English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We introduce a nested optimization procedure using semi-definite relaxationfor the fitting step in Hamiltonian-based cluster dynamical mean-field theory(DMFT) methodologies. We show that the proposed method is more efficient andflexible than state-of-the-art fitting schemes, which allows us to treat aslarge a number of bath sites as the impurity solver at hand allows. Wecharacterize its robustness to initial conditions and symmetry constraints,thus providing conclusive evidence that in the presence of a large bath, oursemi-definite relaxation approach can find the correct set of bath parameterswithout needing to include \emph{a priori} knowledge of the properties that areto be described. We believe this method will be of great use forHamiltonian-based calculations, simplifying and improving one of the key stepsin cluster dynamical mean-field theory calculations.

Efficient hybridization fitting for dynamical mean-field theory via semi-definite relaxation