one-particle spectral function;
temperature Green function;
imaginary time Green function;
non-negative least square fit (NNLS);
Tikhonov regularization;
Pade approximant;
global minimization;
inverse problem;
QUANTUM MONTE-CARLO;
D O I:
10.1063/1.4825883
中图分类号:
O29 [应用数学];
学科分类号:
070104 ;
摘要:
In this proceeding, an idea is outlined suggesting a generic treatment on any type of input data for a numerical analytic continuation problem, which is needed when dynamical information is to be extracted from a calculationally convenient one particle imaginary time Green function. The quality of the resulting spectral function will rely only on the data to be treated, viz, data-driven. This is different from the Maximum Entropy or the Stochastic method which relies on an entropy term to guide convergence of the resulting spectral function.