Abstract 1 Introduction 2 Preliminaries 3 The Algorithm for (𝒕,𝒌)-Fair Sum-of-Radii Clustering 4 The Algorithm for Balanced Sum-of-Radii Clustering References

Polynomial-Time Constant-Approximation for Fair Sum-Of-Radii Clustering

Sina Bagheri Nezhad ORCID Portland State University, OR, USA Sayan Bandyapadhyay ORCID Portland State University, OR, USA Tianzhi Chen ORCID Portland State University, OR, USA
Abstract

In a seminal work, Chierichetti et al. [20] introduced the (t,k)-fair clustering problem: Given a set of red points and a set of blue points in a metric space, a clustering is called fair if the number of red points in each cluster is at most t times and at least 1/t times the number of blue points in that cluster. The goal is to compute a fair clustering with at most k clusters that optimizes certain objective function. Considering this problem, they designed a polynomial-time O(1)- and O(t)-approximation for the k-center and the k-median objective, respectively. Recently, Carta et al. [15] studied this problem with the sum-of-radii objective and obtained a (6+ϵ)-approximation with running time O((klog1+ϵ(k/ϵ))knO(1)), i.e., fixed-parameter tractable in k. Here n is the input size. In this work, we design the first polynomial-time O(1)-approximation for (t,k)-fair clustering with the sum-of-radii objective, improving the result of Carta et al. Our result places sum-of-radii in the same group of objectives as k-center, that admit polynomial-time O(1)-approximations. This result also implies a polynomial-time O(1)-approximation for the Euclidean version of the problem, for which an f(k)nO(1)-time (1+ϵ)-approximation was known due to Drexler et al. [24]. Here f is an exponential function of k. We are also able to extend our result to any arbitrary 2 number of colors when t=1. This matches known results for the k-center and k-median objectives in this case. The significant disparity of sum-of-radii compared to k-center and k-median presents several complex challenges, all of which we successfully overcome in our work. Our main contribution is a novel cluster-merging-based analysis technique for sum-of-radii that helps us achieve the constant-approximation bounds.

Keywords and phrases:
fair clustering, sum-of-radii clustering, approximation algorithms
Copyright and License:
[Uncaptioned image] © Sina Bagheri Nezhad, Sayan Bandyapadhyay, and Tianzhi Chen; licensed under Creative Commons License CC-BY 4.0
2012 ACM Subject Classification:
Theory of computation Approximation algorithms analysis
Related Version:
Full Version: https://arxiv.org/abs/2504.14683 [44]
Funding:
This work was supported by the National Science Foundation under Grant No. AF 2311397.
Editors:
Anne Benoit, Haim Kaplan, Sebastian Wild, and Grzegorz Herman

1 Introduction

Given a set of points P in a metric space (Ω,d) and an integer k>0, the task of clustering is to find a partition X1,,Xk of P into k groups or clusters such that each group has similar points. The similarity of the clusters is typically modeled using an objective function which is to be minimized. In this work, we focus on the sum-of-radii objective, which is defined as the sum of the radii of k balls that contain the points of the respective k clusters. The sum-of-radii objective, while also center-based, has a different flavor from objectives such as k-center, k-median, and k-means, as it directly sums the radii of the clusters rather than measuring distances from each point to its assigned center. In these objectives, k representative points (or cluster centers) are chosen, and the corresponding clusters are formed by assigning the points of P to their nearest centers. Such a partition is popularly known as the Voronoi partition. It is not hard to see that an optimal sum-of-radii clustering is not necessarily a Voronoi partition. The study of sum-of-radii was motivated by the idea that it could reduce the so-called dissection effect that is observed in k-center type objectives (see attached full version for details).

Sum-of-radii clustering is known to be NP-hard even in planar metrics and metrics of constant doubling dimension [30]. Consequently, it has received substantial attention from the approximation algorithms community. Charikar and Panigrahy [16] designed a Primal-Dual and Lagrangian-relaxation-based 3.504-approximation algorithm that runs in polynomial time (poly-time). Recently, using similar techniques, Friggstad and Jamshidian [26] improved the approximation factor to 3.389. The best-known approximation factor for sum-of-radii in polynomial time is 3+ϵ for any ϵ>0, due to Buchem et al. [14]. In stark contrast to other well-studied center-based objectives such as k-center and k-median, the sum-of-radii objective admits a QPTAS [30], which is based on a randomized metric partitioning scheme. Additionally, the problem can be solved exactly in polynomial time in the Euclidean metric of constant dimension [31]. The problem also admits polynomial time exact algorithms in other restricted settings, such as when singleton clusters are not allowed [9] and the metric is unweighted [33].

In recent years, sum-of-radii clustering has also been studied with additional constraints. One such popular constraint is the capacity constraint, which puts restriction on the number of points that each cluster can contain. In a series of articles [35, 8, 36, 25], O(1)-approximation algorithms have been designed for capacitated sum-of-radii with running time fixed-parameter tractable (FPT) in k (i.e., f(k)nO(1) for a function f of k), culminating in an approximation factor of 3. Inamdar and Varadarajan [35] studied sum-of-radii with a matroid constraint where the set of centers of the balls must be an independent set of a matroid. They obtain an FPT 9-approximation for this problem. The approximation factor has recently been improved to 3 by Chen et al. [18]. Obtaining a poly-time O(1)-approximation for any of these constrained versions is an interesting open question. However, poly-time O(1)-approximations are known for sum-of-radii with lower bounds and with outliers [3, 14].

