Catherine A. Schevon

Optimization by simulated annealing: an experimental evaluation. Part I, graph partitioning

In this and two companion papers, we report on an extended empirical study of the simulated annealing approach to combinatorial optimization proposed by S. Kirkpatrick et al. That study investigated how best to adapt simulated annealing to particular problems and compared its performance to that of more traditional algorithms. This paper (Part I) discusses annealing and our parameterized generic implementation of it, describes how we adapted this generic algorithm to the graph partitioning problem, and reports how well it compared to standard algorithms like the Kernighan-Lin algorithm. (For sparse random graphs, it tended to outperform Kernighan-Lin as the number of vertices become large, even when its much greater running time was taken into account. It did not perform nearly so well, however, on graphs generated with a built-in geometric structure.) We also discuss how we went about optimizing our implementation, and describe the effects of changing the various annealing parameters or varying the basic annealing algorithm itself.

Optimization by simulated annealing: an experimental evaluation; part II, graph coloring and number partitioning

This is the second in a series of three papers that empirically examine the competitiveness of simulated annealing in certain well-studied domains of combinatorial optimization. Simulated annealing is a randomized technique proposed by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi for improving local optimization algorithms. Here we report on experiments at adapting simulated annealing to graph coloring and number partitioning, two problems for which local optimization had not previously been thought suitable. For graph coloring, we report on three simulated annealing schemes, all of which can dominate traditional techniques for certain types of graphs, at least when large amounts of computing time are available. For number partitioning, simulated annealing is not competitive with the differencing algorithm of N. Karmarkar and R. M. Karp, except on relatively small instances. Moreover, if running time is taken into account, natural annealing schemes cannot even outperform multiple random runs of the local optimization algorithms on which they are based, in sharp contrast to the observed performance of annealing on other problems.

Shaping a distributed-RC line to minimize Elmore delay

Eulers differential equation of the calculus of variations is used to determine the shape of a distributed-RC wire that minimizes Elmore delay. In two dimensions the optimal shape is an exponential taper. In three dimensions the optimal shape is a frustum of a cone.

Computing the geodesic diameter of a 3-polytope

We present an <italic>&Ogr;</italic>(<italic>n</italic><supscrpt>14</supscrpt> log <italic>n</italic>) algorithm for computing the geodesic diameter of a 3-polytope of <italic>n</italic> vertices. The geodesic diameter is the greatest separation between two points on the surface, where distance is determined by the shortest (geodesic) path between two points. We assume a model of computation that permits finding roots of a one-variable polynomial of fixed degree in constant time. The key geometric result underlying the algorithm is that, although it may be that neither endpoint of the diameter is a vertex of the polytope, when this occurs, there must be at least five distinct equal-length paths between the diameter endpoints.

On the development of closed convex curves on 3-polytopes

It is shown that a closed convex polygonal curve on the surface of a 3-polytope develops in the plane to a simple path: it does not self-intersect.