JuliaDiff / JuliaDiff/ForwardDiff.jl
Calculating J_f(x) . y efficiently
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- Dominant language
- Julia
- Stars
- 1k
- Forks
- 160
- PR merge metrics
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Description
As far as I understand, if you have a function f:R^n -> R^n, there should be a way to calculate
J_f(x) . y (Jacobian of f at point x, multiplied by the vector y) more efficiently than calculating the whole matrix and then doing the multiplication.
How can this be done using ForwardDiff?
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 with ForwardDiff's public API and documentation for Jacobians and directional derivatives, then check whether an existing entry point supports multiplying the Jacobian by a vector without materializing it. Done would be a documented or implemented way to perform this calculation efficiently, with its behavior and usage explained.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- tooling
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100