SciML / SciML/ComplementaritySolve.jl
Differentiable Trajectory Optimization
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 3
- Forks
- 2
- Avg merge
- 7h 44m
- Merged PRs (30d)
- 7
Description
https://github.com/lassepe/ParametricMCPs.jl and https://github.com/lassepe/DifferentiableTrajectoryOptimization.jl have useful examples for MCP use cases.
They have a very naive sensitivity analysis code (constructing the entire Jacobian). We should try and reproduce their experiments in our framework. Those are quite low-hanging fruits.
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
Review the examples in ParametricMCPs.jl and DifferentiableTrajectoryOptimization.jl, then inspect the ComplementaritySolve.jl framework to identify the relevant MCP and sensitivity-analysis entry points. Reproduce the cited trajectory-optimization experiments and compare them with the existing naive full-Jacobian approach; done means the experiments work in this framework.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- tooling
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100