ON THE WAY TOWARDS INCREASING THE PREDICTIVE POWER OF THE NUCLEAR MEAN FIELD THEORIES: EVALUATION OF TWO-BODY MATRIX ELEMENTS

2012 ◽  
Vol 21 (05) ◽  
pp. 1250037
Author(s):  
HERVÉ MOLIQUE ◽  
JERZY DUDEK

In this paper we collect a number of technical issues that arise when constructing the matrix representation of the most general nuclear mean field Hamiltonian within which "all terms allowed by general symmetries are considered not only in principle but also in practice". Such a general posing of the problem is necessary when investigating the predictive power of the mean field theories by means of the well-posed inverse problem. [J. Dudek et al., Int. J. Mod. Phys. E21 (2012) 1250053]. To our knowledge quite often ill-posed mean field inverse problems arise in practical realizations what makes reliable extrapolations into the unknown areas of nuclei impossible. The conceptual and technical issues related to the inverse problem have been discussed in the above-mentioned topic whereas here we focus on "how to calculate the matrix elements, fast and with high numerical precision when solving the inverse problem" [For space-limitation reasons we illustrate the principal techniques on the example of the central interactions].

1994 ◽  
Vol 567 (3) ◽  
pp. 637-654 ◽  
Author(s):  
M. Colonna ◽  
Ph. Chomaz ◽  
J. Randrup

Entropy ◽  
2017 ◽  
Vol 19 (11) ◽  
pp. 589 ◽  
Author(s):  
Peter W. Egolf ◽  
Kolumban Hutter

2017 ◽  
Vol 50 (11) ◽  
pp. 114002 ◽  
Author(s):  
Carsten Henkel ◽  
Tim-O Sauer ◽  
N P Proukakis

Author(s):  
David Barber

Finding clusters of well-connected nodes in a graph is a problem common to many domains, including social networks, the Internet and bioinformatics. From a computational viewpoint, finding these clusters or graph communities is a difficult problem. We use a clique matrix decomposition based on a statistical description that encourages clusters to be well connected and few in number. The formal intractability of inferring the clusters is addressed using a variational approximation inspired by mean-field theories in statistical mechanics. Clique matrices also play a natural role in parametrizing positive definite matrices under zero constraints on elements of the matrix. We show that clique matrices can parametrize all positive definite matrices restricted according to a decomposable graph and form a structured factor analysis approximation in the non-decomposable case. Extensions to conjugate Bayesian covariance priors and more general non-Gaussian independence models are briefly discussed.


2001 ◽  
Vol 13 (36) ◽  
pp. L819-L824 ◽  
Author(s):  
S M M Virtanen ◽  
T P Simula ◽  
M M Salomaa

Sign in / Sign up

Export Citation Format

Share Document