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

Comparing Algorithms for the Traveling Salesman Problem. Moreover, we prove that better approximation algorithms do not exist unless NP-complete problems admit efficient algorithms. We obtain computationally simple optimal rules for aggregating and thereby minimizing the errors in the decisions of the nodes executing the intrusion detection software (IDS) modules. I was expecting that I'd have to find an approximate solution, as this looked like a classic hairy NP-hard optimization problem. The traveling salesman problem (TSP) is an NP-complete problem. Different approximation algorithms have their advantages and disadvantages. Product Description This is the first book to fully address the study of approximation algorithms as a tool for coping with intractable problems. 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. We then show that the selection of the optimal set of nodes for executing these modules is an NP-hard problem. The field of "Sparse Approximation" deals with ways to perform atom decomposition, namely finding the atoms building the data vector. Combining theories of hypothesis testing, stochastic analysis, and approximation algorithms, we develop a framework to counter different threats while minimizing the resource consumption.