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2010

Maximum likelihood analysis of algorithms and data structures

9 years 6 months ago
Maximum likelihood analysis of algorithms and data structures
We present a new approach for an average-case analysis of algorithms and data structures that supports a non-uniform distribution of the inputs and is based on the maximum likelihood training of stochastic grammars. The approach is exemplified by an analysis of the expected size of binary tries as well as by three sorting algorithms and it is compared to the known results that were obtained by traditional techniques. Investigating traditional settings like the random permutation model we rediscover well-known results formerly derived by pure analytic methods; changing to biased data yields original results. All but one step of our analysis can be automated on top of a computer-algebra system. Thus our new approach can reduce the effort required for an average-case analysis allowing for the consideration of realistic input distributions with unknown distribution functions at the same time. As a by-product our approach yields an easy way to generate random combinatorial objects accord...
Ulrich Laube, Markus E. Nebel
Added 30 Jan 2011
Updated 30 Jan 2011
Type Journal
Year 2010
Where TCS
Authors Ulrich Laube, Markus E. Nebel
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