To clarify, here is a text file showing an iteration ('iteration': 0) showing 'observable': 'TruncatedWeight':
from here, it shows 382 values for that observable. I'm wondering why there are 382 truncation error values.
On Sun, Mar 26, 2017 at 12:50 AM, Michele Dolfi dolfim@phys.ethz.ch wrote:
First note that this refers only to convergence of a single simulation. One should then address the convergence in the bond dimension separately.
I would say that there is no exact rule for convergence, but I will give you some example of what we usually check.
- Convergence of energy.
- Truncation error. This is the sum of all discarded eigenvalues in the
reduced density matrix.
- Any other observable of interest, for example the local density is good
indicator, because sometime you fall in a state which is not symmetric and you are pretty sure that something wrong happened. For this values the iteration values we store one number per optimization, i.e. one sweep are 2*N with N the system size.
It is a bit arbitrary how to aggregate the truncation errors. It should just be clear outlined in the publications, so that readers know what you are talking about. Usually you either sum them of take the maximum value.
Regards, Michele
On 24 Mar 2017, at 22:24, Robertson Esperanza <
robbie.esperanza@gmail.com> wrote:
Thank you for all the help so far, now I'm trying to check for
convergence of my results.
First, is it enough to just check how the truncation error changes with
sweeps (at a certain MAXSTATE value) or I need to both check the iteration history of the ground state energy and the truncation error?
Second, checking the supplementary materials provided (Computer Physics
Communications 185 (2014) 3430–3440) for the Hubbard ladder, why are there 381 values of energy and truncation weights for every iteration? Why is the total truncation error for that iteration is the sum of all the 381 truncation weight values (based on iteration.py)? How is truncation error defined (e.g. is it 1 - sum of all reduced density eigenvalues corresponding to all the picked states?) ?
As far as I understand the DMRG algorithm every iteration/sweep has a
corresponding bond dimension in which as you progress through iterations (until you reach the "SWEEPS"th iteration), bond dimension increases until you reach "MAXSTATES" at the "SWEEPS"th iteration.
Regards, Robertson Esperanza
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