madeleineudell / madeleineudell/ParallelSparseRegression.jl
Compute regularization paths
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 12
- Forks
- 7
- PR merge metrics
- No merged PRs in 30d
Description
Start with a very high regularizer, so the problem is easy to solve, and decrease it. Each iteration should be very fast. Design `make_prox_*` so that changing lambda incurs little overhead. Allow user to choose lambda_min and lambda_max, but also include good defaults to encourage interesting (eg non-zero) solutions.
Contributor guide
No contributing guide indexed for this repository
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 by locating the `make_prox_*` entry points and reading how regularization parameters are currently passed through the solver. Clarify the intended lambda range and defaults with the maintainers; done means users can choose `lambda_min` and `lambda_max`, receive useful defaults, and run successive regularization iterations with little lambda-change overhead.
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
- Mostly clear
- Newbie friendliness
- 35/100