JuliaDiff / JuliaDiff/ForwardDiff.jl
Take advantage of symmetry in `hessian`
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- Dominant language
- Julia
- Stars
- 1k
- Forks
- 160
- PR merge metrics
- No merged PRs in 30d
Description
As the benchmarks of HyperHessians.jl suggest (@KristofferC), symmetry can probably speed up Hessians quite a bit, even though it is not the only factor. It would be nice to take advantage of it here!
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 hessian entry point and reading how Hessians are currently computed. Compare the approach and benchmarks in HyperHessians.jl, then establish which symmetry optimization is intended and how it should be measured. Done means a decided implementation direction with benchmarks showing the effect on Hessian performance.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100