Sum-of-radii has also been studied with fairness constraints, which is the main focus of our work. Clustering with fairness constraints or fair clustering stems from the idea that protected groups (defined based on a sensitive feature, e.g., gender) must be well-represented in each cluster. In recent years, fair clustering has received significant attention from researchers across several areas of computer science. In a seminal work, Chierichetti et al. [20] introduced the (t,k)-fair clustering problem. In this problem, we are given a set P1 of red points, a set P2 of blue points, that together contain n points, and an integer balance parameter t1. A clustering is called (t,k)-fair if, for any cluster X, the number of red points in X is at least 1/t times and at most t times the number of blue points in X. We say that each cluster in a (t,k)-fair clustering is t-balanced.

Chierichetti et al. studied (t,k)-fair clustering with k-center and k-median objectives, and obtained poly-time 4- and O(t)-approximation, respectively. Since then obtaining a poly-time O(1)-approximation for (t,k)-fair median or means remained an intriguing open question. The main challenge in this case is that the optimal clusterings are no longer Voronoi partitions, as they also need to be (t,k)-fair. Subsequently, (t,k)-fair median/means has been studied in a plethora of works. The only setting where it is known to obtain a poly-time O(1)-approximation is when t=1 [12], that is for (1,k)-fair median/means. These problems have also been considered in the Euclidean case [46, 5].

The (t,k)-fair median/means problem has also been studied with an arbitrary number of groups. The algorithm of Böhm et al. [12] for t=1 also yields a poly-time O(1)-approximation in this case. Note that for t=1, a cluster contains the same number of points from all groups. Bandyapadhyay et al. [6] obtained a poly-time approximation for (t,k)-fair median with a factor that depends on t, , and k. Bercea et al. [11] and Bera et al. [10] independently defined a generalization of (t,k)-fair clustering. There we are given balance parameters αi,βi[0,1] for each group 1i. A clustering is called fair representational if the fraction of points from group i in every cluster is at least αi and at most βi for all 1i. They show that it is possible to obtain poly-time bi-criteria type O(1)-approximations where we are allowed to violate the fairness constraints by an additive small constant factor. Subsequently, Dai et al. [23] designed a DP-based poly-time O(logk)-approximation for this problem. For groups, their running time is nO(l).

Carta et al. [15] studied fair versions of sum-of-radii. In particular, they study a more general class of mergeable constraints. A clustering constraint is called mergeable if the union of two clusters satisfying the constraint also satisfies the constraint. They show that the fairness constraints defined in (t,k)-fair clustering and fair representational clustering are mergeable. In their work, they obtained a (6+ϵ)-approximation for sum-of-radii with mergeable constraints. In particular, for the above two fairness constraints, their run time is O((klog1+ϵ(k/ϵ))knO(1)), so FPT in k. The algorithm iteratively guesses the next cluster based on a k-center completion problem leading to the FPT run time. Their approximation factor improves to 3+ϵ when t=1. Drexler et al. [24] obtained an FPT (1+ϵ)-approximation for Euclidean sum-of-radii with mergeable constraints. Chen et al. [18] studied a fair version, which is a special case of matroid sum-of-radii, and hence obtained an FPT 3-approximation. A summary of the results on fair clustering under various objectives is provided in Table 1.

As mentioned before, for fair representational models, only bi-criteria type O(1)-approximations are known for k-center/median/means, even with two groups. As we focus on our theoretical quest of designing poly-time O(1)-approximations fully satisfying the fairness constraints, we study (t,k)-fair sum-of-radii. In light of the above discussion, we state the following two questions.

Question 1:

Does (t,k)-fair sum-of-radii (with two groups) admit a poly-time constant-approximation algorithm?

Question 2:

Does (1,k)-fair sum-of-radii with an arbitrary 2 number of groups admit a poly-time constant-approximation algorithm?

After the work of Chierichetti et al. [20], several other notions of fairness have been considered in the context of clustering problems. The following is a sample of these works grouped by the fairness notions: individual fairness [38, 43, 47, 13, 1], proportional fairness [19, 42], fair center representation [17, 40, 39, 21, 34], colorful [7, 37, 4], and min-max fairness [2, 28, 41, 22, 29, 32].

Table 1: Summary of approximation results for fair clustering under various objectives. “Poly” denotes polynomial time; “FPT” denotes fixed-parameter tractable in k; is the number of groups.
Objective Fairness Type Approximation Time Reference
k-Center (t,k) (2 groups) 4 Poly [20]
k-Median (t,k) (2 groups) O(t) Poly [20]
k-Median (1,k) ( groups) O(1) Poly [12]
k-Median (t,k) ( groups) f(t,,k) Poly [6]
k-Median / Center Representational (bi-criteria) O(1) Poly [10, 11]
Sum-of-Radii Unconstrained 3+ε Poly [14]
Sum-of-Radii Capacitated 3 FPT [25]
Sum-of-Radii Matroid constraint 3 FPT [18]
Sum-of-Radii (t,k) (2 groups) 6+ε FPT [15]
Sum-of-Radii (1,k) ( groups) 3+ε FPT [15]
Sum-of-Radii (t,k) (2 groups) 𝟏𝟒𝟒+ε Poly This work
Sum-of-Radii (1,k) ( groups) 𝟏𝟖𝟎+ε Poly This work

1.1 Our Contributions and Techniques

In our work, we prove two theorems resolving Questions 1 and 2 in the affirmative. First, we prove the following theorem.

