On the Discrete Unit Disk Cover Problem

Abstract. Given a set P of n points and a set D of m unit disks on a 2-dimensional plane, the discrete unit disk cover (DUDC) problem is (i) to check whether each point in P is covered by at least one disk in D or not and (ii) if so, then find a minimum cardinality subset D ∗ ⊆ D such that unit disks in D ∗ cover all the points in P. The discrete unit disk cover problem is a geometric version of the general set cover problem which is NP-hard [14]. The general set cover problem is not approx-imable within c log |P|, for some constant c, but the DUDC problem was shown to admit a constant factor approximation. In this paper, we pro-vide an algorithm with constant approximation factor 18. The running time of the proposed algorithm is O(n log n+m logm+mn). The previ-ous best known tractable solution for the same problem was a 22-factor approximation algorithm with running time O(m2n4).

Approximation Algorithms with Bounded Performance Guarantees for the Clustered Traveling Salesman Problem

Let G = (V, E) be a complete undirected graph with vertex set V , edge set E, and edge weights l(e) satisfying triangle inequality. The vertex set V is partitioned into clusters V 1 , ..., V k . The clustered traveling salesman problem (CTSP) is to compute a shortest Hamiltonian cycle (tour) that visits all the vertices, and in which the vertices of each cluster are visited consecutively. Since this problem is a generalization of the traveling salesman problem, it is NP-hard. In this paper, we consider several variants of this basic problem and provide polynomial time approximation algorithms for them