JuliaAI / JuliaAI/MLJLinearModels.jl
Improved solvers
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long-term
- Dominant language
- Julia
- Stars
- 86
- Forks
- 15
- PR merge metrics
- No merged PRs in 30d
Description
Quantile, LAD regression
- ADMM should work but if requires adapting rho then needs refactoring of H often which is wasteful, could imagine doing CG for that bit but that would also end up being expensive see also #8
- MM and other algorithms see issue #3 and #4
- IP Method with or without pre-proc ox-code
- Frisch-Newton (for L1 reg may be good) ref
NewtonCG, IWLSCG
- should add a field where the user can specify
:cgor:minresor something else, unlikely it would make a big difference in perf though.
Contributor guide
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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
No files, tests, or entry points are named. Start by breaking the request into the quantile/LAD solver alternatives and the NewtonCG/IWLSCG method-selection change, then determine the relevant implementation locations and tests; done requires an agreed scope and working, benchmarked solver behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 18/100