Theorem 1.

There is a polynomial-time (144+ϵ)-approximation algorithm for (t,k)-fair sum-of-radii (with two groups).

Our result complements the FPT approximation result of Carta et al. [15] by achieving the first O(1)-approximation for the problem in polynomial time. The result also implies a poly-time O(1)-approximation for Euclidean (t,k)-fair sum-of-radii, for which only an FPT (1+ϵ)-approximation was known [24]. We note that our result should also be compared with that of (t,k)-fair k-median for which only O(t)-approximation is known in polynomial time. In particular, our result places sum-of-radii in the same group of objectives as k-center that admits polynomial-time O(1)-approximations. Moreover, our result shows that (t,k)-fair sum-of-radii is in contrast to most of the constrained versions of sum-of-radii, including capacitated clustering, for which only FPT O(1)-approximations are known.

Next, we give an overview of our approach. Our approximation algorithm is motivated by the algorithms for (t,k)-fair center and (t,k)-fair median [20]. These algorithms have two major steps. In the first step, a fairlet decomposition of the points in X=P1P2 is computed, i.e., a partition 𝒴={Y1,,Ym} such that for each fairlet Yi, it either has 1 red point and at most t blue points or 1 blue point and at most t red points. Let β:P1P2[m] be the function that maps each point x to the index of the fairlet that contains x. From each Yi, an arbitrary point yi is designated as its representative. In the second step, a clustering of these m representatives is computed with the respective cost function. Also, for each Yi, all of its points are assigned to the cluster that contains yi. The new clustering is obviously (t,k)-fair, as each cluster is a merger of fairlets. For the analysis of the cost of the computed clustering, they define a fairlet decomposition cost, which is used to bound the assignment cost of the points in the second step. For k-center, this cost is maxxXd(x,yβ(x)), and for k-median, it is xXd(x,yβ(x)). Indeed, both of these costs when optimal are comparable to the optimal (t,k)-fair clustering cost. For k-center, it is within a constant factor, and for k-median it is within an O(t) factor. Then, it is sufficient to compute a fairlet decomposition in the first step whose cost is within a small constant-factor of the optimal fairlet decomposition cost.

Coming back to (t,k)-fair sum-of-radii, it is not clear how to define a suitable fairlet decomposition cost that can be compared to the optimal (t,k)-fair sum-of-radii cost. In particular, such a cost needs to be defined independent of the number of clusters k. However, for sum-of-radii, the objective is the sum of radii of k clusters. For example, a natural candidate, the cost for k-median, i.e., xXd(x,yβ(x)), is likely to be much larger than the optimal sum-of-radii cost. In the absence of such a suitable fairlet decomposition cost, it is difficult to argue the increase in the assignment cost, when actual points of Yi are assigned instead of just the representative yi.

Our approach.

Our algorithm is surprisingly simple to state. We first compute a complete bi-partite graph G with P1 and P2 being the two parts. The weight of each edge is set to be the distance between the two corresponding endpoints. Subsequently, a degree-constrained, spanning subgraph of this graph is computed where each vertex has a degree in range [1,t], and the sum of the weights of the edges is minimized. Such an optimal subgraph can be computed in polynomial time using the algorithm of Gabow [27]. Moreover, one can show that such a subgraph is a collection of stars each having at most t edges. Thus, our algorithm up to this point is in a similar spirit to that of k-median. As we argued before, the total weight of such a subgraph can be very large compared to the optimal sum-of-radii cost. Our main contribution is to prove that there is a sum-of-radii clustering of the stars (or representatives of them) computed in this way whose cost is at most a constant times the optimal (t,k)-fair sum-of-radii cost. Then, one can compute an approximate sum-of-radii clustering of these stars and return the corresponding clustering of the points in P1P2. The obtained clustering is (t,k)-fair, as the clusters are disjoint union of the vertices of stars, each having at most t edges. The proof of the existence of a clustering of the computed stars whose cost is nicely bounded is based on a novel analysis technique that merges a set of optimal clusters to obtain superclusters. We give an overview in the following.

Let H be the degree-constrained subgraph computed with the minimum weight possible. Also, let 𝒞={C1,C2,,Ck} be a fixed optimal (t,k)-fair sum-of-radii clustering. We repetitively merge pairs of these clusters if there are edges in H across them. Let 𝒞^={C^1,C^2,,C^κ} be the resulting clustering. By our construction, each star of H is fully contained in one of these merged clusters or superclusters. Thus, it is sufficient to show that the radius of each supercluster C^i is at most O(1) times the sum of the radii of the associated optimal clusters whose merger is C^i. To bound such radius, we introduce a notion of minimum-switch paths between pairs of clusters. These paths play a central role in our analysis. We prove that it is possible to bound the (weighted) length of any such path by O(1) times the sum of the radii of the associated optimal clusters. Then the diameter (or radius) of the supercluster can also be bounded likewise, as any two cluster vertices are connected by a minimum-switch path. The important distinction is that the length of any arbitrary path might not be bounded in such a nice way.

Next, we prove the following theorem concerning Question 2.

Theorem 2.

There is a polynomial-time (180+ϵ)-approximation algorithm for (1,k)-fair sum-of-radii with 2 groups of points.

