Abstructqew algorithms for transmission scheduling in multi-hop broadcast radio networks are presented. Both link scheduling and broadcast scheduling are considered. In each instance, sched-uling algorithms are given that improve upon existing algorithms both theoretically and experimentally. Theoretically, it is shown that tree networks can be scheduled optimally, and that arbitrary networks can be scheduled so that the schedule is bounded by a length that is proportional to a function of the network thickness times the optimum. Previous algorithms could guarantee only that the schedules were bounded by a length no worse than the maximum node degree times optimum. Since the thickness is typically several orders of magnitude less than the maximum node degree, the algorithms presented here represent a considerable theoretical improvement. Experimentally, a realistic model of a radio network is given and the performance of the new algorithms is studied. These results show that, for both types of scheduling, the new algorithms (experimentally) perform consistently better than earlier methods.

. The graph realization problem is that of computing the relative locations of a set of vertices placed in Euclidean space, relying only upon some set of inter-vertex distance measurements. This paper is concerned with the closely related problem of determining whether or not a graph has a unique realization. Both these problems are NP-hard, but the proofs rely upon special combinations of edge lengths. If we assume the vertex locations are unrelated then the uniqueness question can be approached from a purely graph theoretic angle that ignores edge lengths. This paper identifies three necessary graph theoretic conditions for a graph to have a unique realization in any dimension. Efficient sequential and NC algorithms are presented for each condition, although these algorithms have very different flavors in different dimensions. 1. Introduction. Consider a graph G = (V; E) consisting of a set of n vertices and m edges, along with a real number associated with each edge. Now try to assi...

Many important macroscopic properties of materials depend upon the number of microscopic degrees of freedom. The task of counting the number of such degrees of freedom can be computationally very expensive. We describe a new approach for this calculation which is appropriate for two dimensional, glass-like networks, building upon recent work in graph rigidity. This purely combinatorial algorithm is formulated in terms of a simple pebble game. It has allowed for the first studies of the rigidity transition in generic networks, which are models of amorphous materials and glasses. In the context of generic rigidity percolation, we show how to calculate the number of internal degrees of freedom, identify all rigid clusters and locate the over-constrained regions. For a network of n sites the pebble game has a worst case performance of O(n 2 ). In our applications its performance scaled as n 1:15 at the rigidity transition, while away from the transition region it grew linearly. y ja...

. The molecule problem is that of determining the relative locations of a set of objects in Euclidean space relying only upon a sparse set of pairwise distance measurements. This NP--hard problem has applications in the determination of molecular conformation. The molecule problem can be naturally expressed as a continuous, global optimization problem, but it also has a rich combinatorial structure. This paper investigates how that structure can be exploited to simplify the optimization problem. In particular, we present a novel divide--and--conquer algorithm in which a large global optimization problem is replaced by a sequence of smaller ones. Since the cost of the optimization can grow exponentially with problem size, this approach holds the promise of a substantial improvement in performance. Our algorithmic development relies upon some recently published results in graph theory. We describe an implementation of this algorithm and report some results of its performance on a sample ...

A central issue in dealing with geometric constraint systems for CAD/CAM/CAE is the generation of an optimal decomposition plan that not only aids efficient solution, but also captures design intent and supports conceptual design. Though complex, this issue has evolved and crystallized over the past few years, permitting us to take the next important step: in this paper, we formalize, motivate and explain the decomposition–recombination (DR)-planning problem as well as several performance measures by which DR-planning algorithms can be analyzed and compared. These measures include: generality, validity, completeness, Church–Rosser property, complexity, best- and worst-choice approximation factors, (strict) solvability preservation, ability to deal with underconstrained systems, and ability to incorporate conceptual design decompositions specified by the designer. The problem and several of the performance measures are formally defined here for the first time—they closely reflect specific requirements of CAD/CAM applications. The clear formulation of the problem and performance measures allow us to precisely analyze and compare existing DR-planners that use two well-known types of decomposition methods: SR (constraint shape recognition) and MM (generalized maximum matching) on constraint graphs. This analysis additionally serves to illustrate and provide intuitive substance to the newly formalized measures. In Part II of this article, we use the new performance measures to guide the development of a new DR-planning algorithm which excels with respect to these performance measures. c ○ 2001 Academic Press 1.

The problem of finding an implicit representation for a graph such that vertex adjacency can be tested quickly is fundamental to all graph algorithms. In particular, it is possible to represent sparse graphs on n vertices using O(n) space such that vertex adjacency is tested in O(1) time. We show here how to construct such a representation efficiently by providing simple and optimal algorithms, both in a sequential and a parallel setting. Our sequential algorithm runs in O(n) time. The parallel algorithm runs in O(log n) time using O(n=log n) CRCW PRAM processors, or in O(log n log n) time using O(n = log n log

Abstract—We provide an overview of link scheduling algorithms

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