SciML / SciML/ComplementaritySolve.jl
Total Variational Denoising
Open
Nobody has claimed this yet.
applications
lcp
- Dominant language
- Julia
- Stars
- 3
- Forks
- 2
- Avg merge
- 7h 44m
- Merged PRs (30d)
- 7
Description
- reformulate TDV(as given https://eeweb.engineering.nyu.edu/iselesni/lecture_notes/TVDmm/TVDmm.pdf as Linear Complementarity Problem
- Learn the difference operator
- Train a neural LCP model and compare against https://github.com/locuslab/optnet/tree/master/denoising
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 linked TDV lecture notes and the optnet/denoising comparison referenced in the issue. Work out the difference operator, then train the neural LCP model and compare its results with that denoising example. Done means both unchecked checklist items are addressed; no repository file or test is named.
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
- 25/100