ITensor / ITensor/ITensorMPS.jl
Sparse diagonalization methods for getting the relevant spectrum of the projected Hamiltonian?
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- Julia
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Description
I’ve understood that a dense/exact diagonalization method (e.g., LinearAlgebra.jl's eigen()) is currently used for diagonalizing projected Hamiltonians during the sweeps (please correct me if I’m wrong). If this is the case, I'm curious if sparse diagonalization methods are already available (or easy to be implemented) as an alternative method for this step. Is this something that has been discussed?
At glance, it seems that DMRG implementation has an argument like solver_krylovdim, suggesting it might work with sparse diagonalization methods. I wonder if the same is true for DMRG-X as well, and if not whether we can implement this as an option.
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Research direction
Start by locating the projected-Hamiltonian sweep diagonalization that currently uses LinearAlgebra.jl's eigen(), then compare how DMRG exposes solver_krylovdim with the corresponding DMRG-X path. Done would require a decided design for an optional sparse method and confirmation that it produces the relevant spectrum correctly in DMRG-X.
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Assessment
- Tech stack
- julia
- Domain
- performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100