Dear ALPS community.
I am studying Fermionic Hubbard model. And right now I am using QUEST package from quest-qmc.googlecode.com,
Algorithm that is used in it is Determinant Quantum Monte Carlo. I do not like how it scales with lattice size and beta (inverse temperature) and I do not like that you should do extra extrapolation to take of the Trotter errors. I was thinking if some other algorithms do not have these problems.
I am trying to use ALPS to do study the same model.
[1] I've tried and successfully used Full and Sparse diagonalisation routines and they are great( Small wishes: [1][a] It will be nice if diagonalization code could measure 4 operator variables. [1][b] It will be nice if routines were able to use not only translational symmetries, but also point groups. )
[2] I'v tried to use 'dirloop_sse', but I get
"Cannot convert fermionic operator to a bosonic matrix"
[3] All tutorials about Monte Carlo, that I found in the ALPS package work either with spin or boson models.
So, question is: Is there any Monte Carlo routine that works with Fermionic Hubbard model?
Questions not related to Physics: [4] Is there any url, similar to http://docs.python.org, where I can find documentation about pyalps library?
Right now I am using help(function_name), but this looks like a bad way to get the information.
[5] Is there any bug tracker?
Thank you for any help.