Approximation algorithm
Field of study
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In computer science and operations research, approximation algorithms are efficient algorithms that find approximate solutions to optimization problems with provable guarantees on the distance of the returned solution to the optimal one. Wikipedia
May 9, 2022 · An approximation algorithm is a way of dealing with NP-completeness for an optimization problem. This technique does not guarantee the best solution.
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An algorithm is a factor α approximation (α-approximation algorithm) for a problem iff for every instance of the problem it can find a solution within a factor ...
Most of these algorithms are based on the Markov chain Monte Carlo (MCMC) method, a topic that deserves a book by itself and is therefore not treated here.
The quality of an approximation algorithm is the maximum “distance” between its solutions and the optimal solutions, evaluated over all the possible instances ...
Approximation algorithms are algorithms designed to solve problems that are not solvable in polynomial time for approximate solutions. These problems are known ...
Course Summary. Approximation algorithms for NP-hard problems are polynomial time heuristics that have guarantees on the quality of their solutions.
Oct 12, 2005 · The area of approximation algorithms is aimed at giving provable guarantees on the performance of heuristics for hard problems. The course will ...
This course studies approximation algorithms – algorithms that are used for solving hard optimization problems. Such algorithms find approximate (slightly ...
This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions.
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