New PDF release: A guide to experimental algorithmics

By Catherine C. McGeoch

ISBN-10: 0521173019

ISBN-13: 9780521173018

ISBN-10: 1107001730

ISBN-13: 9781107001732

"Computational experiments on algorithms can complement theoretical research by way of displaying what algorithms, implementations, and speed-up equipment paintings top for particular machines or difficulties. This e-book publications the reader in the course of the nuts and bolts of the key experimental questions: What should still I degree? What inputs should still I try out? How do I research the knowledge? Answering those questions wishes principles from set of rules design Read more...

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Worst-case instances provide general upper bounds; random generators that span the input space can reveal the range of possible outcomes. Real-world instances from application hot spots can highlight properties of particular interest to certain communities; algorithm-centered inputs reveal how the algorithm responds to specific input properties; and so forth. • More ambitious analyses tend to require more general input classes and tight control of parameters. When the goal is to build a model of algorithm performance in terms of input parameters, success is more likely if the inputs obey simple random models or are produced by algorithm-centered generators that allow explicit control of relevant properties, so that experimental designs can focus on the question that prompts the experiment.

And incrementing by powers of 10 n = 10, 100, 1000, . .. 9 To study trends and functions, choose design points that exploit what you already know. Making Comparisons with Factorial Designs Another common goal of algorithm research is to compare performance across several algorithm and instance factors, to discover which implementation ideas work for which inputs. These types of questions arise in horse race experiments and assessment studies. For this type of problem a full factorial design, a cornerstone of DOE, is simplest and often the best choice.

In panel (b) the vertices are reordered using the reverse vertex rule, which reverses the color groups to become (1 = blue, 2 = green, 3 = yellow, 4 = red). Next the colors are reordered according to a color rule C: assume here the random rule is applied and the new color order is: green, blue, red, yellow. Panel (c) shows the graph after recoloring, respecting the original colors. The order by which new colors are assigned to vertices is shown in the following table. 7. SIG. A simplified version of Culberson and Luo’s Iterated Greedy algorithm.

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A guide to experimental algorithmics by Catherine C. McGeoch

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