Markov chain Monte Carlo algorithms for optimizing grazing incidence optics for wide-field x-ray survey imaging

Peter W.A. Roming, John C. Liechty, David H. Sohn, Jared R. Shoemaker, David N. Burrows, Gordon P. Garmire

Research output: Contribution to journalConference article

7 Scopus citations

Abstract

The Markov chain Monte Carlo (MCMC) algorithms as a method for optimizing the multi-dimensional coefficient space were investigated. Although MCMC algorithms were traditionally used to explore probability densities that result from a particular model specification, they can be used to create irreducible algorithms for optimizing arbitrary, bounded functions. The irreducible nature of the MCMC algorithm combined with the ability to adapt MCMC algorithms offers a promising framework for optimizing the multi-dimensional complex function.

Original languageEnglish (US)
Pages (from-to)146-153
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4496
DOIs
StatePublished - Jan 1 2002
EventX-Ray Optics for Astronomy: Telescopes, Multilayers, Spectrometers, and Missions - San Diego, CA, United States
Duration: Jul 30 2001Jul 30 2001

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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