By Catherine C. McGeoch
"Computational experiments on algorithms can complement theoretical research by means of exhibiting what algorithms, implementations, and speed-up tools paintings top for particular machines or difficulties. This booklet courses the reader throughout the nuts and bolts of the key experimental questions: What may still I degree? What inputs should still I attempt? How do I learn the knowledge? Answering those questions wishes rules from set of rules design and research, working platforms and reminiscence hierarchies, and statistics and knowledge research. The wide-ranging dialogue contains a instructional on process clocks and CPU timers, a survey of thoughts for tuning algorithms and knowledge buildings, a cookbook of tools for producing random combinatorial inputs, and an illustration of variance relief suggestions. various case reviews and examples express how you can practice those recommendations. all of the helpful strategies in computing device structure and information research are coated in order that the booklet can be utilized through an individual who has taken a path or in information buildings and algorithms. A spouse site, AlgLab (www.cs.amherst. edu/ccm/alglab) comprises downloadable records, courses, and instruments to be used in projects"-- Read more...
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Additional resources for A guide to experimental algorithmics
This property often holds in the case of solution quality – the color count produced by Greedy is the same no matter what test platform is used – or when the performance indicator is an abstract cost (such as loop iteration counts). Fixing a parameter that matters will narrow the scope of the experiment and may reduce the generality of results. This is especially true of categorical parameters – if you only measure option (a), you cannot comment on options (b) and (c). If the parameter is numeric, it may be possible to ﬁx it at an extreme value to obtain upper or lower bounds on costs or to interpolate a line between measurements at extreme points.
5. How much time do they take on average, as a function of n and m (and I )? Are they competitive with state-of-the-art GC algorithms? On what types of inputs are they most and least effective? How does I affect the trade-off between time and color count in Random? assignColor(c,v)? Each of these questions can be attacked via experiments – but each is best answered with a different experiment. For example, question 1 should be studied Cambridge Books Online © Cambridge University Press, 2012 20 2 A Plan of Attack by measuring time performance on random graphs, with a wide range of n, m values to evaluate function growth best.
Replicate tests on different platforms to check for numerical precision errors; then swap out the random number generator and run the tests again. As a safeguard against spurious results, before concluding that “A causes B,” replicate the experiment with A absent to check whether B still occurs – and if it does, abandon the conclusion. Ideally the pilot and workhorse programs should be implemented by two different people. The pilot code should represent a straightforward, no-frills version of the algorithm, while the workhorse may incorporate speedups (Chapter 4) and experimental tricks (Chapter 6) for fast turnaround.