Show simple item record

dc.contributor.authorÅ najdr, Martin.en_US
dc.date.accessioned2009-07-09T18:38:28Z
dc.date.available2009-07-09T18:38:28Z
dc.date.issued1999-07-09T18:38:28Z
dc.identifier.urihttp://hdl.handle.net/10464/1901
dc.description.abstractOptimization 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.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectMonte Carlo method.en_US
dc.subjectMathematical optimization.en_US
dc.subjectFilters (Mathematics)en_US
dc.subjectStatistical physics.en_US
dc.titleHistogram filtering as a tool in variational Monte Carlo optimizationen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Physicsen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Physicsen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record