Let OptimizationPolyalgorithms return or save the optimization state
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- Dominant language
- Julia
- Stars
- 839
- Forks
- 100
- Avg merge
- 20h 43m
- Merged PRs (30d)
- 38
Description
Currently, the final state of the Adam and BFGS is dropped when solve stops. However, it can be beneficial to resume training, and then it would help to resume Adam and BFGS from their last settings.
This could be handled by making PolyOpt an object, as when using Adam from Flux.
As an example:
opt = PolyOpt()
// Start training
solve(problem, opt);
// Change something - e.g. adding data points when fitting a neural ode
problem = ...
// Softly restart training
solve(problem, opt);
opt then stores the relevant information.
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
Start by tracing OptimizationPolyalgorithms and the solve entry point, focusing on how Adam and BFGS state is currently created and discarded. Compare the proposed PolyOpt object with Flux's Adam state handling, then define how repeated solve calls preserve state and verify that training can resume after the problem changes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- api, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100