Approximation Algorithms for NP-Hard Problems by Dorit Hochbaum

Approximation Algorithms for NP-Hard Problems



Download Approximation Algorithms for NP-Hard Problems




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


This problem addresses the issue of timing when deploying viral campaigns. These results 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. Here is an example to give a feeling. Both these problems are NP-hard, which motivates our interest in their approximation. Arora's research revolutionized the approach to essentially unsolvable problems that have long bedeviled the computing field, the so-called NP-complete problems. If yes, you may like to visit this site: A Compendium of NP optimization problems. For these problems, approximation algorithms are good choices. Many combinatorial optimization problems can be expressed as the minimization or maximization of a submodular function, including min- and max-cut, coverage problems, and welfare maximization in algorithmic game theory. 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. As we know, NP-hard problems are nightmare for the computers. 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. Unsurprisingly, submodular maximization tends to be NP-hard for most natural choices of constraints, so we look for approximation algorithms. With Christos Papadimitriou in 1988, he framed the systematic study of approximation algorithms for {\mathsf{NP}} -hard optimization problems around the classes {\mathsf{MaxNP}} and {\mathsf{MaxSNP}} . Problem classes P, NP, NP-hard and NP-complete, deterministic and nondeterministic polynomial time algorithms., Approximation algorithms for some NP complete problems. Many of the striking advances in theoretical computer science over the past two decades concern approximation algorithms, which compute provably near-optimal solutions to NP-hard optimization problems. Have you ever wondered if a specific NP-hard problem has an approximation algorithm or not?

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