C Simulated annealing is a global optimization method that distinguishes C between different local optima. Starting from an initial point, the C algorithm takes a step and the function is evaluated. When minimizing a C function, any downhill step is accepted and the process repeats from this C new point. An uphill step may be accepted. Thus, it can escape from local C optima. This uphill decision is made by the Metropolis criteria. As the C optimization process proceeds, the length of the steps decline and the C algorithm closes in on the global optimum. Since the algorithm makes very C few assumptions regarding the function to be optimized, it is quite C robust with respect to non-quadratic surfaces. The degree of robustness C can be adjusted by the user. In fact, simulated annealing can be used as C a local optimizer for difficult functions.

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