Again our result directly improves the FPT approximation result of Carta et al. [15] and extends to more than 2 groups. The result matches the known constant-approximation bound for k-center/median/means in this case. The proof of the above theorem is similar to the proof of Theorem 1, and so employs the same supercluster-based analysis framework. However, here we need to handle colors. The main challenge boils down to bounding the diameter of a certain multi-partite graph G1 with i=1Pi being the set of vertices. Intuitively, by the analysis for two groups, the diameter of the graphs induced by only P1Pi is nicely bounded. However, we still need to bound the diameter of G1. Consequently, we introduce an additional notion of minimum-color-switch paths. We prove that the lengths of these paths can also be bounded nicely, exploiting their special properties.

Organization.

We introduce notation in Section 2 and present our algorithms for (t,k)-fair and (1,k)-fair sum-of-radii in Sections 3 and 4, respectively. Proofs of statements marked by () are available in the full version111https://arxiv.org/pdf/2504.14683.

2 Preliminaries

In sum-of-radii clustering, we are given a set P of n points in a metric space with distance d and an integer k>0. We would like to find: (i) a subset C of P containing k points and a non-negative integer rq (called the radius) for each qC, and (ii) a function ϕ assigning each point pP to a center qC such that d(p,q)rq. The subset Xq=ϕ1(q) for each qC is called the cluster corresponding to q having radius rq. The goal is to find a clustering {XqqC} that minimizes the sum of the radii qCrq.

In (t,k)-fair sum-of-radii clustering, we are given two disjoint groups P1 (red) and P2 (blue) having n points in total in a metric space (Ω=P1P2,d) and an integer balance parameter t1. A clustering is called (t,k)-fair if, for each cluster X, the number of points from P1 in X is at least 1/t times the number of points from P2 in X and at most t times the number of points from P2 in X. The goal is to compute a (t,k)-fair clustering minimizing the sum of the radii of the clusters. Each cluster in a (t,k)-fair clustering is called t-balanced.

In Balanced sum-of-radii clustering, we are given 2 disjoint groups P1,P2,,P having n points in total in a metric space (Ω=i=1Pi,d) such that |P1|=|P2|==|P|. A clustering is called balanced if, for each cluster X, it holds that |XP1|=|XP2|==|XP|. The goal is to compute a balanced clustering that minimizes the sum of the radii of the clusters. We say that each cluster in a balanced clustering is 1-balanced.

Consider any metric space (Ω1,d1) and a subset S1Ω1. For any cluster Q and a point p, d1(p,Q)=maxqQd1(p,q). The center of Q in S1 is the point, argminpS1d1(p,Q). The radius of Q w.r.t. S1 and d1, denoted by r(S1,d1)(Q), is the distance between Q and its center in S1, i.e., r(S1,d1)(Q)=minpS1d1(p,Q). We refer to the sum of the radii, w.r.t. S1 and d1, of the clusters in any clustering 𝒞 as the cost of 𝒞 w.r.t. S1 and d1 and denote it by cost(𝒞)(S1,d1).

We note that the term “(t,k)-fairness” refers specifically to the two-color case, where each cluster must maintain a red-to-blue ratio within [1/t,t]. This notion does not naturally extend to more than two colors. In contrast, in the multi-color setting with 2 groups, we adopt the term “balanced clustering” (or “(1,k)-fair clustering with groups”) to describe the setting where each cluster must contain an equal number of points from each group. While we use similar notation for consistency, these two notions are structurally different and should be interpreted accordingly.

3 The Algorithm for (𝒕,𝒌)-Fair Sum-of-Radii Clustering

In this section, we prove Theorem 1. To set up the stage, we define the following problem.

Min-cost Degree Constrained Subgraph (Min-cost DCS).

A Degree Constrained Subgraph (DCS) H=(V,E) of a graph G=(V,E) is a subgraph such that the degree of each vertex v in H is in the range [l(v),u(v)] for given integers l(v) and u(v). Suppose we are also given a weight function w:E+{0}. A min-cost DCS H=(V,E) of G is a DCS that minimizes the sum of the weights of the edges in E over all DCS.

Proposition 3 ([27]).

Min-cost DCS can be solved in O(|V|4) time.

The proposition follows from the work of Gabow (Theorem 5.2) [27]. There the stated time complexity is O((iVui) min{|E|log|V|,|V|2}), which is O(|V|4), as each upper-bound ui can be assumed to be at most the degree of the i-th vertex. One technicality is that they study the maximization version (with real weights), but the minimization version can be solved by the standard method of negating edge-weights in min-cost DCS. Also see [45] that has similar discussions and an O(|V|6) time algorithm for min-cost DCS, which they call minimum-cost many-to-many matching with demands and capacities.

Observation 4 ().

A min-cost DCS with l(v)=1 for all vV does not contain a path of length three, and thus it is a disjoint union of star graphs.

Our algorithm is as follows.

The Algorithm

  • 1.

    Construct a graph G=(V,E) where V=P1P2 and E={{p,q}pP1,qP2}. Define the weight function w such that for each edge e={p,q}, w(e)=d(p,q). Compute a min-cost DCS H=(V,E) of G with l(v)=1 and u(v)=t for all vV.

  • 2.

    Construct an edge-weighted graph G in the following way: For each pΩ, add a vertex to G; For each star S in H, add a vertex corresponding to S to G, which we also call by S; For each p,qΩ, add the edge {p,q} to G with weight d(p,q); For all pΩ and S in H, add the edge {p,S} to G with weight maxqSd(p,q). Let d be the shortest path metric in G. Construct the metric space (Ω,d) where Ω is the subset of vertices in G corresponding to the stars in H.

