A bi-criteria approximation algorithm for kk Means

We consider the classical $k$-means clustering problem in the setting bi-criteria approximation, in which an algoithm is allowed to output $\beta k > k$ clusters, and must produce a clustering with cost at most $\alpha$ times the to the cost of the optimal set of $k$ clusters. We argue that this approach is natural in many settings, for which the exact number of clusters is a priori unknown, or unimportant up to a constant factor. We give new bi-criteria approximation algorithms, based on linear programming and local search, respectively, which attain a guarantee $\alpha(\beta)$ depending on the number $\beta k$ of clusters that may be opened. Our gurantee $\alpha(\beta)$ is always at most $9 + \epsilon$ and improves rapidly with $\beta$ (for example: $\alpha(2)<2.59$, and $\alpha(3) < 1.4$). Moreover, our algorithms have only polynomial dependence on the dimension of the input data, and so are applicable in high-dimensional settings

Cluster-Exact Approximation of Spin Glass Groundstates

We present an algorithm which calculates groundstates of Ising spin glasses approximately. It works by randomly selecting clusters of spins which exhibit no frustrations. The spins which were not selected, contribute to the local fields of the selected spins. For the spin--cluster a groundstate is exactly calaculated by using graphtheoretical methods. The other spins remain unchanged. This procedure is repeated many times resulting in a state with low energy. The total time complexity of this scheme is approximately cubic. We estimate that the groundstate energy density of the infinite system for the +/- J model is -1.400 +/- 0.005 (2d) and -1.766 +/- 0.002 (3d). The distribution of overlaps for selected systems is calculated in order to characterize the algorithm.Comment: 13 pages, LaTeX (including figures in LaTeX-format

Approximate Clustering via Metric Partitioning

In this paper we consider two metric covering/clustering problems - \textit{Minimum Cost Covering Problem} (MCC) and $k$-clustering. In the MCC problem, we are given two point sets $X$ (clients) and $Y$ (servers), and a metric on $X \cup Y$. We would like to cover the clients by balls centered at the servers. The objective function to minimize is the sum of the $\alpha$-th power of the radii of the balls. Here $\alpha \geq 1$ is a parameter of the problem (but not of a problem instance). MCC is closely related to the $k$-clustering problem. The main difference between $k$-clustering and MCC is that in $k$-clustering one needs to select $k$ balls to cover the clients. For any \eps > 0, we describe quasi-polynomial time (1 + \eps) approximation algorithms for both of the problems. However, in case of $k$-clustering the algorithm uses (1 + \eps)k balls. Prior to our work, a $3^{\alpha}$ and a ${c}^{\alpha}$ approximation were achieved by polynomial-time algorithms for MCC and $k$-clustering, respectively, where $c > 1$ is an absolute constant. These two problems are thus interesting examples of metric covering/clustering problems that admit (1 + \eps)-approximation (using (1+\eps)k balls in case of $k$-clustering), if one is willing to settle for quasi-polynomial time. In contrast, for the variant of MCC where $\alpha$ is part of the input, we show under standard assumptions that no polynomial time algorithm can achieve an approximation factor better than $O(\log |X|)$ for $\alpha \geq \log |X|$.Comment: 19 page

Constant-Factor FPT Approximation for Capacitated k-Median

Capacitated k-median is one of the few outstanding optimization problems for which the existence of a polynomial time constant factor approximation algorithm remains an open problem. In a series of recent papers algorithms producing solutions violating either the number of facilities or the capacity by a multiplicative factor were obtained. However, to produce solutions without violations appears to be hard and potentially requires different algorithmic techniques. Notably, if parameterized by the number of facilities k, the problem is also W[2] hard, making the existence of an exact FPT algorithm unlikely. In this work we provide an FPT-time constant factor approximation algorithm preserving both cardinality and capacity of the facilities. The algorithm runs in time 2^O(k log k) n^O(1) and achieves an approximation ratio of 7+epsilon