Metropolis-type Annealing Algorithms for Global Optimization in IRd
Saul B. Gelfand, Sanjoy K. Mitter · 1990
We establish the convergence of a class of Metropolis-type Markov chain annealing algorithms for global optimization of a smooth function U(.) on IRd. No prior information is assumed as to what bounded region contains a global minimum. Our analysis is based on writing the Metropolis-type algorithm in the form of a recursive stochastic algorithm, where [some entities] are independent standard Gaussian random variables, [and others] are (unbounded, correlated) random variables, and then applying results about [our findings].