A disk graph is an intersection graph of disks in $\mathbb{R}^2$. Determining the computational complexity of finding a maximum clique in a disk graph is a long-standing open problem. In 1990, Clark, Colbourn, and Johnson gave a polynomial-time algorithm for computing a maximum clique in a unit disk graph. However, finding a maximum clique when disks are of arbitrary size is widely believed to be a challenging open problem. The problem is open even if we restrict the disks to have at most two different sizes of radii, or restrict the radii to be within $[1,1+\varepsilon]$ for some $ε>0$. In this paper, we provide a new perspective to examine adjacencies in a disk graph that helps obtain the following results. - We design an $O(2^k n^{2k} poly(n))$-time algorithm to find a maximum clique in a $n$-vertex disk graph with $k$ different sizes of radii. This is polynomial for every fixed $k$, and thus settles the open question for the case when $k=2$. - Given a set of $n$ unit disks, we show how to compute a maximum clique inside each possible axis-aligned rectangle determined by the disk centers in $O(n^5\log n)$-time. This is at least a factor of $n^{4/3}$ faster than applying the fastest known algorithm for finding a maximum clique in a unit disk graph for each rectangle independently. - We give an $O(2^kn^{2rk} poly(n,r))$-time algorithm to find a maximum clique in a $n$-vertex ball graph with $k$ different sizes of radii where the ball centers lie on $r$ parallel planes. This is polynomial for every fixed $k$ and $r$, and thus contrasts the previously known NP-hardness result for finding a maximum clique in an arbitrary ball graph.

Given a set P of points and a set U of geometric objects in the Euclidean plane, a minimum ply cover of P with U is a subset of U that covers P and minimizes the number of objects that share a common intersection, called the minimum ply cover number of P with U . Biedl et al. (2021) [9] showed that for both unit squares and unit disks, determining the minimum ply cover number for a set of points is NP-hard. They gave polynomial-time 2-approximation algorithms for the special case when the minimum ply cover number is constant, and asked whether there exists polynomial-time O ( 1 ) -approximation algorithms for these problems. In this paper, we settle the question posed by Biedl et al. by providing polynomial-time O ( 1 ) -approximation algorithms for the minimum ply cover problem for both unit squares and unit disks.

A disk graph is an intersection graph of disks in the Euclidean plane, where the disks correspond to the vertices of the graph and a pair of vertices are adjacent if and only if their corresponding disks intersect. The problem of determining the time complexity of computing a maximum clique in a disk graph is a long-standing open question that has been very well studied in the literature. The problem is known to be open even when the radii of all the disks are in the interval [1,(1+ε)], where ε > 0. If all the disks are unit disks then there exists an O(n³log n)-time algorithm to compute a maximum clique, which is the best-known running time for over a decade. Although the problem of computing a maximum clique in a disk graph remains open, it is known to be APX-hard for the intersection graphs of many other convex objects such as intersection graphs of ellipses, triangles, and a combination of unit disks and axis-parallel rectangles. Here we obtain the following results. - We give an algorithm to compute a maximum clique in a unit disk graph in O(n^2.5 log n)-time, which improves the previously best known running time of O(n³log n) [Eppstein '09]. - We extend a widely used "co-2-subdivision approach" to prove that computing a maximum clique in a combination of unit disks and axis-parallel rectangles is NP-hard to approximate within 4448/4449 ≈ 0.9997. The use of a "co-2-subdivision approach" was previously thought to be unlikely in this setting [Bonnet et al. '20]. Our result improves the previously known inapproximability factor of 7633010347/7633010348 ≈ 0.9999. - We show that the parameter minimum lens width of the disk arrangement may be used to make progress in the case when disk radii are in [1,(1+ε)]. For example, if the minimum lens width is at least 0.265 and ε ≤ 0.0001, which still allows for non-Helly triples in the arrangement, then one can find a maximum clique in polynomial time.

Abstract Thep‐center problem is to locatepfacilities on a network so as to minimize the largest distance from a demand point to its nearest facility. Thep‐median problem is to locatepfacilities on a network so as to minimize the average distance from a demand point to its closest facility. We consider these problems when the network can be modeled by an interval or circular‐arc graph whose edges have unit lengths. We provide, given the interval model of annvertex interval graph, anO(n) time algorithm for the 1‐median problem on the interval graph. We also show how to solve thep‐median problem, for arbitraryp, on an interval graph inO(pnlogn) time and on a circular‐arc graph inO(pn2logn) time. We introduce a spring representation of the objective function and show how to solve thep‐center problem on a circular‐arc graph inO(pn) time, assuming that the arc endpoints are sorted. © 2002 Wiley Periodicals, Inc.

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Abstract Given a graph G = (V, E), a subgraph S = (V, Es) is a t‐spanner of G if for every edge xy ϵ E the distance between x and y in S is at most t. Spanners have applications in communication networks, distributed systems, parallel computation, and many other areas. This paper is concerned with the complexity of finding a minimum size t‐spanner in a graph with bounded degree. A linear time algorithm is presented for finding a minimum‐size 2‐spanner in any graph whose maximum degree is at most four. The algorithm is based on a graph theoretical result concerning edge partition of a graph into a “triangle‐free component” and “triangular‐components.” On the other hand, it is shown that to determine whether a graph with maximum degree at most nine contains a t‐spanner with at most K edges (K is given) is NP‐complete for any fixed t ⩾ 2. © 1994 by John Wiley & Sons, Inc.

The 1-median problem is to locate a service facility so as to minimize the sum of the distances from a given set of demand destinations. In this paper, we develop a data storage scheme which is useful for dealing with sums of rectilinear (L1) distances in space and travel distances along the edges of a tree network. We then use this scheme to provide efficient algorithms to various capacitated 1-median problems. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

We consider clustering problems under two different optimization criteria. One is to minimize the maximum intracluster distance (diameter), and the other is to maximize the minimum intercluster distance. In particular, we present an algorithm which partitions a set S of n points in the plane into two subsets so that their larger diameter is minimized in time Ο(n log n) and space Ο(n). Another algorithm with the same bounds computes a k-partition of S for any k so that the minimum intercluster distance is maximized. In both instances it is first shown that an optimal parition is determined by either a maximum or minimum spanning tree of S.

In this paper we present Ο(n2) time algorithms for the problems of covering a horizontally convex orthogonal polygon with the minimum number of orthogonal convex polygons and with the minimum number of orthogonal star-shaped polygons.

The problem of decomposing a polygon into simpler components is of interest in fields such as computational geometry, syntactic pattern recognition, and graphics. In this paper we consider decompositions which do not introduce Steiner points. The simpler components we consider are convex polygons, spiral polygons, star-shaped polygons and monotone polygons. We apply a technique for improving the efficiency of dynamic programming algorithms in order to achieve polynomial time algorithms for the problems of decomposing a simple polygon into the minimum number of each of the component types. Using the same technique we are able to exhibit polynomial time algorithms for the problems of decomposing a simple polygon into each of the component types while minimizing the length of the internal edges used to form the decomposition. When the polygons are allowed to contain holes many of the problems become NP-hard.