Simulated Annealing and Global Optimization
نویسنده
چکیده
Nelder-Mead (when you don’t know ∇f ) and steepest descent/conjugate gradient (when you do). Both of these methods are based on attempting to generate a sequence of positions xk with monotonically decreasing f(xk) in the hopes that the xk → x∗, the global minimum for f . If f is a convex function (this happens surprisingly often), and has only one local minimum, these methods are exactly the right thing to use: you know in that case that there is only one local min of the function and that it is the global min.
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