  • 3.

    Compute a sum of radii clustering X={X1,,Xk} of the points in Ω using the Algorithm of Buchem et al. [14] (with Ω also being the candidate set of centers).

  • 4.

    Compute a clustering X of the points in P1P2 using X in the following way. For each cluster XiX, add the cluster pSSXi{p} to X. Return X.

Next, we analyze the algorithm. First, we have the following observations.

Observation 5 ().

X is a (t,k)-fair clustering of P1P2.

Next, we analyze the approximation factor. Let 𝒞={C1,C2,,Ck} be a fixed optimal (t,k)-fair clustering. We will prove the following lemma. Our result follows as a corollary.

Lemma 6.

Consider the clustering X of Ω constructed in Step 3 of the algorithm. Then cost(X)(Ω,d)(𝟒𝟖+ϵ)i=1kr(Ω,d)(Ci).

Corollary 7 ().

Consider the clustering X of P1P2 constructed in Step 4 of the algorithm. Then cost(X)(Ω,d)(𝟏𝟒𝟒+ϵ)i=1kr(Ω,d)(Ci). Thus, our algorithm is a (𝟏𝟒𝟒+ϵ)-approximation algorithm.

3.1 Proof of Lemma 6

In the following, we are going to prove Lemma 6. Consider the min-cost DCS H=(V,E) computed in Step 1. Also, consider the optimal clusters in 𝒞. We construct a new clustering 𝒞^={C^1,C^2,,C^κ} by merging clusters in 𝒞 in the following way, where 1κk. Initially, we set 𝒞^ to 𝒞. For each edge {p,q} of E such that pC^i,qC^j and ij, replace C^i,C^j in 𝒞^ by their union and denote it by C^i as well.

When the above merging procedure ends, by renaming the indexes, let 𝒞^={C^1,C^2,,C^κ} be the new clustering. Then, we have the following observation.

Observation 8.

Consider any star S in H. Then, for some 1iκ, all the points of S are contained in C^i.

Consider the clustering 𝒞={C1,,Cκ} of Ω defined in the following way. For each star S in H, identify the cluster C^i in 𝒞^ that contains all the points in S. By Observation 8, such an index i exists. Assign the point p in Ω corresponding to S to Ci.

Lemma 9 ().

cost(𝒞)(Ω,d)2 cost(𝒞^)(Ω,d).

We will prove the following lemma.

Lemma 10.

cost(𝒞^)(Ω,d)8i=1kr(Ω,d)(Ci).

Lemma 6 follows by Lemma 9 and 10 noting that the Algorithm of Buchem et al. [14] yields a (3+ϵ)-factor approximation to the optimal clustering (along with an appropriate scaling of ϵ). In the rest of this section, we prove Lemma 10.

3.2 Proof of Lemma 10

For simplicity of notation, we drop (Ω,d) from r(Ω,d)(.), as henceforth centers are always assumed to be in Ω and the metric to be d. Let us consider any fixed C^i, and suppose it is constructed by merging the clusters Ci1,Ci2,,Ciτ. It is sufficient to prove that r(C^i)8j=1τr(Cij). For simplicity of notation, we rename C^i by C^, and Ci1,Ci2,,Ciτ by C1,C2,,Cτ.

Let H1=(V1,E1) be the induced subgraph of H such that the vertices of V1 are in C^. We refer to a point of P1 (resp. P2) as a red (resp. blue) point. Note that the edges of H are across red and blue points. In the following, we construct an edge-weighted, directed multi-graph G=(V,E) in the following manner. G has a vertex vj corresponding to each cluster Cj, where 1jτ. There is an edge e=(vi,vj) from vi to vj for each pP1Ci and qP2Cj such that {p,q} is in E1. We refer to such an edge as a 0-edge, i.e., its parity is 0. The weight ωe of the edge e is d(p,q). Similarly, there is a 1-edge (or parity 1 edge) e=(vi,vj) from vi to vj for each pP2Ci and qP1Cj such that {p,q} is in E1. The weight ωe of the edge e is d(p,q). For each edge eiE, we denote the corresponding edge in E1 by {ri,bi}, where ri is the red point and bi is the blue point. For simplicity of exposition, we are going to make heavy use of this correspondence.

Observation 11 ().

Suppose there is a 0-edge (resp. 1-edge) (vi,vj) in E. Then there is also a 1-edge (resp. 0-edge) (vj,vi) in E.

A directed path (or simply a path) π={u1,,ul} from u1 to ul in G is a sequence of distinct vertices such that (ui,ui+1) is in G for all 1il1. We say that π contains the edges (ui,ui+1). If π contains all 0-edges (resp. 1-edges), it is called a 0-path (resp. 1-path). Two consecutive edges e1=(ui,ui+1),e2=(ui+1,ui+2) on π are said to form a switch if they have different parity. We say that the switch happens at ui+1 and it is the corresponding switching vertex. The switch is called a b-switch if the parity of e1 is b for b{0,1}. A directed cycle is formed from π by adding the edge (ul,u1) (if any) with it. The reverse path of π is the path {ul,,u1} that contains the edges (ui+1,ui) for all 1il1. Such edges exist according to Observation 11. A 0-path (resp. 1-path) in a subgraph of G starting at vi and ending at vj is called maximal if vj does not have any outgoing 0-edges (resp. 1-edges) in the subgraph.

Observation 12 ().

