JuliaDiff / JuliaDiff/Capstan.jl
Complex Differentiation
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- Dominant language
- Julia
- Stars
- 56
- Forks
- 6
- PR merge metrics
- No merged PRs in 30d
Description
Complex differentiation is one of those features that I had always planned on officially supporting in previous packages, but I never got around to grinding out the details/API consequences.
Since I see Capstan as my "fresh start" to AD now that Cassette is on the table, maybe it's time to dive into complex AD for real (pun intended).
Ideally, Capstan could eventually provide an API that supports differentiating:
- non-holomorphic functions
- C^N -> C^M functions
- R^N -> C^M functions
- C^N -> R^M functions
Relevant resources include https://arxiv.org/abs/0906.4835 (thanks for introducing me to this material, @ssfrr).
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
Start by reading the issue's linked paper on complex differentiation and compare its API implications with Capstan's current automatic-differentiation design. Define the supported behavior for non-holomorphic functions and the four stated input/output combinations; done means those cases have an agreed, implemented API.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- backend-api-design
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100