JuliaApproximation / JuliaApproximation/ApproxFun.jl
Interface to optimization and AD
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- Dominant language
- Julia
- Stars
- 559
- Forks
- 71
- PR merge metrics
- No merged PRs in 30d
Description
Since differential programming is all the rage in Julia, do you think it will one day be possible to use nonlinear optimization packages where objective functions include ApproxFun Funs? Here is a simple case study where this feature can be useful. This may be more of an issue to raise on, say, Optim and Zygote's pages, but I was wondering if you had thought of the feasibility of it given the infinite-dimensional nature of the operators. Thank you in advance!
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Research direction
Start with the linked parameter_estimation.ipynb case study and review how ApproxFun Funs would interact with the nonlinear optimization packages Optim and Zygote named in the issue. The issue does not identify files, tests, or a concrete acceptance criterion; feasibility and the desired interface would need to be defined before implementation.
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
- 20/100