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Approximation Algorithms for NP-Hard Problems

Approximation Algorithms for NP-Hard Problems

Approximation Algorithms for NP-Hard Problems. Dorit Hochbaum

Approximation Algorithms for NP-Hard Problems


Approximation.Algorithms.for.NP.Hard.Problems.pdf
ISBN: 0534949681,9780534949686 | 620 pages | 16 Mb


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




Heuristics for NP-hard problems. Problem classes P, NP, NP-hard and NP-complete, deterministic and nondeterministic polynomial time algorithms., Approximation algorithms for some NP complete problems. The expected value of a discrete random variable). The Travelling-Salesman; Subset-Sum; Set-Covering. It assumes familiarity with algorithms, mathematical proofs about the correctness of algorithms, probability theory and NP-completeness. Algorithms vis-à-vis Everyday Programming; Polynomial-Time Algorithms; NP-Complete Problems. For graduate-level courses in approximation algorithms. To minimum spanning trees and Huffman codes; dynamic programming, including applications to sequence alignment and shortest-path problems; and exact and approximate algorithms for NP-complete problems. 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. Approaches include approximation algorithms, heuristics, average-case analysis, and exact exponential-time algorithms: all are essential. It is known that the decisional subset-sum is NP-complete (I believe this result is essentially due to Karp).