Clustering is a fundamental tool in data mining. It partitions points into groups (clusters) and may be used to make decisions for each point based on its group. However, this process may harm protected (minority) classes if the clustering algorithm does not adequately represent them in desirable clusters -- especially if the data is already biased. At NIPS 2017, Chierichetti et al. proposed a model for fair clustering requiring the representation in each cluster to (approximately) preserve the global fraction of each protected class. Restricting to two protected classes, they developed both a 4-approximation for the fair $k$-center problem and a $O(t)$-approximation for the fair $k$-median problem, where $t$ is a parameter for the fairness model. For multiple protected classes, the best known result is a 14-approximation for fair $k$-center. We extend and improve the known results. Firstly, we give a 5-approximation for the fair $k$-center problem with multiple protected classes. Secondly, we propose a relaxed fairness notion under which we can give bicriteria constant-factor approximations for all of the classical clustering objectives $k$-center, $k$-supplier, $k$-median, $k$-means and facility location. The latter approximations are achieved by a framework that takes an arbitrary existing unfair (integral) solution and a fair (fractional) LP solution and combines them into an essentially fair clustering with a weakly supervised rounding scheme. In this way, a fair clustering can be established belatedly, in a situation where the centers are already fixed.

Abstract Potential‐based flows are an extension of classical network flows in which the flow on an arc is determined by the difference of the potentials of its incident nodes. Such flows are unique and arise, for example, in energy networks. Two important algorithmic problems are to determine whether there exists a feasible flow and to maximize the flow between two designated nodes. We show that these problems can be solved for the single source and sink case by reducing the network to a single arc. However, if we additionally consider switches that allow to force the flow to 0 and decouple the potentials, these problems are NP‐hard. Nevertheless, for particular series‐parallel networks, one can use algorithms for the subset sum problem. Moreover, applying network presolving based on generalized series‐parallel structures allows to significantly reduce the size of realistic energy networks.

In the Steiner Forest problem, we are given a graph and a collection of source-sink pairs, and the goal is to find a subgraph of minimum total length such that all pairs are connected. The problem is APX-Hard and can be 2-approximated by, e.g., the elegant primal-dual algorithm of Agrawal, Klein, and Ravi from 1995. We give a local-search-based constant-factor approximation for the problem. Local search brings in new techniques to an area that has for long not seen any improvements and might be a step towards a combinatorial algorithm for the more general survivable network design problem. Moreover, local search was an essential tool to tackle the dynamic MST/Steiner Tree problem, whereas dynamic Steiner Forest is still wide open. It is easy to see that any constant factor local search algorithm requires steps that add/drop many edges together. We propose natural local moves which, at each step, either (a) add a shortest path in the current graph and then drop a bunch of inessential edges, or (b) add a set of edges to the current solution. This second type of moves is motivated by the potential function we use to measure progress, combining the cost of the solution with a penalty for each connected component. Our carefully-chosen local moves and potential function work in tandem to eliminate bad local minima that arise when using more traditional local moves. Our analysis first considers the case where the local optimum is a single tree, and shows optimality w.r.t. moves that add a single edge (and drop a set of edges) is enough to bound the locality gap. For the general case, we show how to "project" the optimal solution onto the different trees of the local optimum without incurring too much cost (and this argument uses optimality w.r.t. both kinds of moves), followed by a tree-by-tree argument. We hope both the potential function, and our analysis techniques will be useful to develop and analyze local-search algorithms in other contexts.

The weighted tree augmentation problem (WTAP) is a fundamental network design problem. We are given an undirected tree G = (V, E) with n = |V| nodes, an additional set of edges L called links and a cost vector . The goal is to choose a minimum cost subset S ⊆ L such that G = (V, E ∪ S) is 2-edgeconnected. In the unweighted case, that is, when we have cℓ = 1 for all ℓ ∊ L, the problem is called the tree augmentation problem (TAP). Both problems are known to be APX-hard, and the best known approximation factors are 2 for WTAP by (Frederickson and JáJá, ’81) and for TAP due to (Kortsarz and Nutov, TALG ’16). Adjashvili (SODA ’17) recently presented an ≈ 1.96418 + ε-approximation algorithm for WTAP for the case where all link costs are bounded by a constant. This is the first approximation with a better guarantee than 2 that does not require restrictions on the structure of the tree or the links. In this paper, we improve Adjiashvili's approximation to a + ε-approximation for WTAP under the bounded cost assumption. We achieve this by introducing a strong LP that combines {0, ½}-Chvátal-Gomory cuts for the standard LP for the problem with bundle constraints from Adjiashvili. We show that our LP can be solved efficiently and that it is exact for some instances that arise at the core of Adjiashvili's approach. This results in the improved performance guarantee of + ε, which is asymptotically on par with the result by Kortsarz and Nutov. Our result also is the best-known LP-relative approximation algorithm for TAP.

