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Approximation Algorithms for NP-Hard Problems

Approximation Algorithms for NP-Hard Problems

Approximation Algorithms for NP-Hard Problems by Dorit Hochbaum

Approximation Algorithms for NP-Hard Problems



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Approximation Algorithms for NP-Hard Problems Dorit Hochbaum ebook
ISBN: 0534949681, 9780534949686
Page: 620
Format: djvu
Publisher: Course Technology


This paper deals with firefighter problem on rooted trees. I'd started contemplating local optimizations, simulated annealing, etc. Study of low-distortion embeddings (which can be pursued in a more general setting) has been a highly-active TCS research topic, largely due to its role in designing efficient approximation algorithms for NP-hard problems. For graduate-level courses in approximation algorithms. We propose techniques to improve a given approximation algorithm. Finally, we assume that the reader knows something about NP-completeness, at least enough to know that there might be good reason for wanting fast, approximate solutions to NP-hard discrete optimization problems. Moreover, we prove that better approximation algorithms do not exist unless NP-complete problems admit efficient algorithms. It assumes familiarity with algorithms, mathematical proofs about the correctness of algorithms, probability theory and NP-completeness. I was expecting that I'd have to find an approximate solution, as this looked like a classic hairy NP-hard optimization problem. I normally do machine learning work, and when I'm evaluating an algorithm on a data set, I always use cross-validation to determine how effective the. The expected value of a discrete random variable). It is known that the firefighter problem is NP-hard even for rooted trees of maximum degree 3.

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