SciML / SciML/ModelOrderReduction.jl
Hankel-Norm Approximation
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- Dominant language
- Julia
- Stars
- 43
- Forks
- 8
- Avg merge
- 11h 47m
- Merged PRs (30d)
- 14
Description
From the catch-all tracking issue https://github.com/SciML/ModelOrderReduction.jl/issues/78
Implement optimal Hankel-norm approximation for LTI systems (Adamjan–Arov–Krein / Nehari theory).
https://people.kth.se/~hsan/modred_files/intro_modred.pdf
Reference list (pyMOR LTI MOR checklist): https://github.com/pymor/pymor/issues/388#issuecomment-892486556
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the catch-all tracking issue #78, then read the linked introduction to model reduction and the pyMOR LTI MOR checklist. Identify the package's LTI-system entry points before determining where an Adamjan–Arov–Krein/Nehari implementation belongs. Done means optimal Hankel-norm approximation is implemented for LTI systems.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- hpc
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100