For any two vertices vi,vjV, there is a directed path from vi to vj in G.

Consider any two vertices vα and vβ of G. Let π={vα=u1,,ul=vβ} be a directed path from vα to vβ having the minimum number of switches, i.e., a minimum-switch path from vα to vβ.

We prove the following lemma.

Lemma 13.

eπωe6i=1τr(Ci). Moreover, if π does not have a switch, eπωe4i=1τr(Ci).

Before proving this lemma, we show how to prove Lemma 10. Consider any point p in C^j. Let pCg. Now, consider any point q in C^j that is the farthest point from p. Let qCh. By Lemma 13 it follows that, there is a path, say π, from vg to vh whose sum of the edge weights is at most 6i=1τr(Ci). Then, r(C^j)d(p,q)eπωe+vertex viπ2r(Ci)8i=1τr(Ci).

Summing over all clusters C^j in 𝒞^, we obtain Lemma 10.

3.3 Proof of Lemma 13

The overall idea is to show the existence of a subset of edges E2E, such that the set of edges (Eπ)E2 form a valid degree-constrained subgraph of G on the set of vertices P1P2. Additionally, we need that the total weight of the edges of E2 is small. Then we can show that the weight of π is also small, as H=(V,E) is a min-cost DCS of G. However, it might not be possible to remove only the edges of π from E to show the existence of such a set E2. We show that there is a subset E1E that contains the edges of π and can be removed to obtain such a valid degree-constrained subgraph. In the following, we prove that obtaining two such sets E1 and E2 is sufficient to prove Lemma 13. For a set of edges SE, let w(S)=eSw(e).

Lemma 14.

Suppose there are E1E,E2E, such that the set of edges (EE1)E2 forms a valid degree-constrained subgraph of G and w(E2)6i=1τr(Ci). Then, w(E1)6i=1τr(Ci).

Proof.

Note that H is a min-cost DCS of G. Consider the graph H induced by the set of edges (EE1)E2. By our assumption, H is a valid DCS of G. It follows that,

w(E)w((EE1)E2),or, w(E1)w(E2)6i=1τr(Ci).

The last inequality follows from our assumption.

Assuming that the conditions of the above lemma are true, we finish the proof of Lemma 13.

eπωe =(vi,vj)πωe
={p,q} corresponding to (vi,vj)πpCi,qCjw({p,q})w(E1)6i=1τr(Ci).

If π does not have a switch, then we will show that w(E2)4i=1τr(Ci). Hence, the moreover part in Lemma 13 also follows. It is left to show the existence of such E1 and E2.

3.4 Construction of 𝑬𝟏 and 𝑬𝟐

Two subgraphs G1 and G2 of G are called 0-1-edge-disjoint if for any edge eη1 of G1 and eη2 of G2, the corresponding edges in E are distinct. Thus, if G1 contains a 0-edge (vi,vj), and G1 and G2 are 0-1-edge-disjoint, then G2 cannot contain the 0-edge (vi,vj) and the 1-edge (vj,vi). Similarly, if G1 contains a 1-edge (vi,vj), and G1 and G2 are 0-1-edge-disjoint, then G2 cannot contain the 1-edge (vi,vj) and the 0-edge (vj,vi). Let j1<j2<<jλ be the indexes of the vertices on π={u1,,ul} where the switches occur. Note that j1>1,jλ<l. Denote the switch that occurs at ujh by bh for all 1hλ (i.e., bh is the parity of (ujh1,ujh)). Let b0 be the parity of (u1,u2) and bλ+1 be the parity of (ul1,ul).

First, we consider the simple case when the parity of (ul𝟏,ul) is 0 (resp. 1) and there is a 0-path (resp. 1-path) from ul to u𝟏 in G that is 0-1-edge-disjoint from π. Let us denote the latter path by π(l). Note that π is a path having the minimum number of switches and the existence of π(l) ensures that π does not have a switch. Let U0E be the subset of edges that lie on the paths in {π}{π(l)}. Next, we define a subset E1E1 that has a one-to-one mapping with U0. In particular, consider any edge (vi,vj) in U0. Note that if it is a 0-edge, it was added due to an edge {p,q} in E1 such that pP1Ci and qP2Cj. We add the edge {p,q} to E1. Otherwise, if (vi,vj) is a 1-edge, it was added due to an edge {p,q} in E1 such that pP2Ci and qP1Cj. In this case, we add the edge {p,q} to E1.

Next, we show the construction of E2. Wlog, let us assume that π is a 0-path. The other case is symmetric. Note that then π(l) is also a 0-path as per our assumption. First, we describe the process of adding the replacement edges for the path π. Consider any intermediate vertex (if any) vj on this path. Then, there are exactly two points in Cj corresponding to the edges on π, which are of opposite colors. We add an edge between these two points in E2 (see Figure 1). Removal of the edges of E1 corresponding to π and the addition of this edge do not change the degree of the two points in Cj. Similarly, we add edges to E2 corresponding to the intermediate vertices of π(l). Next, consider the vertex ul=vi. There is an incoming 0-edge on π and an outgoing 0-edge on π(l) that are incident on vi. Thus, there are exactly two points in Ci of opposite colors corresponding to these two edges. We add an edge between these two points in E2. Removal of the edges of E1 corresponding to those two edges, and the addition of this edge does not change the degree of the two points in Ci. Similarly, consider the vertex u1=vi. There is an outgoing 0-edge on π and an incoming 0-edge on π(l) that are incident on vi. Thus, there are exactly two points in Ci of opposite colors corresponding to these two edges. We add an edge between these two points in E2. Again, the removal of the edges of E1 corresponding to those two edges, and the addition of this edge does not change the degree of the two points in Ci. See Figure 1 for an illustration.

