Publicizing the event

Date taken from stamp on front page."To publicize an event like a field day or a meeting, first consider who you want to attend. If the potential audience is small, talk to each member personally, call or write a letter. You can't beat the personal approach."--First page.Joe Marks (Professor and News Director, College of Agriculture

Automating the layout of network diagrams with specified visual organization.

Network diagrams are a familiar graphic form that can express many different kinds of information. The problem of automating network-diagram layout has therefore received much attention. Previous research on network-diagram layout has focused on the problem of aesthetically optimal layout, using such criteria as the number of link crossings, the sum of all link lengths, and total diagram area. In this paper the authors propose a restatement of the network-diagram layout problem in which layout-aesthetic concerns are subordinated to perceptual-organization concerns. The authors present a notation for describing the visual organization of a network diagram. This notation is used in reformulating the layout task as a constrained-optimization problem in which constraints are derived from a visual-organization specification and optimality criteria are derived from layout-aesthetic considerations. Two new heuristic algorithms are presented for this version of the layout problem: one algorithm uses a rule-based strategy for computing a layout; the other is a massively parallel genetic algorithm. The authors demonstrate the capabilities of the two algorithms by testing them on a variety of network-diagram layout problems.Engineering and Applied Science

A viewer for PostScript documents

We describe a PostScript viewer that provides navigation and annotation functionality similar to that of paper documents using simple unified user-interface techniques.Engineering and Applied Science

An empirical study of algorithms for point feature label placement

A major factor affecting the clarity of graphical displays that include text labels is the degree to which labels obscure display features (including other labels) as a result of spatial overlap. Point-feature label placement (PFLP) is the problem of placing text labels adjacent to point features on a map or diagram so as to maximize legibility. This problem occurs frequently in the production of many types of informational graphics, though it arises most often in automated cartography. In this paper we present a comprehensive treatment of the PFLP problem, viewed as a type of combinatorial optimization problem. Complexity analysis reveals that the basic PFLP problem and most interesting variants of it are NP-hard. These negative results help inform a survey of previously reported algorithms for PFLP; not surprisingly, all such algorithms either have exponential time complexity or are incomplete. To solve the PFLP problem in practice, then, we must rely on good heuristic methods. We propose two new methods, one based on a discrete form of gradient descent, the other on simulated annealing, and report on a series of empirical tests comparing these and the other known algorithms for the problem. Based on this study, the first to be conducted, we identify the best approaches as a function of available computation time.Engineering and Applied Science

Empirical testing of algorithms for variable-sized label placement

We report an empirical comparison of different heuristic techniques for variable-sized point-feature label placement.Engineering and Applied Science

A general cartographic labeling algorithm

Some apparently powerful algorithms for automatic label placement on maps use heuristics that capture considerable cartographic expertise but are hampered by provably inefficient methods of search and optimization. On the other hand, no approach to label placement that is based on an efficient optimization technique has been applied to the production of general cartographic maps - those with labeled point, line, and area features - and shown to generate labelings of acceptable quality. We present an algorithm for label placement that achieves the twin goals of practical efficiency and high labeling quality by combining simple cartographic heuristics with effective stochastic optimization techniques.Engineering and Applied Science

Easily searched encodings for number partitioning

Can stochastic search algorithms outperform existing deterministic heuristics for the NP-hard problem Number Partitioning if given a sufficient, but practically realizable amount of time? In a thorough empirical investigation using a straightforward implementation of one such algorithm, simulated annealing, Johnson et al. (Ref. 1) concluded tentatively that the answer is negative. In this paper, we show that the answer can be positive if attention is devoted to the issue of problem representation (encoding). We present results from empirical tests of several encodings of Number Partitioning with problem instances consisting of multiple-precision integers drawn from a uniform probability distribution. With these instances and with an appropriate choice of representation, stochastic and deterministic searches can—routinely and in a practical amount of time—find solutions several orders of magnitude better than those constructed by the best heuristic known (Ref. 2), which does not employ searching. We thank David S. Johnson of AT&T Bell Labs for generously and promptly sharing his test instances. For stimulating discussions, we thank members of the Harvard Animation/Optimization Group (especially Jon Christensen), the Computer Science Department at the University of New Mexico, the Santa Fe Institute, and the Berkeley CAD Group. The anonymous referees made numerous constructive suggestions. We thank Rebecca Hayes for comments concerning the figures. The second author is grateful for a Graduate Fellowship from the Fannie and John Hertz Foundation. We thank the Free Software Foundation for making the GNU Multiple Precision package available. The research described in this paper was conducted mostly while the third author was at Digital Equipment Corporation Cambridge Research Lab. This work was supported in part by the National Science Foundation, principally under Grants IRI-9157996 and IRI-9350192 to the fourth author, and by matching grants from Digital Equipment Corporation and Xerox Corporation.Engineering and Applied Science

A seed-growth heuristic for graph bisection

We present a new heuristic algorithm for graph bisection, based on an implicit notion of clustering. We describe how the heuristic can be combined with stochastic search procedures and a postprocess application of the Kernighan-Lin algorithm. In a series of time-equated comparisons with large-sample runs of pure Kernighan-Lin, the new algorithm demonstrates significant superiority in terms of the best bisections found.Engineering and Applied Science

Easily searched encodings for number partitioning

Can stochastic search algorithms outperform existing deterministic heuristics for the NP-hard problem Number Partitioning if given a sufficient, but practically realizable amount of time? In a thorough empirical investigation using a straightforward implementation of one such algorithm, simulated annealing, Johnson et al. (Ref. 1) concluded tentatively that the answer is negative. In this paper, we show that the answer can be positive if attention is devoted to the issue of problem representation (encoding). We present results from empirical tests of several encodings of Number Partitioning with problem instances consisting of multiple-precision integers drawn from a uniform probability distribution. With these instances and with an appropriate choice of representation, stochastic and deterministic searches can—routinely and in a practical amount of time—find solutions several orders of magnitude better than those constructed by the best heuristic known (Ref. 2), which does not employ searching. We thank David S. Johnson of AT&T Bell Labs for generously and promptly sharing his test instances. For stimulating discussions, we thank members of the Harvard Animation/Optimization Group (especially Jon Christensen), the Computer Science Department at the University of New Mexico, the Santa Fe Institute, and the Berkeley CAD Group. The anonymous referees made numerous constructive suggestions. We thank Rebecca Hayes for comments concerning the figures. The second author is grateful for a Graduate Fellowship from the Fannie and John Hertz Foundation. We thank the Free Software Foundation for making the GNU Multiple Precision package available. The research described in this paper was conducted mostly while the third author was at Digital Equipment Corporation Cambridge Research Lab. This work was supported in part by the National Science Foundation, principally under Grants IRI-9157996 and IRI-9350192 to the fourth author, and by matching grants from Digital Equipment Corporation and Xerox Corporation.Engineering and Applied Science