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Simulated annealing

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Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. For large numbers of local optima, SA can find the global optimum. It is often used when the search space is discrete (for example the traveling salesman problem, the boolean satisfiability problem, protein structure prediction, and job-shop scheduling). For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to exact algorithms such as gradient descent or branch and bound.

Simulated annealing - Wikipedia Jump to content From Wikipedia, the free encyclopedia Probabilistic optimization technique and metaheuristic For other uses, see Annealing (disambiguation) . This article needs more citations . Please help improve this article by adding citations to reliable sources . Unsourced material may be challenged and removed . Find sources: "Simulated annealing" – news · newspapers · books · scholar · JSTOR ( December 2009 ) ( Learn how and when to remove this message ) Simulated annealing can be used to solve combinatorial problems. Here it is applied to the travelling

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