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Approximation Algorithms for NP-Hard Problems Dorit Hochbaum
Publisher: Course Technology

Title: Approximation algorithms for Euler genus and related problems. Thus unless P = NP, there are no efficient algorithms to find optimal solutions to such problems. Approximation Algorithm for NP-hard problems by Dorit Hochbaum is a set of chapters by different contributors. Authors: Chandra Computing it has been shown to be NP-hard [Thomassen 1989, 1993], and it is known to be fixed-parameter tractable. It is known that the decisional subset-sum is NP-complete (I believe this result is essentially due to Karp). The reason the Cooper result holds is essentially that Bayes nets can be used to encode boolean satisfiability (SAT) problems, so solving the generic Bayes net inference problem lets you solve any SAT problem. Yet most such problems are NP-hard. Perhaps, the best source on approximation algorithms. The study of approximation algorithms for NP-hard problems has blossomed into a rich field, especially as a result of intense work over the last two decades. In 2003 proved that it is still NP-hard and gave a polynomial-time algorithm with an approximation factor of 1nm.