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[[Image:SA_perform.jpg|thumb|Figure 2: An example of convergence for a Simulated Annealing Run]]

[[Image:SA_perform.jpg|thumb|Figure 2: An example of convergence for a Simulated Annealing Run]]

Convergence when spoken in the context of simulated annealing refers to how (if at all) a particular algorithm will approach the correct solution (or for very difficult problems a close to correct solution).  There are many proofs of convergence that are given for certain types of simulated annealing algorithms ([[#References|[3]]] and [[#References|[7]]]), each with there own twist on cooling and other aspects of the algorithm's implementation.  To understand fully (in a mathematical sense) the subject of convergence one must look into the properties of Markov chains and there connections to Monte Carlo-like algorithms.  This topic is reserved for another wiki page.  However, citing the article by [[#References|[2]]], the ParSA library suggests that convergence speed is governed by the following equation:

Convergence when spoken in the context of simulated annealing refers to how (if at all) a particular algorithm will approach the correct solution (or for very difficult problems a close to correct solution).  There are many proofs of convergence that are given for certain types of simulated annealing algorithms ([[#References|[3]]] and [[#References|[7]]]), each with there own twist on cooling and other aspects of the algorithm's implementation.  To understand fully (in a mathematical sense) the subject of convergence one must look into the properties of Markov chains and their connections to Monte Carlo-like algorithms.  This topic is reserved for another wiki page.  However, citing the article by [[#References|[2]]], the ParSA library suggests that convergence speed is governed by the following equation:

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