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Approximation Algorithms for NP-Hard Problems by Dorit Hochbaum

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

Product Description This is the first book to fully address the study of approximation algorithms as a tool for coping with intractable problems. The computer scientist Richard Karp, of the University of California at Berkeley, showed that the traveling salesman problem is “NP-hard,” which means that it has no efficient algorithm (unless a famous conjecture called P=NP is true — but the majority of computer scientists now suspect that it is false). 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. In spite of their theoretical hardness, heuristic solvers are often able to solve large realistic problem instances surprisingly fast. The theory of NP-completeness suggests that some problems in CS are inherently hard—that is, there is likely no possible algorithm that can efficiently solve them. Algorithms vis-à-vis Everyday Programming; Polynomial-Time Algorithms; NP-Complete Problems. Optimization/approximation algorithms/polynomial time/ NP-HARD. It assumes familiarity with algorithms, mathematical proofs about the correctness of algorithms, probability theory and NP-completeness. Problem classes P, NP, NP-hard and NP-complete, deterministic and non deterministic polynomial time algorithms., Approximation algorithms for some NP-complete problems. An infinitesimal advance in the traveling salesman problem breathes new life into the search for improved approximate solutions. The problem is NP-hard and an approximation algorithm has been described in the reference. He helped create new approximation algorithms for fundamental optimization problems such as the Sparsest Cuts problem and the Euclidean Travelling Salesman problem, and contributed to the development of semi-definite programming as a practical algorithmic tool. The expected value of a discrete random variable). Sanjeev Arora is one of the architects of the Probabilistically Checkable Proofs (PCP) theorem, which revolutionized our understanding of complexity and the approximability of NP-hard problems. For graduate-level courses in approximation algorithms. Have you ever wondered if a specific NP-hard problem has an approximation algorithm or not? If yes, you may like to visit this site: A Compendium of NP optimization problems. Since many interesting optimization problems are computationally intractable (NP-Hard), we resort to designing approximation algorithms which provably output good solutions. The Travelling-Salesman; Subset-Sum; Set-Covering.