Variational density matrix optimization using semidefinite programming
Computational Physics
2011-10-27 v2
Abstract
We discuss how semidefinite programming can be used to determine the second-order density matrix directly through a variational optimization. We show how the problem of characterizing a physical or N -representable density matrix leads to matrix-positivity constraints on the density matrix. We then formulate this in a standard semidefinite programming form, after which two interior point methods are discussed to solve the SDP. As an example we show the results of an application of the method on the isoelectronic series of Beryllium.
Cite
@article{arxiv.1006.3721,
title = {Variational density matrix optimization using semidefinite programming},
author = {Brecht Verstichel and Helen van Aggelen and Dimitri Van Neck and Paul W. Ayers and Patrick Bultinck},
journal= {arXiv preprint arXiv:1006.3721},
year = {2011}
}
Comments
corrected typos, added doi