Asymptotic Global Behavior for Stochastic Approximation and Diffusions with Slowly Decreasing Noise Effects: Global Minimization via Monte Carlo

1987 ◽  
Vol 47 (1) ◽  
pp. 169-185 ◽  
Author(s):  
H. J. Kushner
2014 ◽  
Vol 46 (04) ◽  
pp. 1059-1083 ◽  
Author(s):  
Qifan Song ◽  
Mingqi Wu ◽  
Faming Liang

In this paper we establish the theory of weak convergence (toward a normal distribution) for both single-chain and population stochastic approximation Markov chain Monte Carlo (MCMC) algorithms (SAMCMC algorithms). Based on the theory, we give an explicit ratio of convergence rates for the population SAMCMC algorithm and the single-chain SAMCMC algorithm. Our results provide a theoretic guarantee that the population SAMCMC algorithms are asymptotically more efficient than the single-chain SAMCMC algorithms when the gain factor sequence decreases slower than O(1 / t), where t indexes the number of iterations. This is of interest for practical applications.


2003 ◽  
Vol 36 (2) ◽  
pp. 239-243 ◽  
Author(s):  
V. Brodski ◽  
R. Peschar ◽  
H. Schenk

A new direct-space method forab initiosolution of crystal structures from powder diffraction data is presented. The approach consists of a combined global minimization ofRwpand the potential energy of the system. This method was tested on two organic compounds with known structure and also applied successfully in the structure determination of the previously unknown structure of melamine pyrophosphate.


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