Parallel Algorithms

  • Gerhard Winkler
Part of the Applications of Mathematics book series (SMAP, volume 27)

Abstract

In the previously considered relaxation algorithms, current configurations were updated sequentially: The Gibbs sampler (possibly) changed a given configuration x in a systematically or randomly chosen site s, replacing the old value x s by a sample y s from the local characteristic П(x s |xS\{s}) The next step started from the new configuration y = y s x S \{s}. More generally, on a (random) set AS the subconfiguration x A could be replaced by a sample from П(y A |x S \A) and the next step started from y = y A x S \A. The latter reduces the number of steps needed for a good estimate but in general does not result in a substantial gain of computing time. The computational load in each step increases as the subsets get larger; for large A (A = S) the algorithms even become computationally infeasible.

Keywords

Paral 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Gerhard Winkler
    • 1
  1. 1.Mathematical InstituteLudwig-Maximilians UniversitätMünchenGermany

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