We reconsider the problem of locating the globally optimal solution of a multilayer-optical-coating design problem, within some predetermined space of parameters, with the aim of obtaining a robust technique that requires a minimum of user intervention. The approach we adopt centers on exploring the space of the parameters of interest by using a Markov-chain Monte Carlo sampling algorithm. This technique enables one to locate the global optimum automatically with high confidence and without the need for a good starting design. It also allows the trivial inclusion of prior constraints on the variables and provides a natural means for investigating the robustness of the optimal solution.
Description
Optics InfoBase: Applied Optics - Markov-Chain Monte Carlo Approach to the Design of Multilayer Thin-Film Optical Coatings
%0 Journal Article
%1 Hobson:04
%A Hobson, Michael P.
%A Baldwin, John E.
%D 2004
%I OSA
%J Appl. Opt.
%K coatings
%N 13
%P 2651--2660
%R 10.1364/AO.43.002651
%T Markov-Chain Monte Carlo Approach to the Design of Multilayer Thin-Film Optical Coatings
%U http://ao.osa.org/abstract.cfm?URI=ao-43-13-2651
%V 43
%X We reconsider the problem of locating the globally optimal solution of a multilayer-optical-coating design problem, within some predetermined space of parameters, with the aim of obtaining a robust technique that requires a minimum of user intervention. The approach we adopt centers on exploring the space of the parameters of interest by using a Markov-chain Monte Carlo sampling algorithm. This technique enables one to locate the global optimum automatically with high confidence and without the need for a good starting design. It also allows the trivial inclusion of prior constraints on the variables and provides a natural means for investigating the robustness of the optimal solution.
@article{Hobson:04,
abstract = {We reconsider the problem of locating the globally optimal solution of a multilayer-optical-coating design problem, within some predetermined space of parameters, with the aim of obtaining a robust technique that requires a minimum of user intervention. The approach we adopt centers on exploring the space of the parameters of interest by using a Markov-chain Monte Carlo sampling algorithm. This technique enables one to locate the global optimum automatically with high confidence and without the need for a good starting design. It also allows the trivial inclusion of prior constraints on the variables and provides a natural means for investigating the robustness of the optimal solution.},
added-at = {2013-07-02T13:11:39.000+0200},
author = {Hobson, Michael P. and Baldwin, John E.},
biburl = {https://www.bibsonomy.org/bibtex/26ccad3c7498d03f4ea57ab701f101c2a/dbuscher},
description = {Optics InfoBase: Applied Optics - Markov-Chain Monte Carlo Approach to the Design of Multilayer Thin-Film Optical Coatings},
doi = {10.1364/AO.43.002651},
interhash = {a257c5dd63a6bad595287df6a35815fe},
intrahash = {6ccad3c7498d03f4ea57ab701f101c2a},
journal = {Appl. Opt.},
keywords = {coatings},
month = may,
number = 13,
pages = {2651--2660},
publisher = {OSA},
timestamp = {2013-07-02T13:11:40.000+0200},
title = {Markov-Chain Monte Carlo Approach to the Design of Multilayer Thin-Film Optical Coatings},
url = {http://ao.osa.org/abstract.cfm?URI=ao-43-13-2651},
volume = 43,
year = 2004
}