COMPLEXITY AND APPROXIMATION AUSIELLO PDF

Autoren: Ausiello , G. The need to solve difficult optimization problems is another reason that forces us to deal with approximation. In particular, when a problem is computationally hard i. In such cases, we may decide to restrict ourselves to compute a solution that, though not being an optimal one, nevertheless is close to the optimum and may be determined in polynomial time. We call this type of solution an approximate solution and the corresponding algorithm a polynomial-time approximation algorithm.

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It seems that you're in Germany. We have a dedicated site for Germany. Authors: Ausiello , G. The need to solve difficult optimization problems is another reason that forces us to deal with approximation. In particular, when a problem is computationally hard i.

In such cases, we may decide to restrict ourselves to compute a solution that, though not being an optimal one, nevertheless is close to the optimum and may be determined in polynomial time. We call this type of solution an approximate solution and the corresponding algorithm a polynomial-time approximation algorithm. Most combinatorial optimization problems of great practical relevance are, indeed, computationally intractable in the above sense.

In formal terms, they are classified as Np-hard optimization problems. Only valid for books with an ebook version. Springer Reference Works are not included. JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. Computer Science Software Engineering. Free Preview.

Buy eBook. Buy Hardcover. Buy Softcover. FAQ Policy. Show all. Approximation Classes Pages Ausiello, Giorgio et al. Approximation through Randomization Pages Ausiello, Giorgio et al. Probabilistic analysis of approximation algorithms Pages Ausiello, Giorgio et al. Heuristic methods Pages Ausiello, Giorgio et al.

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Complexity and Approximation

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Var I ious notions of approximation appear, in fact, in many circumstances. One notable example is the type of approximation that arises in numer ical analysis or in computational geometry from the fact that we cannot perform computations with arbitrary precision and we have to truncate the representation of real numbers. In other cases, we use to approximate com plex mathematical objects by simpler ones: for example, we sometimes represent non-linear functions by means of piecewise linear ones. The need to solve difficult optimization problems is another reason that forces us to deal with approximation. In particular, when a problem is computationally hard i.

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