wants to build k warehouses in different cities and minimize the maximum distance of a city to a warehouse. In graph theory this means finding a set of k vertices for which the largest distance of any point to its closest vertex in the k-set is minimum. The vertices must be in a metric space, providing a complete graph that satisfies the triangle inequality.

Formal definition

Let ${\displaystyle (X,d)}$ be a metric space where ${\displaystyle X}$ is a set and ${\displaystyle d}$ is a metric
A set ${\displaystyle \mathbf {V} \subseteq {\mathcal {X}}}$, is provided together with a parameter ${\displaystyle k}$. The goal is to find a subset ${\displaystyle {\mathcal {C}}\subseteq \mathbf {V} }$ with ${\displaystyle |{\mathcal {C}}|=k}$ such that the maximum distance of a point in ${\displaystyle \mathbf {V} }$ to the closest point in ${\displaystyle {\mathcal {C}}}$ is minimized. The problem can be formally defined as follows:
For a metric space (${\displaystyle {\mathcal {X}}}$,d),

• Input: a set ${\displaystyle \mathbf {V} \subseteq {\mathcal {X}}}$, and a parameter ${\displaystyle k}$.
• Output: a set ${\displaystyle {\mathcal {C}}}$ of ${\displaystyle k}$ points.
• Goal: Minimize the cost ${\displaystyle r^{\mathcal {C}}(\mathbf {V} )={\underset {v\in V}{\max }}}$ d(v,${\displaystyle {\mathcal {C}}}$)

That is, Every point in a cluster is in distance at most ${\displaystyle r^{\mathcal {C}}(V)}$ from its respective center. [1]

The k-Center Clustering problem can also be defined on a complete undirected graph G = (V, E) as follows:
Given a complete undirected graph G = (V, E) with distances d(v_i, v_j) ∈ N satisfying the triangle inequality, find a subset C ⊆ V with |C| = k while minimizing:

${\displaystyle \max _{v\in V}\min _{c\in C}d(v,c)}$

Computational complexity

In a complete undirected graph G = (V, E), if we sort the edges in nondecreasing order of the distances: d(e₁) ≤ d(e₂) ≤ … ≤ d(e_m) and let G_i = (V, E_i), where E_i = {e₁, e₂, …, e_i}. The k-center problem is equivalent to finding the smallest index i such that G_i has a dominating set of size at most k. [2]

Although Dominating Set is NP-complete, the k-center problem remains NP-hard. This is clear, since the optimality of a given feasible solution for the k-center problem can be determined through the Dominating Set reduction only if we know in first place the size of the optimal solution (i.e. the smallest index i such that G_i has a dominating set of size at most k) , which is precisely the difficult core of the NP-Hard problems.

Approximations

A simple greedy algorithm

A simple greedy approximation algorithm that achieves an approximation factor of 2 builds ${\displaystyle {\mathcal {C}}}$ using a farthest-first traversal in k iterations. This algorithm simply chooses the point farthest away from the current set of centers in each iteration as the new center. It can be described as follows:

• Pick an arbitrary point ${\displaystyle {\bar {c}}_{1}}$ into ${\displaystyle C_{1}}$
• For every point ${\displaystyle v\in \mathbf {V} }$ compute ${\displaystyle d_{1}[v]}$ from ${\displaystyle {\bar {c}}_{1}}$
• Pick the point ${\displaystyle {\bar {c}}_{2}}$ with highest distance from ${\displaystyle {\bar {c}}_{1}}$.
• Add it to the set of centers and denote this expanded set of centers as ${\displaystyle C_{2}}$. Continue this till k centers are found

Running time

• The i^th iteration of choosing the i^th center takes ${\displaystyle {\mathcal {O}}(n)}$ time.
• There are k such iterations.
• Thus, overall the algorithm takes ${\displaystyle {\mathcal {O}}(nk)}$ time.[3]

Proving the approximation factor

The solution obtained using the simple greedy algorithm is a 2-approximation to the optimal solution. This section focuses on proving this approximation factor.