Figure 1: Figure illustrating the construction of E2 for {π}{π(l)}. The bold (orange) edges are in E1 and the dashed (purple) edges are in E2.

By our construction, the set of edges (EE1)E2 form a valid degree-constrained subgraph of G on the set of vertices P1P2. The way we add the edges to E2, both endpoints of each edge lie in a cluster Cj such that the vertex vj corresponding to the cluster lies on a path in {π}{π(l)}. Now, vj can lie either on one such path or on two paths. Thus, we add at most two edges to E2 corresponding to vj. The sum of the weights of these two edges is at most 2 times the diameter of Cj. Hence, by Lemma 14, we obtain eπωe4i=1τr(Ci).

Next, we consider the remaining case when the parity of (ul𝟏,ul) is 0 (resp. 1) and there is no 0-path (resp. 1-path) from ul to u𝟏 in G that is 0-1-edge-disjoint from π. Thus, there is no bλ+1-path from ul to u1 in G that is 0-1-edge-disjoint from π.

Consider a path π and let vi denote its start vertex. Also, consider a cycle O such that π and O have exactly one vertex vj in common. Note that π might not have an edge, in which case vj=vi. Let D be the graph formed by the union of π and O, i.e., by gluing them together at vj. We refer to such a graph D as a hanging cycle for vi with vj being the join vertex. D is called a b-hanging cycle if all the edges of π and O are b-edges. Let p be the point in the cluster Cj corresponding to the edge of the cycle O incoming to vj. Additionally, D is called special if the degree of p in H1 is at least 2, and p is called the special point of D (see Figure 2).

Figure 2: A special hanging cycle with the special point p.

In the current case, we need the following lemmas.

Lemma 15 ().

Suppose the parity of (ul1,ul) is 0 (resp. 1), and there is no 0-path (resp. 1-path) from ul to u1 in G that is 0-1-edge-disjoint from π. Moreover, suppose there is no special 0-hanging cycle (resp. 1-hanging cycle) in G for ul that is 0-1-edge-disjoint from π. Then, there exists a 0-path (resp. 1-path) π1 in G from ul to a vertex vj, such that π1 is 0-1-edge-disjoint from π and one of the following is true: (i) the degree of bη (resp. rη) in H1 is at least 2, where eη is the last edge on π1 if it has an edge or (ul1,ul) otherwise; or (ii) Cj has a red (resp. blue) point whose degree in H1 is at most t1.

Lemma 16 ().

Suppose the parity of (u1,u2) is 1 (resp. 0), and there is no 1-path (resp. 0-path) from ul to u1 in G that is 0-1-edge-disjoint from π. Moreover, suppose there is no special 0-hanging cycle (resp. 1-hanging cycle) in G for u1 that is 0-1-edge-disjoint from π. Then, there exists a 0-path (resp. 1-path) π1 from u1 to a vertex vj, such that π1 is 0-1-edge-disjoint from π and one of the following is true: (i) the degree of bη (resp. rη) in H1 is at least 2, where eη is the last edge on π1 if it has an edge or (u2,u1) otherwise, and (ii) Cj has a red (resp. blue) point whose degree in H1 is at most t1.

Next, we apply the above lemmas to show the construction of E1 and E2. Recall that in this case there is no bλ+𝟏-path from ul to u𝟏 in G that is 0-1-edge-disjoint from π. If π has a switch, then there is no 0-path or 1-path from ul to u1 in G that is 0-1-edge-disjoint from π. Otherwise, it must be that b0=bλ+1, and hence by our assumption, there is no b0-path from ul to u1 in G that is 0-1-edge-disjoint from π. We conclude that in this case (b0=bλ+1), there is no b0-path from ul to u1 in G that is 0-1-edge-disjoint from π. Then, by Lemma 16 it follows that, either there is a (1b0)-hanging cycle for u1 in G 0-1-edge-disjoint from π, or a (1b0)-path starting from u1 in G with special properties. This is true, as the parity of (u1,u2) is b0. We denote this structure by π(0). Additionally, if π(0) is a path, we call bη (resp. rη) an anchor point if its degree in H1 is at least 2. Similarly, by Lemma 15 it follows that, either there is a bλ+1-hanging cycle for ul in G 0-1-edge-disjoint from π, or a bλ+1-path starting from ul in G with special properties. This is true, as (ul1,ul) is a bλ+1-edge. We denote this structure by π(λ+1). Additionally, if π(λ+1) is a path, we call bη (resp. rη) an anchor point if its degree in H1 is at least 2.

Now, if 𝟏𝐛𝟎𝐛λ+𝟏, then π(0) and π(λ+1) must be vertex-disjoint. If they are not vertex-disjoint, there exists a bλ+1-path from ul to u1 in G that is 0-1-edge-disjoint from π: take the edges on π(λ+1) from ul to a common vertex and the reverse of π(0), from the common vertex to u1. These reverse edges have parity opposite of 1b0, i.e., the same as bλ+1. But, by our assumption, such a bλ+1-path does not exist. Hence, π(0) and π(λ+1) are vertex-disjoint.

