Download e-book for kindle: A guide to experimental algorithmics by Catherine C. McGeoch

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 means of displaying what algorithms, implementations, and speed-up tools paintings most sensible 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 may still I degree? What inputs may still I try? How do I research the information? Answering those questions wishes principles from set of rules design Read more...

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From this graph we can observe the following: • Factor F1 has a positive main effect because every line has positive slope. • Factor F2 has a positive main effect since both solid lines are above both dotted lines. • The main effect of F3 is also positive, since in each pair the line marked (+) is above the line marked (−). • Factor F2 has the greatest main effect, since the distance between solid and dotted lines is greater than the distances that represent the effects of F1 (left vs. right points) and F3 (as labeled).

All three implementations gave different behaviours . .. Naturally our confidence went out the window. ” We now have two modes of running our experiments, one with the paranoid flag on. In this mode, we put efficiency aside and make sure that the algorithms and their heuristics do exactly the same thing, as far as we can tell. • Pseudorandom number generators can produce patterns of nonrandomness that skew results. I once spent a week pursuing the theoretical explanation for an interesting property of the move-to-front algorithm described in Chapter 6: the interesting property disappeared when the random number generator was swapped out in a validation test.

We now have two modes of running our experiments, one with the paranoid flag on. In this mode, we put efficiency aside and make sure that the algorithms and their heuristics do exactly the same thing, as far as we can tell. • Pseudorandom number generators can produce patterns of nonrandomness that skew results. I once spent a week pursuing the theoretical explanation for an interesting property of the move-to-front algorithm described in Chapter 6: the interesting property disappeared when the random number generator was swapped out in a validation test.

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A guide to experimental algorithmics by Catherine C. McGeoch


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