Physics – Condensed Matter – Strongly Correlated Electrons
Scientific paper
2009-02-18
Comput. Phys. Comm. 180, 1271 (2009)
Physics
Condensed Matter
Strongly Correlated Electrons
11 pages, 2 figures. Related source code available from http://nrgljubljana.ijs.si/adapt
Scientific paper
10.1016/j.cpc.2009.02.007
The problem of the logarithmic discretization of an arbitrary positive function (such as the density of states) is studied in general terms. Logarithmic discretization has arbitrary high resolution around some chosen point (such as Fermi level) and it finds application, for example, in the numerical renormalization group (NRG) approach to quantum impurity problems (Kondo model), where the continuum of the conduction band states needs to be reduced to a finite number of levels with good sampling near the Fermi level. The discretization schemes under discussion are required to reproduce the original function after averaging over different interleaved discretization meshes, thus systematic deviations which appear in the conventional logarithmic discretization are eliminated. An improved scheme is proposed in which the discretization-mesh points themselves are determined in an adaptive way; they are denser in the regions where the function has higher values. Such schemes help in reducing the residual numeric artefacts in NRG calculations in situations where the density of states approaches zero over extended intervals. A reference implementation of the solver for the differential equations which determine the full set of discretization coefficients is also described.
No associations
LandOfFree
Adaptive logarithmic discretization for numerical renormalization group methods does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Adaptive logarithmic discretization for numerical renormalization group methods, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Adaptive logarithmic discretization for numerical renormalization group methods will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-128279