We study a natural generalization of the maximum weight many-to-one matching problem. We are given an undirected bipartite graph $$G= (A\, \dot{\cup }\, P, E)$$ with weights on the edges in E, and with lower and upper quotas on the vertices in P. We seek a maximum weight many-to-one matching satisfying two sets of constraints: vertices in A are incident to at most one matching edge, while vertices in P are either unmatched or they are incident to a number of matching edges between their lower and upper quota. This problem, which we call maximum weight many-to-one matching with lower and upper quotas (WMLQ), has applications to the assignment of students to projects within university courses, where there are constraints on the minimum and maximum numbers of students that must be assigned to each project. In this paper, we provide a comprehensive analysis of the complexity of WMLQ from the viewpoints of classical polynomial time algorithms, fixed-parameter tractability, as well as approximability. We draw the line between $$\textsf {NP}$$ -hard and polynomially tractable instances in terms of degree and quota constraints and provide efficient algorithms to solve the tractable ones. We further show that the problem can be solved in polynomial time for instances with bounded treewidth; however, the corresponding runtime is exponential in the treewidth with the maximum upper quota $$u_{\max }$$ as basis, and we prove that this dependence is necessary unless $$\textsf {FPT}= \textsf {W}[1]$$ . The approximability of WMLQ is also discussed: we present an approximation algorithm for the general case with performance guarantee $$u_{\max }+1$$ , which is asymptotically best possible unless $$\textsf {P}= \textsf {NP}$$ . Finally, we elaborate on how most of our positive results carry over to matchings in arbitrary graphs with lower quotas.

We study a natural generalization of the maximum weight many-to-one matching problem. We are given an undirected bipartite graph $$G= (A \dot{\cup }P, E)$$ with weights on the edges in E, and with lower and upper quotas on the vertices in P. We seek a maximum weight many-to-one matching satisfying two sets of constraints: vertices in A are incident to at most one matching edge, while vertices in P are either unmatched or they are incident to a number of matching edges between their lower and upper quota. This problem, which we call maximum weight many-to-one matching with lower and upper quotas (wmlq), has applications to the assignment of students to projects within university courses, where there are constraints on the minimum and maximum numbers of students that must be assigned to each project. In this paper, we provide a comprehensive analysis of the complexity of wmlq from the viewpoints of classic polynomial time algorithms, fixed-parameter tractability, as well as approximability. We draw the line between $$\mathsf{NP}$$ -hard and polynomially tractable instances in terms of degree and quota constraints and provide efficient algorithms to solve the tractable ones. We further show that the problem can be solved in polynomial time for instances with bounded treewidth; however, the corresponding runtime is exponential in the treewidth with the maximum upper quota $$u_{\max }$$ as basis, and we prove that this dependence is necessary unless $$\mathsf{FPT}= \mathsf{W}[1]$$ . Finally, we also present an approximation algorithm for the general case with performance guarantee $$u_{\max }+1$$ , which is asymptotically best possible unless $$\mathsf{P}= \mathsf{NP}$$ .

Flows over time are used to model many real‐world logistic and routing problems. The networks underlying such problems—streets, tracks, etc.—are inherently undirected and directions are only imposed on them to reduce the danger of colliding vehicles and similar problems. Thus, the question arises, what influence the orientation of the network has on the network flow over time problem that is being solved on the oriented network. In the literature, this is also referred to as the contraflow or lane reversal problem. We introduce and analyze the price of orientation: How much flow is lost in any orientation of the network if the time horizon remains fixed? We prove that there is always an orientation where we can still send one‐third of the flow and this bound is tight. For the special case of networks with a single source or sink, this fraction is half, which is again tight. We present more results of similar flavor and also show nonapproximability results for finding the best orientation for single and multicommodity maximum flows over time. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 66(3), 196–209 2015