Metropolis-type annealing algorithms for global optimization in R d
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[1] J. Doob. Stochastic processes , 1953 .
[2] S. Orey. Lecture Notes on Limit Theorems for Markov Chain Transition Probabilities , 1971 .
[3] Harold J. Kushner,et al. wchastic. approximation methods for constrained and unconstrained systems , 1978 .
[4] K. Binder. Monte Carlo methods in statistical physics , 1979 .
[5] V. Fabian. Stochastic Approximation Methods for Constrained and Unconstrained Systems (Harold L. Kushner and Dean S. Clark) , 1980 .
[6] C. Hwang. Laplace's Method Revisited: Weak Convergence of Probability Measures , 1980 .
[7] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[8] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] D. Mitra,et al. Convergence and finite-time behavior of simulated annealing , 1985, 1985 24th IEEE Conference on Decision and Control.
[10] B. Gidas. Nonstationary Markov chains and convergence of the annealing algorithm , 1985 .
[11] V. Cerný. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .
[12] B. Gidas. Global optimization via the Langevin equation , 1985, 1985 24th IEEE Conference on Decision and Control.
[13] S. Geman,et al. Diffusions for global optimizations , 1986 .
[14] C. Hwang,et al. Diffusion for global optimization in R n , 1987 .
[15] T. Chiang,et al. On the convergence rate of annealing processes , 1987 .
[16] Saul B. Gelfand,et al. Analysis of simulated annealing type algorithms , 1987 .
[17] H. Kushner. Asymptotic global behavior for stochastic approximation and diffusions with slowly decreasing noise effects: Global minimization via Monte Carlo , 1987 .
[18] Chiang Tzuu-Shuh,et al. On the convergence rate of annealing processes , 1988 .
[19] P. Kumar,et al. Simulated-annealing type Markov chains and their order balance equations , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.
[20] D. G. Brooks,et al. Computational experience with generalized simulated annealing over continuous variables , 1988 .
[21] Bruce E. Hajek,et al. Cooling Schedules for Optimal Annealing , 1988, Math. Oper. Res..
[22] J. Tsitsiklis. A survey of large time asymptotics of simulated annealing algorithms , 1988 .
[23] John N. Tsitsiklis,et al. Markov Chains with Rare Transitions and Simulated Annealing , 1989, Math. Oper. Res..
[24] John W. Woods,et al. Simulated annealing in compound Gaussian random fields , 1990, IEEE Trans. Inf. Theory.
[25] Rama Chellappa,et al. Relaxation algorithms for MAP estimation of gray-level images with multiplicative noise , 1990, IEEE Trans. Inf. Theory.
[26] S. Mitter,et al. Recursive stochastic algorithms for global optimization in R d , 1991 .
[27] Stuart GEMANf. DIFFUSIONS FOR GLOBAL OPTIMIZATION , 2022 .
[28] S. Mitter,et al. RECURSIVE STOCHASTIC ALGORITHMS FOR GLOBAL OPTIMIZATION IN , 2022 .