arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 0902.3124cond-mat.str-el
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数值重整化群方法的自适应对数离散

Adaptive logarithmic discretization for numerical renormalization group methods

Rok Zitko

摘要:

一般地研究了任意正函数(如态密度)的对数离散化问题。对数离散化在某些选定的点(例如费米能级)周围具有任意高的分辨率,并且它可以应用于例如量子杂质问题(近藤模型)的数值重整化群(NRG)方法,其中导带态的连续体需要在费米能级附近具有良好采样的情况下减少到有限数量的能级。所讨论的离散方案要求在不同的交错离散网格上平均后再现原始函数,从而消除了常规对数离散中出现的系统偏差。提出了一种改进的方案,其中离散网格点本身是以自适应的方式确定的,它们在函数具有较高值的区域中更密集。这种方案有助于减少NRG计算中的残余数值伪影,在这种情况下,状态密度在延长的时间间隔内接近零。还描述了用于确定完整离散系数集的微分方程的求解器的参考实现。

英文摘要:

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.

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