Hi!

I am trying to perform some calculations for Hubbard model (t and +U) on a chain away from half filling. In a previous a question I asked about the sweep parameters for a clean case, your suggestion of starting with very small number of bond dimension (10,20,etc) for a few steps does help in the convergence. However, the same sweep set is not helping the convergence if I introduce bond disorder to the problem. I have tried a couple of sweep parameters where I observed entropy is increasing and then after few sweeps it starts decreasing but never really converges.

My colleague who uses conventional style DMRG performed the same calculation (with disorder) with very high bond dimension like 4000 and 1000 alternatively to achieve convergence in less than 20 sweeps. Is there a fundamental difference between the algorithm for DMRG as implemented in itensor and conventional DMRG?

He also achieved convergence for calculations with lower max_D=min_D(say 400) but with a little less accuracy. One difference is that I am always starting with a product state but he is starting with a random initial state. I am setting the initial state, where holes are equally spread out throughout the chain and the electrons in between are set such that the total spin of the state is always zero. I am doing so, because to my understanding, if QN conservation is set to be true (which is the default configuration), then the system will remain in the sector set by the initial condition. I want to check if my convergence issue can be resolved with random MPS. Can you suggest the possible ways to do so?

I tried to perform a calculation by setting max_D=min_D=4000 and cut-off=0 or 10^-16. In some of the calculations, even though it reads the sweeps input correctly, it is truncating the calculation at 16 for sweep 1 and 256 for sweep 2. Can you guess why that might be happening?

I want to increase the speed of my calculation by using multiple processors. Can you guide me the way it can be done?