Titlebook: Complexity and Approximation; Combinatorial Optimi Giorgio Ausiello,Alberto Marchetti-Spaccamela,Vigg Textbook 1999 Springer-Verlag Berlin

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Heuristic methods,gorithms with a guaranteed behaviour, where such a guarantee refers both to the quality of the returned solution (in terms of either worst case or expected performance ratio) and to the running time (polynomial either in the worst or in the average case).

exacerbate

The Regime for Securities Regulation,ant to solve by computer may have quite varying characteristics. In general, we are able to express our problem in terms of some .⊆ ., where I is the set of . and . is the set of .. As an alternative view, we can also consider a predicate .(x,y) which is true if and only if (x,y) ∈ .. If we want to

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https://doi.org/10.1057/9781403981011unless P = N.. Therefore, if we want to solve an N.-hard optimiza­tion problem by means of an efficient (polynomial-time) algorithm, we have to accept the fact that the algorithm does not always return an optimal solution but rather an approximate one. In Chap. 2, we have seen that, in some cases, s

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Paul Kingston,Marie-Joelle Zahartant factor. We also saw examples of N. problems for which no approximation algorithm exists (unless P=N.) and examples of N. problems for which an approximation algorithm but no approxima­tion scheme exists (unless P=N.). To deal with these two latter kinds of problem, in this chapter we will relax

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J. David Alvis,Jason R. Jividen many problems arising in different areas: taking into account the scope of this book, however, we will limit ourselves to considering randomized approximation algorithms for N.-hard optimization problems.

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