How to adjust the 'N_sweeps_check' and 'update_env' parameters in Tenpy's idmrg calculation
Posted: 01 Jun 2023, 14:05
When using the idmrg algorithm in Tenpy for calculations, there are two parameters, 'N_sweeps_check' and 'update_env', that have been confusing for me.
Here are my concerns regarding the role of these parameters in the idmrg calculation:
Here are my concerns regarding the role of these parameters in the idmrg calculation:
- By default, 'N_sweeps_check' is set to 10. If I reduce 'N_sweeps_check' to 2, it seems that the calculation information is output every two sweeps. Does this result in longer computation time and more accurate results (such as momentum entanglement spectra)? If my results (such as momentum entanglement spectra) are not satisfactory, should I adjust the 'update_env' parameter?
- Based on my experience, 'update_env' seems to be important during the charge_pump process in the calculation. A larger value for 'update_env' appears to help achieve the desired shape of the charge_pump. My question is, if max_sweep = 100, 'N_sweeps_check' = 2, and 'update_env' = 10, does this mean that the environment is updated 100/2*10 = 500 times or 10 times? If it is 500 times, does it imply that I need to adjust both 'update_env' and 'N_sweeps_check' simultaneously to obtain a reasonable total number of environment updates when computing charge_pump?
- In summary, I am unsure how to properly adjust these two parameters when calculating momentum entanglement spectra and charge_pump. Could you provide specific guidance on how to adjust these parameters to obtain more accurate results when the entanglement spectra and charge_pump results are not satisfactory?