| Metropolis-type Annealing Algorithms for Global Optimization in IRd (2007) | |||||||||||
Abstract | |||||||||||
| 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].. Prepared in cooperation with Purdue University, West Lafayette, IN. | |||||||||||
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