For every d ≥ 3 and k ∈ {2} ∪ [3,∞), we determine the smallest ε such that every fractional (k + ε)-precoloring of vertices at mutual distance at least d of a graph G with fractional chromatic number equal to k can be extended to a proper fractional (k + ε)-coloring of G. Our work complements the analogous results of Albertson for ordinary colorings and those of Albertson and West for circular ...

Let X be a Banach space with closed unit ball B. Given k ∈ N, X is said to be k-β, repectively, (k + 1)-nearly uniformly convex ((k + 1)-NUC), if for every ε > 0, there exists δ, 0 < δ < 1, so that for every x ∈ B, and every ε-separated sequence (xn) ⊆ B, there are indices (ni) k i=1, respectively, (ni) k+1 i=1 , such that 1 k+1 ‖x + ∑k i=1 xni‖ ≤ 1 − δ, respectively, 1 k+1 ‖ ∑k+1 i=1 xni‖ ≤ 1−...

For a graph G with n vertices and m edges, we give a randomized Las Vegas algorithm that approximates a small balanced vertex separator of G in almost linear time. More precisely, we show the following, for any 2 3 ≤ α < 1 and any 0 < ε < 1 − α: If G contains an α-separator of size K, then our algorithm finds an (α + ε)-separator of size O(ε−1K2 log n) in time O(ε−1K3m log n) w.h.p. In particul...

We investigate algorithms to find the first vertex in large trees generated by either the uniform attachment or preferential attachment model. We require the algorithm to output a set of K vertices, such that, with probability at least 1 − ε, the first vertex is in this set. We show that for any ε, there exist such algorithms with K independent of the size of the input tree. Moreover, we provid...

We describe a polynomial time O(k log log OPTk(P ))approximation algorithm for the k-guarding problem of finding a minimum number, OPTk(P ), of vertex guards of an n-vertex simple polygon P so that for every point p ∈ P , the number of guards that see p is at least the minimum of k and the number of vertices that see p. Our approach finds O ( k ε log log 1 ε ) size (k, ε)-nets for instances of ...

Metric clustering is a fundamental primitive in machine learning with several applications for mining massive datasets. An important example of metric the k-center problem. While this problem has been extensively studied distributed settings, all previous algorithms use Ω(k) space per and Ω(n k) total work. In paper, we develop first highly scalable approximation algorithm clustering, O~(n^ε) O...

We prove that the sum of the squared Euclidean distances from the n rows of an n×d matrix A to any compact set that is spanned by k vectors in R can be approximated up to (1+ε)-factor, for an arbitrary small ε > 0, using the O(k/ε)-rank approximation of A and a constant. This implies, for example, that the optimal k-means clustering of the rows of A is (1+ε)approximated by an optimal k-means cl...