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Advances in Randomized Parallel Computing (Paperback, Softcover reprint of the original 1st ed. 1999)
Loot Price: R4,359
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Advances in Randomized Parallel Computing (Paperback, Softcover reprint of the original 1st ed. 1999)
Series: Combinatorial Optimization, 5
Expected to ship within 10 - 15 working days
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The technique of randomization has been employed to solve numerous
prob lems of computing both sequentially and in parallel. Examples
of randomized algorithms that are asymptotically better than their
deterministic counterparts in solving various fundamental problems
abound. Randomized algorithms have the advantages of simplicity and
better performance both in theory and often in practice. This book
is a collection of articles written by renowned experts in the area
of randomized parallel computing. A brief introduction to
randomized algorithms In the aflalysis of algorithms, at least
three different measures of performance can be used: the best case,
the worst case, and the average case. Often, the average case run
time of an algorithm is much smaller than the worst case. 2 For
instance, the worst case run time of Hoare's quicksort is O(n ),
whereas its average case run time is only O( n log n). The average
case analysis is conducted with an assumption on the input space.
The assumption made to arrive at the O( n log n) average run time
for quicksort is that each input permutation is equally likely.
Clearly, any average case analysis is only as good as how valid the
assumption made on the input space is. Randomized algorithms
achieve superior performances without making any assumptions on the
inputs by making coin flips within the algorithm. Any analysis done
of randomized algorithms will be valid for all p0: .sible inputs."
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