By Catherine C. McGeoch
"Computational experiments on algorithms can complement theoretical research by way of exhibiting what algorithms, implementations, and speed-up tools paintings most sensible for particular machines or difficulties. This ebook publications the reader throughout the nuts and bolts of the key experimental questions: What should still I degree? What inputs may still I attempt? How do I research the information? Answering those questions wishes rules from set of rules design and research, working structures and reminiscence hierarchies, and information and information research. The wide-ranging dialogue contains a instructional on procedure clocks and CPU timers, a survey of concepts for tuning algorithms and information buildings, a cookbook of equipment for producing random combinatorial inputs, and an illustration of variance relief options. a variety of case experiences and examples exhibit tips on how to observe those recommendations. all of the useful innovations in desktop structure and information research are coated in order that the e-book can be utilized via somebody who has taken a path or in facts constructions and algorithms. A spouse web site, AlgLab (www.cs.amherst. edu/ccm/alglab) includes downloadable records, courses, and instruments to be used in projects"-- Read more...
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Extra info for A guide to experimental algorithmics
Question 2 should be attacked by measuring both time and solution quality, using a variety of graph classes and some state-of-the art algorithms for comparison, and problem sizes that are typical in practice. An experimental design is a plan for an experiment that targets a speciﬁc question. The design speciﬁes what properties to measure, what input classes to incorporate, what input sizes to use, and so forth. Like battle plans, experimental designs may be small and tactical, suitable for reconnaissance missions, or large and strategic, for full-scale invasions.
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.
Which input parameters appear to be relevant to performance? The workhorse study comprises experiments built upon precisely stated problems: Estimate, to within 10 percent, the mean comparison costs for data structures A and B, on instances drawn randomly from input class C; bound the leading term of the (unknown) cost function F (n). Designs for workhorse experiments require some prior understanding of algorithm mechanisms and of the test environment. This understanding may be gleaned from pilot experiments; furthermore, a great deal of useful intelligence – which ideas work and do not work, which input classes are hard and easy, and what to expect from certain algorithms – may be found by consulting the experimental literature.
A guide to experimental algorithmics by Catherine C. McGeoch