In the other case, 𝟏𝐛𝟎=𝐛λ+𝟏. We note that if π has no switch, b0=bλ+1. Thus, if 1b0=bλ+1, then we can safely assume that π has at least one switch. In this case, suppose there are a (1b0)-hanging cycle for u1 and a bλ+1-hanging cycle for ul in G, such that both are 0-1-edge-disjoint, each of the hanging cycles is 0-1-edge-disjoint from π, and either the special vertices of both are distinct or the special points are the same and the degree of that point in H1 is at least 3. Then, we take the hanging cycle for u1 as π(0) and the one for ul as π(λ+1). Otherwise, if there is a (1b0)-hanging cycle for u1 in G 0-1-edge-disjoint from π or a bλ+1-hanging cycle for ul in G 0-1-edge-disjoint from π, we consider one of those. Assume that the former holds. The other case is symmetric. We take such a hanging cycle for u1 as π(0). Then, one can prove that there is a bλ+1-path from ul in G with special properties (Lemma 8 in the full version). We take this bλ+1-path as π(λ+1). Otherwise, there is neither a (1b0)-hanging cycle for u1 in G 0-1-edge-disjoint from π nor a bλ+1-hanging cycle for ul in G 0-1-edge-disjoint from π. Then, one can prove that there are two 0-1-edge-disjoint paths with parity 1b0=bλ+1, from u1 and ul, respectively, such that both are also 0-1-edge-disjoint from π (Lemma 9 in the full version). In this case, we take the path from u1 as π(0) and the path from ul as π(λ+1).

For all 1hλ, if there are two 0-1-edge-disjoint bh-hanging cycles for ujh in G that are 0-1-edge-disjoint from π and have distinct special points or the same special point of degree at least 3 in H1, denote them by π1(h) and π2(h). Otherwise, if there is one bh-hanging cycle for ujh in G that is 0-1-edge-disjoint from π, denote it by π1(h). Now, (ujh1,ujh) is a bh-edge and (ujh,ujh+1) is a (1bh)-edge. Then, one can prove that there is a bh-path starting from ujh in G with special properties (Lemma 8 in the full version). Denote this path by π2(h). Otherwise, there is no bh-hanging cycle for ujh in G that is 0-1-edge-disjoint from π. In this case, one can prove that there are two bh-paths starting from ujh in G with special properties. Denote them by π1(h) and π2(h). Note that in all the cases, π(), π1() or π2() can either be a path or a hanging cycle.

Construction of 𝑬𝟏.

Let UE be the subset of edges that lie on the structures in 𝒮=i=1λ({π1(i)}{π2(i)}){π(0),π(λ+1),π}. Next, we define the subset E1 of E that has a one-to-one mapping with U. In particular, consider any edge (vi,vj) in U. Note that if it is a 0-edge, it was added due to an edge {p,q} in E1 such that pP1Ci and qP2Cj. We add the edge {p,q} to E1. Otherwise, if (vi,vj) is a 1-edge, it was added due to an edge {p,q} in E1 such that pP2Ci and qP1Cj. We again add the edge {p,q} to E1.

Our proof is completed by the following two lemmas.

Lemma 17 ().

There is a subset of edges E2E, such that the set of edges (EE1)E2 form a valid degree-constrained subgraph of G on the set of vertices P1P2.

4 The Algorithm for Balanced Sum-of-Radii Clustering

In this section, we prove Theorem 2. Recall that we are given disjoint groups P1,,P having n points in total in a metric space (Ω=i=1Pi,d), such that |P1|=|P2|==|P|.

Our algorithm is as follows.

The Algorithm

  • 1.

    For each 2i, construct a graph Gi=(Vi,Ei) where Vi=P1Pi and Ei={{p,q}pP1,qPi}. Define the weight function wi such that for each edge e={p,q}, wi(e)=d(p,q). Compute a minimum-weight (w.r.t. wi) perfect matching Mi of Gi. For each pP1, let Sp be the union of {p} and the points from P2,,P that are matched to p in M=i=2Mi.

  • 2.

    Construct an edge-weighted graph G in the following way: For each pΩ, add a vertex to G; For each pP1, add a vertex corresponding to Sp to G, which we also call by Sp; For each p,qΩ, add the edge {p,q} to G with weight d(p,q); For all pΩ and pP1, add the edge {p,Sp} to G with weight maxqSpd(p,q). Let d be the shortest path metric in G. Construct the metric space (Ω,d) where Ω is the subset of vertices {SppP1} in G.

  • 3.

    Compute a sum of radii clustering X={X1,,Xk} of the points in Ω using the Algorithm of Buchem et al. [14] (with Ω also being the candidate set of centers).

  • 4.

    Compute a clustering X of the points in i=1Pi using X in the following way. For each cluster Xi, add the cluster qSpSpXi{q} to X. Return X.

Let 𝒞={C1,C2,,Ck} be a fixed optimal balanced clustering.

We have the following lemma. Our main result follows as a corollary.

Lemma 18 ().

Consider the clustering X of Ω constructed in Step 3 of the algorithm. Then cost(X)(Ω,d)(𝟔𝟎+ϵ)i=1kr(Ω,d)(Ci).

Corollary 19 ().

Consider the clustering X of i=1Pi constructed in Step 3 of the algorithm. Then cost(X)(Ω,d)(𝟏𝟖𝟎+ϵ)i=1kr(Ω,d)(Ci). Thus, our algorithm is a (𝟏𝟖𝟎+ϵ)-approximation algorithm.

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