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Histogram filtering as a tool in variational Monte Carlo optimization

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Show simple item record Å najdr, Martin. en_US 2009-07-09T18:38:28Z 2009-07-09T18:38:28Z 1999-07-09T18:38:28Z
dc.description.abstract Optimization of wave functions in quantum Monte Carlo is a difficult task because the statistical uncertainty inherent to the technique makes the absolute determination of the global minimum difficult. To optimize these wave functions we generate a large number of possible minima using many independently generated Monte Carlo ensembles and perform a conjugate gradient optimization. Then we construct histograms of the resulting nominally optimal parameter sets and "filter" them to identify which parameter sets "go together" to generate a local minimum. We follow with correlated-sampling verification runs to find the global minimum. We illustrate this technique for variance and variational energy optimization for a variety of wave functions for small systellls. For such optimized wave functions we calculate the variational energy and variance as well as various non-differential properties. The optimizations are either on par with or superior to determinations in the literature. Furthermore, we show that this technique is sufficiently robust that for molecules one may determine the optimal geometry at tIle same time as one optimizes the variational energy. en_US
dc.language.iso eng en_US
dc.publisher Brock University en_US
dc.subject Monte Carlo method. en_US
dc.subject Mathematical optimization. en_US
dc.subject Filters (Mathematics) en_US
dc.subject Statistical physics. en_US
dc.title Histogram filtering as a tool in variational Monte Carlo optimization en_US
dc.type Electronic Thesis or Dissertation en_US M.Sc. Physics en_US Masters en_US
dc.contributor.department Department of Physics en_US Faculty of Mathematics and Science en_US

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