tensorflow / tensorflow/probability
Suggestion: do not reoptimize already converged points
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Description
Similar to the idea from https://github.com/tensorflow/probability/pull/928, it seems like internally the (l)bgfs optimisers will go through points that already have reached convergence if any other points in the problem have not. The linked PR allows one to re-enter the optimiser with an initial state, allowing one to remove those already converged points manually.
I was wondering if doing that internally would be an option, that is skipping computations for batches that are already converged, or if that would be too complicated/slow for the general use case to do that internally and automatically.
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 reviewing the linked TensorFlow Probability pull request 928 and the internal L-BFGS optimizers discussed in the issue. Determine whether already-converged batches can be skipped without affecting unfinished points, then document the behavior and add coverage demonstrating that converged points are not reoptimized.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100