Given a set of n points ${\displaystyle \mathbf {V} \subseteq {\mathcal {X}}}$,belonging to a metric space (${\displaystyle {\mathcal {X}}}$,d), the greedy K-center algorithm computes a set K of k centers, such that K is a 2-approximation to the optimal k-center clustering of V.

i.e. ${\displaystyle r^{\mathbf {K} }(\mathbf {V} )\leq 2r^{opt}(\mathbf {V} ,{\textit {k}})}$ [1]

This theorem can be proven using two cases as follows,

Case 1: Every cluster of ${\displaystyle {\mathcal {C}}_{opt}}$ contains exactly one point of ${\displaystyle \mathbf {K} }$

• Consider a point ${\displaystyle v\in \mathbf {V} }$
• Let ${\displaystyle {\bar {c}}}$ be the center it belongs to in ${\displaystyle {\mathcal {C}}_{opt}}$
• Let ${\displaystyle {\bar {k}}}$ be the center of ${\displaystyle \mathbf {K} }$ that is in ${\displaystyle \Pi ({\mathcal {C}}_{opt},{\bar {c}})}$
• ${\displaystyle d(v,{\bar {c}})=d(v,{\mathcal {C}}_{opt})\leq r^{opt}(\mathbf {V} ,k)}$
• Similarly, ${\displaystyle d({\bar {k}},{\bar {c}})=d({\bar {k}},{\mathcal {C}}_{opt})\leq r^{opt}}$
• By the triangle inequality: ${\displaystyle d(v,{\bar {k}})\leq d(v,{\bar {c}})+d({\bar {c}},{\bar {k}})\leq 2r^{opt}}$

Case 2: There are two centers ${\displaystyle {\bar {k}}}$ and ${\displaystyle {\bar {u}}}$ of ${\displaystyle \mathbf {K} }$ that are both in ${\displaystyle \Pi ({\mathcal {C}}_{opt},{\bar {c}})}$, for some ${\displaystyle {\bar {c}}\in {\mathcal {C}}_{opt}}$ (By pigeon hole principle, this is the only other possibility)

• Assume, without loss of generality, that ${\displaystyle {\bar {u}}}$ was added later to the center set ${\displaystyle \mathbf {K} }$ by the greedy algorithm, say in i^th iteration.
• But since the greedy algorithm always chooses the point furthest away from the current set of centers, we have that ${\displaystyle {\bar {c}}\in {\mathcal {C}}_{i-1}}$and,

${\displaystyle {\begin{aligned}r^{\mathbf {K} }(\mathbf {V} )\leq r^{{\mathcal {C}}_{i-1}}(\mathbf {V} )&=d({\bar {u}},{\mathcal {C}}_{i-1})\\&\leq d({\bar {u}},{\bar {k}})\\&\leq d({\bar {u}},{\bar {c}})+d({\bar {c}},{\bar {k}})\\&\leq 2r^{opt}\end{aligned}}}$ [1]

Another 2-factor approximation algorithm

Another algorithm with the same approximation factor takes advantage of the fact that the k-center problem is equivalent to finding the smallest index i such that G_i has a dominating set of size at most k and computes a maximal independent set of G_i, looking for the smallest index i that has a maximal independent set with a size of at least k. [4] It is not possible to find an approximation algorithm with an approximation factor of 2 − ε for any ε > 0, unless P = NP. [5] Furthermore, the distances of all edges in G must satisfy the triangle inequality if the k-center problem is to be approximated within any constant factor, unless P = NP. [6]

See also

• Traveling salesman problem
• Minimum k-cut
• Dominating set
• Independent set (graph theory)
• Facility location problem

References

1. Har-peled, Sariel (2011). Geometric Approximation Algorithms. Boston, MA, USA: American Mathematical Society. ISBN 0821849115.
2. Vazirani, Vijay V. (2003), Approximation Algorithms, Berlin: Springer, pp. 47–48, ISBN 3-540-65367-8
3. Gonzalez, Teofilo F. (1985), "Clustering to minimize the maximum intercluster distance", Theoretical Computer Science, 38, Elsevier Science B.V., pp. 293–306, doi:10.1016/0304-3975(85)90224-5
4. Hochbaum, Dorit S.; Shmoys, David B. (1986), "A unified approach to approximation algorithms for bottleneck problems", Journal of the ACM, 33, pp. 533–550, ISSN 0004-5411
5. Hochbaum, Dorit S. (1997), Approximation Algorithms for NP-Hard problems, Boston: PWS Publishing Company, pp. 346–398, ISBN 0-534-94968-1
6. Crescenzi, Pierluigi; Kann, Viggo; Halldórsson, Magnús; Karpinski, Marek; Woeginger, Gerhard (2000), "Minimum k-center", A Compendium of NP Optimization Problems

Further reading

• Hochbaum, Dorit S.; Shmoys, David B. (1985), "A Best Possible Heuristic for the k-Center Problem", Mathematics of Operations Research, 10, pp. 180–184