Cluster solver for dynamical mean-field theory with linear scaling in inverse temperature

Details Cluster solver for dynamical mean-field theory with linear scaling in inverse temperature Cluster solver for dynamical mean-field theory with linear scaling in inverse temperature

2009-04-07

arxiv.org/abs/0904.1239v2

Phys. Rev. E 81, 056703 (2010)

Physics

Condensed Matter

Strongly Correlated Electrons

7 pages, 6 figures

Scientific paper

10.1103/PhysRevE.81.056703

Dynamical mean field theory and its cluster extensions provide a very useful approach for examining phase transitions in model Hamiltonians, and, in combination with electronic structure theory, constitute powerful methods to treat strongly correlated materials. The key advantage to the technique is that, unlike competing real space methods, the sign problem is well controlled in the Hirsch-Fye (HF) quantum Monte Carlo used as an exact cluster solver. However, an important computational bottleneck remains; the HF method scales as the cube of the inverse temperature, $\beta$. This often makes simulations at low temperatures extremely challenging. We present here a new method based on determinant quantum Monte Carlo which scales linearly in $\beta$, and demonstrate that the sign problem is identical to HF.

Affiliated with

Also associated with

No associations

Rating

Cluster solver for dynamical mean-field theory with linear scaling in inverse temperature 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 Cluster solver for dynamical mean-field theory with linear scaling in inverse temperature, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Cluster solver for dynamical mean-field theory with linear scaling in inverse temperature will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-213793